One More Time About a Synergy of AI and OR/MS

            I have recently attended a webinar organized by the NY Metro INFORMS Chapter on "Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier." https://us06web.zoom.us/webinar/register/WN_HI7YOf3YSaGPYyuC5w6TzQ#/registration

             The panelists aggressively promoted findings from their recent INFORMS Journal on Data Science paper

Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier

This paper revisits Herbert Simon's 1987 vision of a partnership between artificial intelligence (AI) and operations research/management science (OR/MS) to enhance decision-making. 

Two Heads Are Better than One: The Collaboration between AI and OR

I have mixed feelings about this webinar and its conclusions.

Therefore, I decided to test myself what value could be added by AI to OR/MS.

I used, as a test-bed, my consulting work that I had performed some time ago. This example is instructive because it illustrates a typical request for analysis written in plain language with some unstructured numbers by a healthcare manager who is an analytic layman. This is a rather typical example of a problem in a healthcare setting that requires, first, some interpretation and making some reasonable assumptions to perform the analysis using OR/MS.

I used three popular AI platforms (OpenAI GPT-4o Mini by OpenAI; Grok 3 Mini by xAI; and Free ChatGPT by OpenAI). I used, as input, the same consulting request text provided in the attached document.  The AI outcomes and recommendations are also presented in the attached document.

The bottom line:

·       In my opinion, the presenters of the webinar failed to present a business case or a specific example that would clearly demonstrate the AI value added to OR/MS approach.

·       Simon's vision, " we should aspire to increase the impact of MS/OR by incorporating the AI kit of tools that can be applied to ill-structured non-quantitative decision domains, " was and still remains a big beautiful dream.

·       There were no convincing demonstrations of the value added or additional insights to OR/MS methodology by any of the three tested AI platforms. In fact, the AI recommendations were to use OR/MS methods, such as Excel spreadsheets or simulation modeling. Such recommendations were trivial and generic.

This is the original consulting request:

"Center Manager-Wockhardt Eye Hospital. Location: Aurangabad, Maharashtra, India.

Dear Mr.Kolker, let me discuss my problem in detail.

I work in an ophthalmology hospital. The ideal situation I wish is: we treat 120 patients a day with 5 doctors from 9 am to 1 pm, 2 doctors would see a preliminary examination of the eye and dilate the patient for 30 minutes, whereas 3 doctors would start seeing the final examination after the dilation and will dispose the patient. We promise a delivery of 2 hours to 80% of patients who come for watering & irritation, normal checkups, headaches, glasses, and redness. The rest 20% would have cataract, glaucoma, and other complex disease which is bound to take more than 2 hours of investigation. We can monitor the waiting time of more than 2 hours in each clinic. But this 'ideal' situation hardly happens because one doctor would be in operation theatre, one doctor would be on leave, or in ward rounds or in camp, so I am left with only 3 doctors in the unit from 9 am to 1 pm.

           With 3 doctors, the bulk of patients arrive at 10 am (40 patients/hour) all these patients have to go for vision examination by nurses (5 min each patient) and 3 nurses would do it.

The challenges I have observed are:

* arrival of complicated cases, where a thorough examination of the posterior segment of the eye is needed (could take up to 30 min/ patient)

* patients who find it hard to place their chin in front of the slit lamp or language barrier, more inquisitive patients who keep loading the doctors with questions

* patients who would go for nature's call or coffee or to the lab for blood tests and will arrive to see their successors in the doctor's consultation room before them.

* trauma case that needs complete attention before shifting the patient to OT

* doctors who are purposefully slow to make sure that they see fewer patients than their colleagues

* missing case sheets by the nurse who has to spend all her time searching the case sheets.

Now, how would I monitor the patient waiting time in a clinic of 120 patients with all these parameters, to make sure patients are seen as early as possible rather than shouting, and arguing, thus disturbing the entire atmosphere of a peaceful work environment?

Can I work this situation with all the challenges in an Excel sheet or simulation software? How should I go about it? Kindly assist me."

OR/MS perspective

 (see section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3 )

            This problem description should first be converted into a more specific and formal description that would make it amenable to quantitative OR/MS analysis.

Below is my own problem summary that I made without any AI input:

- clinic is open 4 hours per day, from 9 am to 1 pm. The daily patient volume is 120.

- 80% of patients are regular ones (normal checkups, minor irritation, etc.). They are all first preliminary examined, then dilated, then finally examined and discharged.

- 20% of patients are complicated (cataract, glaucoma, etc.). They are first preliminary examined, and then proceed for final examination by a doctor skipping dilation.

- Preliminary examination takes 5 min, with the range 4-6 min.

- Dilation takes to work about 30 min with a range of 20 - 35 min.

- Dilated patients wait in a separate area until they are seen by the next available doctor for final examination.

-Final examination by the doctor for regular patients takes about 20 min with a range from 15 min to 30 min.

- Final examination by the doctor for complicated patients is in the range from 30 min (posterior segment of eye) to 120 min (2 hrs) (cataract, glaucoma) with the most likely being 1 hour (65 min).

- Staffing for base (ideal case) model: 2 doctors for preliminary examination and dilation, and 3 doctors for final examination. Total doctors' staffing: 5. No nurses.

- Staffing for the actual case is 3 nurses for preliminary examination and dilation and 3 doctors for final examination.

           Operations performance metrics target: 80% or more of regular patients wait less than 2 hours. Because of a lack of more specific information "the bulk of patients arriving at 10 am…" was interpreted as some patients (20) arriving from 9 to 10 am, while the remaining 100 (bulk) arrive at 10 am (actually spread from 10 am to 12:30 pm).  

           The question in the problem description document was "How would I monitor the patient waiting time in a clinic of 120 patients with all these parameters to make sure patients are seen as early as possible?"

            From the OR/MS standpoint, the question should have been interpreted as 'Given patient arrival pattern uncertainty, what staffing would be needed to meet the target operational performance?'

OR/MS Solution

            Obviously, an Excel spreadsheet would not be sufficient for analyzing such a dynamic problem. Discrete event simulation modeling is the methodology that should be used to determine the optimal staffing of doctors that meets the operational performance criteria. The first model would be a baseline one (an ideal case) that includes two preliminary exam doctors and three final exam doctors. Again, I suggested this approach without any AI help. As an OR/MS expert, I did not need any help. Details of a discrete event simulation model and simulation results were presented in section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3

The main take-away was that the number of doctors (not nurses) was the bottleneck. Adding full and part-time doctors will elevate this bottleneck. The model suggested the required number of full- and part-time doctors.

           It was possible to simulate many other scenarios using this type of simulation modeling to find out the most satisfactory staffing level and shifts for meeting the operational performance targets. The following simulation scenarios were suggested: (i) the effect of additional part-time exam doctors scheduled for different shifts with various availability; (ii) restriction of the total daily patient volume; (iii) changing the percentage of regular and complicated patients; (iv) various patient arrival patterns; (v) extension of clinic hours or allowing overtime; (vi) or combinations of the above.

AI perspective.

I used the above plain language description as an input into several popular open AI platforms to test what value AI can add to OR/MS approach or improve or enhance it in any way.

The following AI platforms have been used:

            OpenAI GPT-4o Mini By OpenAI

GPT-4o Mini is OpenAI's most advanced model in the small models category. It is multimodal (accepting text or image inputs and outputting text), and has higher intelligence than gpt-3.5-turbo but is just as fast. It is meant to be used for smaller tasks, including vision tasks.

            The AI output was:

"Optimizing patient flow and monitoring waiting times in a busy ophthalmology clinic, particularly with the challenges you've described, requires a structured approach. Utilizing tools like Excel for tracking and managing data or simulation software for modeling can be effective strategies. Here's a comprehensive outline to help you navigate this issue:

1. Data Collection and Monitoring Setup:

Using Excel:

- Patient Tracking Sheet:

Create a log where you input patient arrival times, initial examination start, dilation times, and final exam times.

- Use columns for patient ID, arrival time, start time for prelim exams, dilation duration, start time for final exams, and total waiting time.

- Categorization:

- Separate patients based on their complexity (routine vs. complicated cases) in different sheets/tabs.

- Assign a complexity code (e.g., 1 for simple and 2 for complex) to each patient for sorting and analysis.

 - Nurse and Doctor Schedules:

create a schedule that indicates the availability of each doctor and nurse. Make a note of any planned leaves or availability that may affect patient processing.

- Incorporating Break Times/Disruptions:

- Maintain a log for each doctor to note any disruptions (emergency cases, breaks, etc.) to identify patterns affecting wait times.

2. Data Analysis:

- Calculate Waiting Times:

- Analyze the sheet daily or weekly to calculate the average waiting time, broken down by patient complexity.

- Identify instances where patients exceed the 2-hour waiting window.

- Bottleneck Analysis:

- Identify common bottlenecks (e.g., wait time at vision examination, delays due to complications) and their causes.

- Performance Tracking:

- Track the performance of each doctor and each shift. Analyze if some doctors are consistently slower than their colleagues and identify reasons.

3. Optimizing Patient Flow:

Suggestion Batching:

- Establish a triage system upon patient arrival:

- *Fast-Track Routine Patients:* Identify lower complexity patients who can quickly proceed with exams.

- *Complex Cases Protocol:* Set aside specific days or sessions for complex cases to reduce their impact during peak hours.

Improvement Measures:

- Patient Education:

- Have educational materials ready for patients (pamphlets, videos) to reduce questions and prepare them for examinations.

- Checking Mechanism:

- Ensure nurses check all case sheets and necessary forms are ready before patients are called to see doctors.

- Queue Management:

- Use a digital queue management system (or even a physical digital board) to manage patient queues.

4. Simulation Software:

Using simulation software can help model various scenarios, predict patient flow, and identify optimal staffing for different times.

1. Creating a Simulation Model:

- Input your clinic's workflow, including patient arrival rates, doctor availability, exam times, etc.

- Simulate different scenarios (varying the number of doctors, adding a patient education session, etc.) to see impacts on waiting times and patient satisfaction.

Grok 3 Mini

The latest fast model from xAI. A lightweight model that thinks before responding. Fast, smart, and great for logic-based tasks that do not require deep domain knowledge.

The output was:

"I understand your situation in the ophthalmology hospital-it sounds frustrating to manage patient flow with so many variables and disruptions, especially when you're short-staffed and dealing with unpredictable challenges like complicated cases, patient behaviors, and administrative issues. The good news is that you can absolutely monitor patient waiting times effectively, even with all these parameters, by using tools like Microsoft Excel for basic tracking and analysis, or simulation software for more advanced modeling. This will help you identify bottlenecks, optimize workflows, and maintain a peaceful environment without resorting to shouting or arguments.

I'll break this down step by step: first, I'll explain how to approach the monitoring process, then discuss using Excel as a starting point, and finally, suggest when and how to use simulation software. My goal is to help you create a system that's practical, data-driven, and scalable.

Step 1: Define Your Monitoring Goals and Process

Before diving into tools, let's outline a clear plan. You want to track waiting times for 120 patients per day (from 9 am to 1 pm) while accounting for the challenges you mentioned. Here's how to structure this:

- Key Metrics to Track:

- Arrival Time: When the patient arrives (e.g., many at 10 am in bulk).

- Preliminary Exam Time: Time for vision exam by nurses (5 minutes each) and dilation (30 minutes total per patient).

- Final Exam Time: Time with doctors for simple cases (aim for under 2 hours total) vs. complex cases (over 2 hours).

- Waiting Time: Total time from arrival to start of preliminary exam, plus time from preliminary to final exam.

- Total Turnaround Time: From arrival to departure, including any delays.

- Delays and Challenges: Note reasons for delays (e.g., complicated cases, patient interruptions, missing case sheets, doctor absences).

- Bottlenecks: Track how factors like doctor availability (e.g., only 3 doctors), nurse workload, or interruptions affect flow.

- Target Outcomes:

- Ensure 80% of simple cases (watering & irritation, checkups, etc.) are seen within 2 hours.

- Monitor the 20% complex cases (e.g., cataract, glaucoma) separately, as they exceed 2 hours.

- Use data to forecast staffing needs, reduce disruptions, and improve patient satisfaction.

- Data Collection Process:

- Who Collects Data? Assign a dedicated staff member (e.g., a nurse or admin) to log data in real-time using a simple form or app. This could be done on paper initially and transferred to Excel, or directly into a digital tool.

- How Often? Track data for every patient daily, then analyze it weekly or monthly to spot patterns.

- Handle Challenges in Tracking:

- For complicated cases (e.g., 30+ min exams), add a field to note the extra time.

- For disruptions (e.g., patient delays for coffee or trauma cases), log them as "interruption events" with timestamps.

- To address doctor variability (e.g., slow doctors), track individual doctor consultation times anonymously to identify trends without blame.

- For missing case sheets, integrate a quick check-in system to ensure files are ready.

By focusing on these, you'll create a "feedback loop": collect data, analyze it, and use insights to adjust schedules (e.g., stagger patient arrivals or allocate more time for complex cases).

Step 2: Using Excel for Monitoring

Excel is an excellent, accessible tool to start with-it's free (if you have Microsoft Office), easy to learn, and powerful enough to handle your scenario without needing advanced software. You can use it to log data, calculate wait times, and visualize trends. Here's how to set it up:

A. Set Up Your Excel Spreadsheet

Create a template like this (I'll describe it; you can replicate it in Excel):

1. Columns for Patient Data:

- Patient ID: A unique number for each patient (e.g., auto-generated).

- Arrival Time: Timestamp (e.g., 10:00 AM).

- Patient Type: Categorize as "Simple (80%)" or "Complex (20%)" based on initial assessment.

- Preliminary Exam Start Time: When the nurse starts (e.g., after vision check and dilation).

- Preliminary Exam End Time: Approximately 30 minutes later.

- Final Exam Start Time: When the doctor begins.

- Final Exam End Time: When the doctor finishes.

- Departure Time: When the patient leaves.

- Delay Reasons: A dropdown or text field for notes (e.g., "Complicated case - posterior exam," "Patient interruption," "Doctor in OT," "Missing case sheet").

- Total Wait Time: Calculated field (e.g., =Final Exam Start Time - Arrival Time).

- Total Turnaround Time: Calculated field (e.g., =Departure Time - Arrival Time).

Example of a basic row:

| Patient ID | Arrival Time | Patient Type | Prelim Start | Prelim End | Final Start | Final End | Departure Time | Delay Reasons | Total Wait Time | Total Turnaround |

|------------|--------------|--------------|--------------|------------|-------------|-----------|----------------|---------------|-----------------|------------------|

| 001 | 10:00 AM | Simple | 10:05 AM | 10:35 AM | 11:00 AM | 11:30 AM | 11:45 AM | None | 1 hour | 1 hour 45 min |

2. Formulas for Calculations:

- Use Excel's time functions to automate calculations:

- Total Wait Time: =TEXT(Final Start - Arrival Time, "h:mm") (subtracts times and formats as hours:minutes).

- Total Turnaround Time: =TEXT(Departure Time - Arrival Time, "h:mm").

- Conditional Formatting: Highlight cells where Total Turnaround Time > 2 hours in red, so you can quickly spot issues.

- Averages: Add a summary section at the bottom with formulas like =AVERAGE(Total Wait Time column) to calculate daily averages.

3. Incorporate Challenges into the Sheet:

- Variable Patient Arrival: Use a separate tab to log arrival patterns (e.g., 40 patients at 10 am). Then, use graphs to show peaks.

- Complicated Cases: Add a column for "Exam Duration Override" where you manually enter extra time (e.g., +30 min for posterior exams).

- Interruptions: Create a dropdown list in the Delay Reasons column (e.g., options: "Patient Delay," "Trauma Case," "Doctor Absence"). This helps quantify how often these occur.

- Doctor and Nurse Workload: Track which doctor/nurse handled each patient and how long it took. Use pivot tables to analyze if one doctor is consistently slower.

- Bulk Data Entry: If you have many patients, use Excel's data validation for quick entry (e.g., dropdowns for Patient Type).

B. Daily Workflow with Excel

- Morning Prep: Open the Excel file at 9 am. Have staff enter data as patients are processed.

- Real-Time Monitoring: Use a shared Excel file (via OneDrive or Google Sheets for collaboration) so you can check wait times on the fly. If a patient is approaching 2 hours, alert the team.

- End-of-Day Analysis: After 1 pm, review the data. For example:

- Pie chart for patient types (80% simple vs. 20% complex).

- Line graph for wait times over the day to see spikes at 10 am.

- This will help you answer questions like: "How many patients waited over 2 hours today, and why?"

C. Limitations and Tips for Excel

- Pros: It's simple, cost-effective, and you can start immediately.

- Cons: It doesn't handle randomness well (e.g., simulating "what if a doctor is absent?"), so it's better for tracking real data than predicting scenarios.

- Tips:

- Train your team on basic Excel use (e.g., 1-hour session).

- Back up data daily to avoid loss.

- If you have 120 patients, use Excel's filters and sorting to manage the volume.

With Excel, you can monitor your current situation effectively and start making improvements, like adjusting nurse assignments based on arrival peaks.

Step 3: When and How to Use Simulation Software

If Excel feels insufficient (e.g., you want to simulate "what if" scenarios with your challenges), move to simulation software. This is ideal for modeling complex systems like yours, where variables (e.g., doctor absences, patient interruptions) create uncertainty.

Why Use Simulation Software?

- It allows you to create a virtual model of your clinic, incorporating all your challenges to predict wait times and test solutions.

- For example, you could simulate: "If we have only 3 doctors and 40 patients arrive at 10 am, how does that affect wait times?" Or, "What if we add a buffer for complicated cases?"

Recommended Software:

- Free/Entry-Level Options:

- Excel's own simulation tools (e.g., using Monte Carlo add-ins) for simple scenarios.

- AnyLogic Personal Learning Edition (free for basic use) or Simul8 Community Edition-these are user-friendly for healthcare simulations.

- Paid Options: Arena or ExtendSim if your hospital can invest (they're more powerful but might require training).

How to Set It Up:

1. Choose Software: Start with AnyLogic-it's intuitive for beginners.

2. Model Your Process:

- Input Parameters: Define variables based on your description:

- Patient arrival: Bulk at 10 am (40/hour), with 80% simple and 20% complex.

- Resources: 3 doctors, 3 nurses; each preliminary exam takes 5-30 minutes; final exam varies (e.g., 30 minutes for simple, up to 60+ for complex).

- Challenges: Add random events, like a 10% chance of a trauma case interrupting, or a 20% chance of patient delays.

- Simulation Logic: Create a flowchart:

- Step 1: Patient arrives → Goes to nurse for preliminary exam.

- Step 2: If dilated, queues for doctor.

- Step 3: Doctor examines; add delays for interruptions.

- Output: Simulated wait times for 120 patients.

3. Run Scenarios:

- Baseline: Simulate your current setup.

- "What If?": Test changes, like hiring a temp doctor or staggering arrivals.

4. Analyze Results: The software will output reports, e.g., average wait times, probability of meeting the 2-hour promise.

5. Integration with Excel: Use Excel to import simulation results for further analysis.

Getting Started with Simulation:

- Learning Curve: If you're new, start with online tutorials (e.g., YouTube for AnyLogic). It might take a few days to build a basic model.

- Cost and Time: If your hospital can't afford this, stick with Excel for now and scale up later.

- When to Switch: If you notice patterns in Excel data that need deeper analysis (e.g., frequent delays from complicated cases), simulation will help.

Final Thoughts

You can definitely manage this with Excel as your primary tool-it's straightforward and will give you immediate insights into waiting times while accounting for your challenges. Start by creating the spreadsheet I outlined, collect data for a week, and review it to see improvements. If things get more complex, transition to simulation software for predictive modeling.

This approach will help you foster a more efficient, less chaotic environment by focusing on data rather than reactions. If you share more details (e.g., your current workflow or specific Excel questions), I can refine this further. Good luck-I'm here to assist!"

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The output was :

"To effectively monitor patient waiting times in your ophthalmology clinic, especially given the complexities and challenges you've outlined, a systematic approach is important. While Excel can provide a relatively simple way to track and analyze data, a simulation software tool might be better suited for modeling the dynamic and complex nature of your clinic. Here's a step-by-step guide for both approaches:

Option 1: Using Excel

1. Data Collection:

- Gather data on patient arrivals, the type of cases, time spent on examinations, and outcomes. You will need to track:

- Arrival times

- Time taken by nurses for preliminary examination

- Time taken by doctors for final examination

- Types of cases (routine or complex)

- Any delays or disruptions (e.g., nature's call, language barriers)

2. Create a Tracking Template:

- Design an Excel sheet with columns such as:

- Patient ID

- Arrival Time

- Preliminary Examination Start Time

- Preliminary Examination End Time

- Doctor Consultation Start Time

- Doctor Consultation End Time

- Type of Case

- Total Waiting Time

- Notes (for delays or other disruptions)

3. Formulas:

- Use formulas to calculate waiting times. For instance:

- Total Waiting Time = (Doctor Consultation End Time - Arrival Time)

4. Data Analysis:

- Use pivot tables or graphs to analyze waiting times based on different parameters (e.g., types of cases, doctor efficiency).

- Highlight any trends, such as peak times leading to delays or specific issues causing longer waiting times.

5. Regular Reviews:

- Set a schedule to review the data regularly, and adjust workflows or resource allocation based on findings.

Option 2: Using Simulation Software

1. Choose a Simulation Tool:

- Select software that specializes in healthcare operations, such as AnyLogic, Simul8, or Arena, which can model patient flow.

2. Model Your Clinic:

- Create a model that includes:

- The flow of patients from arrival to consultation.

- Time distributions for examinations (e.g., 5 minutes for preliminary, 15-30 minutes for final depending on complexity).

- Random arrivals based on historical data.

- Include factors for delays, such as patients taking breaks, language barriers, and others.

3. Input Data:

- Input your collected data (arrival rates, processing times, etc.) into the simulation model.

4. Run Simulations:

- Simulate various scenarios to observe how changes in staffing levels, patient arrival patterns, and case complexities impact waiting times.

- Explore "what-if" scenarios, such as what happens when one doctor is unavailable or if more nurses are added for preliminary exams.

5. Output Analysis:

- Analyze the results from the simulation to identify bottlenecks and areas for improvement.

- Use the insights gained to inform changes in scheduling, staffing, and processes.

General Recommendations

1. Standard Operating Procedures (SOPs):

- Standardize processes for routine examinations and consultations to minimize variations caused by different doctors.

2. Patient Education:

- Provide clear instructions to patients before their appointment regarding what to expect, helping them to be prepared (e.g., time for blood tests or what to do with delays).

3. Communication:

- Implement a system for keeping patients informed about expected waiting times and managing their expectations.

4. Resource Allocation:

- Consider hiring additional staff during peak hours or cross-training existing staff to fill in as needed.

5. Feedback Mechanism:

- Create a system for collecting patient feedback on waiting times and experiences to continuously improve the process.

By utilizing either Excel for basic tracking or a more advanced simulation for in-depth analysis, you can gain valuable insights into your clinic's operations, ultimately leading to improved patient satisfaction and efficient use of resources."

------------------------------
Alexander Kolker
ge healthcare
MILWAUKEE WI
------------------------------

Alexander,

I sincerely appreciate that you tested a few LLMs to see what kind of assistance they would have provided you in your scenario. That's a great way to learn what works well and what doesn't. The results of your three tests didn't satisfy you that it could help. Might I suggest two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built-in. Also, since you're the expert in the particular area that the client is asking about, maybe a better metric of success for the LLM is not to compare it to what you would have done but what someone with 5-10 years experience in OR/MS would have done. If you had passed this request off to a colleague, what would you have expected to get in return? 

For example, if I prepended this to your client's request: "You are an operations research expert with experience in healthcare. You have read the book "Healthcare Management Engineering in Action" from Springer (2024). You know the way to approach a problem with a fresh perspective and be able to use the INFORMS (informs.org) approach to job task analysis (JTA) and end-to-end solution methodology. Here is a request from a potential client. Outline ways of potentially helping this client, identifying algorithms and software needed. For each, give detailed pros and cons of the approach. Then list clarifying questions that we need to ask the client." and then added the request, submitted it to OpenAI's GPT-5 with 'Thinking' turned on, it gives a different and more detailed answer. I'll post it below - you will need to determine whether or not this is better/worse/same since I'm not experienced in this particular area, but I do think this is a great start that took only a few minutes to get some areas to pursue. As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS.

Here is the GPT-5 output (you can also test on the pro models from X, Anthropic, etc.):

Great brief. Below is a pragmatic, OR-driven plan you can take to leadership. I'll frame it using an INFORMS-style JTA and end-to-end methodology, then outline several complementary solution tracks-with algorithms, software, and concrete pros/cons-followed by the clarifying questions we'll need to ask.

1) Problem framing (JTA + end-to-end method)

Job Task Analysis (JTA) view

• • Purpose: Deliver outpatient ophthalmology consults with an 80% ≤2-hour turnaround, despite variable doctor availability and case complexity.

  • Tasks & resources: Registration → vision exam (nurses) → dilation (30 min for most) → doctor exam → diagnostics (subset) → disposition; resources: 3–5 doctors, 3 nurses, rooms/equipment, paper case sheets.

  • Workflows: Two-stage clinical flow with a fixed delay (dilation), plus branching for complex cases and trauma (priority class).

  • Performance targets: 120 pts/day in 4 clinic hours (9:00–13:00); 80% ≤2h TAT, 20% allowed >2h; minimize >2h waits by clinic; maintain smooth, calm environment.

Method overview (INFORMS/DMAIC)

1. 1. Define: Map the current process (SIPOC + value stream map), define KPIs (TAT, wait time per stage, LWBS/balking, %>2h).

   2. Measure: Time-stamp each handoff; capture arrival pattern (esp. 10–11 a.m. surge), service-time distributions by case type/doctor, routing probabilities, no-shows/walk-aways.

   3. Analyze: Queueing diagnostics; bottleneck identification; variability analysis; capacity vs. demand by 15-min interval.

   4. Improve/Design: Redesign appointment/triage, staffing rosters, pooling rules, priority logic, buffer sizes; add visual controls; 5S for case sheets.

   5. Control/Sustain: Real-time dashboard, escalation rules, control charts; policy for trauma surges and low-staff days; continuous simulation A/B tests for schedule tweaks.

2) Solution tracks (algorithms + software, with pros/cons)

A) Queueing & capacity planning (diagnose the bottlenecks)

What: Model the system as tandem queues: Nurse vision exam → fixed 30-min dilation delay → Doctor exam; with priority classes (trauma high, complex medium, simple low), time-varying arrivals, and server calendars (doctors on leave/OT/rounds).

Algorithms

• • Time-varying Poisson (TVP) arrivals; G/G/c approximations (e.g., square-root-staffing, Allen-Cunneen) for waits.

  • Priority queue formulas (non-preemptive for complex; preemptive for trauma if policy allows).

  • Pooling vs. dedicated servers analysis; Little's Law to sanity-check WIP and TAT.

  • Variability impact (SCV) analysis; bottleneck identification (VUT/Kingman).

Software

• • Excel with add-ins or templates for M/M/c / G/G/c approximations.

  • R (queueing, tidyverse) or Python (NumPy/Pandas + custom calcs).

  • Visualization with Power BI / Tableau.

Pros

• • Fast, low cost, immediate insights.

  • Good for "why is 10–11 a.m. painful?" and staffing what-ifs.

  • Validates feasibility of 120 pts/4 hours with 3 vs. 5 doctors.

Cons

• • Approximations; hard to capture rework, wandering patients, room constraints, and case-sheet losses.

  • Doesn't show minute-by-minute congestion as realistically as simulation.

B) Appointment and arrival smoothing (change the input)

What: Redesign appointment templates and walk-in triage to flatten the 10 a.m. surge; pre-dilation for qualifying patients; stagger doctor start times; buffer slots for trauma/complex.

Algorithms

• • Stochastic appointment scheduling with no-shows and dilation lag; objective: minimize mean/95th-percentile TAT.

  • Overbooking optimization by time block (chance-constrained).

  • Slot mix optimization (simple/complex ratios by 15-min block).

Software

• • Python OR-Tools / PuLP (open-source LP/MIP), or Gurobi/CPLEX if available.

  • Export to Excel scheduling templates for daily use.

Pros

• • Biggest lever: fewer giant peaks → shorter queues everywhere.

  • Works even when only 3 doctors are present.

Cons

• • Requires culture change and booking discipline.

  • Needs reliable complexity triage at scheduling/arrival.

C) Real-time flow control & digital queuing

What: Token-based single queues per stage (avoid "choose your doctor" micro-queues), real-time status boards, SMS recalls for patients who leave for tests/coffee, fixed WIP caps at each step.

Algorithms

• • Dispatch rules: Shortest-processing-time-first for simple pool; triage priority for trauma/complex; pull system from doctor stage to start pre-dilation only when downstream WIP is below cap.

  • Workload leveling (Heijunka) logic.

Software

• • Lightweight kiosk/QR time-stamping + Google Apps Script/Power Apps; or off-the-shelf queue apps.

  • Real-time dashboards in Power BI/Tableau.

Pros

• • Immediate wait-time visibility; fewer arguments at the desk.

  • Reduces "overtaking" by wanderers; controlled WIP lowers TAT variability.

Cons

• • Needs basic IT support and staff training.

  • SMS/WhatsApp workflows must match local patient behavior/languages.

D) Discrete-Event Simulation (DES) of the clinic

What: Build a minute-level simulator of your clinic with actual distributions, calendars, and routing (including dilation, complex cases up to 30 min, trauma preemptions, nurse/room constraints, case-sheet mishaps).

Algorithms

• • DES engine with entity routing, resource seize/release, calendars, priorities, balking/reneging.

  • Simulation-optimization (search over appointment templates, staffing, WIP caps).

Software (choose one)

• • Simul8 Healthcare, AnyLogic, Arena (paid; healthcare libraries, GUIs).

  • SimPy (Python) or simmer (R) (open-source; code-based, very flexible).

  • JaamSim (free GUI) if budget-constrained.

Pros

• • Captures all your "messy realities": wanderers, missing sheets, language delays, doctor-specific speed, trauma preemptions.

  • Lets you test policies safely before rollout; produces persuasive visuals.

Cons

• • Needs clean input data and modeler time.

  • Governance required to keep it updated (avoid "one-off model" trap).

E) Staff rostering and day-of reallocation

What: Build schedules that anticipate OT/leave/camps; cross-train/float roles; day-of robust reallocation when doctors drop from 5 → 3.

Algorithms

• • Integer programming for monthly/weekly rosters with coverage constraints and fairness.

  • Robust optimization or stochastic programming to hedge against absences.

  • Real-time rebalancing heuristic using current WIP + forecast arrivals.

Software

• • OR-Tools CP-SAT, Gurobi/CPLEX; front-end in Excel or a small web app.

Pros

• • Converts variability in staffing into planned buffers.

  • Fairness rules reduce "purposefully slow" behavior incentives.

Cons

• • Requires accurate availability inputs and policy alignment.

  • Optimization outputs must be interpretable and editable by managers.

F) Process reliability: case sheets, 5S, and language support

What: Reduce "special-cause" delays (lost sheets, difficult positioning, language).

Algorithms/tools

• • 5S/visual management for physical files; Kanban for sheet movement.

  • Standard work times with control charts (X-MR) on vision and exam durations.

  • Pareto analysis for delay causes; FMEA for failure modes (lost sheets, device downtime).

  • Interpreter routing (rules-based) and pictorial instruction cards for slit-lamp positioning.

Software

• • Excel/Power BI control charts; lightweight barcode or RFID for case-sheet tracking.

  • Process mining (if EHR timestamps exist) to spot rework loops.

Pros

• • Cheap, immediate waste removal; directly tackles stated pain points.

  • Stabilizes inputs for the DES and scheduling models.

Cons

• • Lean changes require daily management; benefits erode without audits.

G) Performance transparency & incentives (behavioral)

What: Case-mix-adjusted productivity and fairness so no one "wins" by going slow.

Algorithms

• • Multilevel regression to adjust throughput for case complexity and patient mix.

  • Risk-adjusted control charts per doctor; CUSUM to detect sustained drift.

  • Equitable load balancing rules in the dispatcher (pooled queue).

Software

• • R/Python analytics; publish in Power BI/Tableau.

Pros

• • Reduces gaming; focuses debate on data, not personalities.

  • Supports coaching and targeted training.

Cons

• • Needs trust and careful rollout; ensure measures don't encourage cutting corners.

H) Monitoring: how to track waiting time for 120 patients

Minimal viable system

• • Time-stamps at: arrival, start/end vision, start dilation, start/end doctor, diagnostics start/end, disposition. Use QR/short codes on the case sheet or patient token; staff scan at each step (Android phones suffice).

  • Live dashboard: queue lengths per stage, % within 2h today, 95th-percentile wait, per-clinic red/yellow/green.

  • Escalation rules: if predicted >2h exceeds 20% for the next hour, trigger actions (pull in backup doctor, increase simple-case ratio, extend clinic by 15 min, etc.).

Algorithms

• • Short-horizon forecasting of arrivals using moving averages; Erlang-C/G/G/c to predict downstream waits; alerting thresholds.

Software

• • Google Sheets + Apps Script or Power Apps for capture; Power BI for live visuals.

  • Scales later to an EHR-integrated workflow.

Pros

• • Fast to deploy; creates the data spine needed for all other tracks.

  • De-escalates arguments-staff can point to the board, not each other.

Cons

• • Requires consistent scanning; start simple (arrival + doctor start) and add steps.

3) Excel vs. simulation: when to use which

Excel first (weeks 1–2): Queue approximations, simple staffing calculators, appointment templates, daily dashboards.
Simulation next (weeks 3–8): Validate policies with all complexities; run "5→3 doctors" stress tests; design robust playbooks for trauma surges and staff shortages.
Optimization overlays (weeks 4–8): Slot mix, overbooking, and roster optimization informed by the simulation.

4) Quick-win interventions to pilot (low cost)

1. 1. Single pooled queue per stage with token display; ditch room-level micro-queues.

   2. Pre-dilation protocol for clearly simple follow-ups (per clinical policy) to decouple from doctor availability.

   3. Stagger starts (e.g., one doctor at 8:45 for early trickle/trauma buffer; one at 9:15) + buffer slots at 10:00 to absorb the surge.

   4. WIP caps: limit number in dilation buffer and waiting-for-doctor; start pulls only when below cap.

   5. 5S for case sheets + barcode stickers; daily audit.

   6. Interpreter cards and slit-lamp coaching visuals to reduce handling delays.

   7. Daily readiness huddle at 8:50 with forecast and contingency (who is on call if we drop to 2 doctors).

5) Data we'll ask for (minimal to start)

• • Arrivals by 15-min over 4–6 weeks; share of walk-ins vs. booked; no-show rate.

  • Service times (min, mean, 75th/90th/95th percentiles) for: vision exam, doctor exam (simple vs. complex), diagnostics; by doctor if possible.

  • Routing fractions: % needing dilation, diagnostics, procedures; % trauma.

  • Resource calendars: doctor/nurse availability, rooms/equipment counts.

  • Current policies: triage criteria, trauma handling, "overtaking" rules.

  • Outcomes: current %≤2h, LWBS/balking, patient complaints.

6) Clarifying questions for the client

Demand & mix

1. 1. What % of patients truly require dilation, and can any be pre-dilated safely by protocol?

   2. Distribution of simple vs. complex vs. trauma across the day? Are cataract/glaucoma largely pre-booked or walk-ins?

Times & resources
3) Observed time distributions (not just averages) for nurse vision exam, dilation-to-doctor lag beyond 30 min, doctor exam by case type, diagnostics.
4) Number of rooms/slit lamps and any shared equipment constraints.
5) Typical doctor availability patterns (OT days, rounds, camps) and same-day variance.

Policies & priorities
6) Current rules for trauma preemption-can trauma interrupt ongoing consults or is it handled by a designated doctor?
7) Are patients allowed to "hold" their place when they leave for labs/coffee? What is the formal policy vs. actual practice?

Scheduling
8) Share of booked vs. walk-in; appointment template granularity; overbooking currently used?
9) Show-up reliability by time of day/day of week; any patterned late arrivals (e.g., 10–11 a.m. peak due to transport)?

Information flow
10) Do you have EHR time-stamps today? If not, is scanning/QR feasible at each station?
11) Frequency and causes of missing case sheets; can we attach barcodes and define handoff checkpoints?

People & incentives
12) How do you measure doctor productivity now? Are you open to case-mix-adjusted dashboards and pooled queuing to reduce gaming?
13) Language mix of patients; access to interpreters or bilingual staff?

Constraints & goals
14) Acceptable overtime? Willingness to shift some volume to 8:45–9:00 pre-clinic or 13:00–13:15 spillover?
15) Primary success metric: 80th percentile ≤2h, 95th percentile ≤? Specific targets per clinic?

7) Recommended stack (practical)

• • Data capture & dashboards: Google Forms/QR → Google Sheets → Power BI/Tableau.

  • Queue analysis: Excel + Python/R notebooks for G/G/c and priority models.

  • Simulation: Start with SimPy (Python) for flexibility; if budget allows, Simul8 Healthcare for faster stakeholder buy-in.

  • Optimization: OR-Tools (open source) or Gurobi if available; deployment back to Excel templates for scheduling/rosters.

8) What success looks like (within one quarter)

• • Live wait-time board and daily huddles; staff using WIP caps and pooled queues.

  • Appointment template that flattens the 10–11 a.m. spike; buffer for trauma.

  • Case-mix-adjusted productivity view; less variability between doctors.

  • ≥80% ≤2 hours on >80% of clinic days, even when staffing drops to 3 doctors.

If you'd like, I can draft the initial Excel capacity calculator (arrival by 15-min × G/G/c wait estimator) and a SimPy skeleton that mirrors your dilation flow and priority classes, using placeholder distributions until we get your real data.

One More Time About a Synergy of AI and OR/MS

            I have recently attended a webinar organized by the NY Metro INFORMS Chapter on "Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier." https://us06web.zoom.us/webinar/register/WN_HI7YOf3YSaGPYyuC5w6TzQ#/registration

             The panelists aggressively promoted findings from their recent INFORMS Journal on Data Science paper

Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier

This paper revisits Herbert Simon's 1987 vision of a partnership between artificial intelligence (AI) and operations research/management science (OR/MS) to enhance decision-making. 

Two Heads Are Better than One: The Collaboration between AI and OR

I have mixed feelings about this webinar and its conclusions.

Therefore, I decided to test myself what value could be added by AI to OR/MS.

I used, as a test-bed, my consulting work that I had performed some time ago. This example is instructive because it illustrates a typical request for analysis written in plain language with some unstructured numbers by a healthcare manager who is an analytic layman. This is a rather typical example of a problem in a healthcare setting that requires, first, some interpretation and making some reasonable assumptions to perform the analysis using OR/MS.

I used three popular AI platforms (OpenAI GPT-4o Mini by OpenAI; Grok 3 Mini by xAI; and Free ChatGPT by OpenAI). I used, as input, the same consulting request text provided in the attached document.  The AI outcomes and recommendations are also presented in the attached document.

The bottom line:

·       In my opinion, the presenters of the webinar failed to present a business case or a specific example that would clearly demonstrate the AI value added to OR/MS approach.

·       Simon's vision, " we should aspire to increase the impact of MS/OR by incorporating the AI kit of tools that can be applied to ill-structured non-quantitative decision domains, " was and still remains a big beautiful dream.

·       There were no convincing demonstrations of the value added or additional insights to OR/MS methodology by any of the three tested AI platforms. In fact, the AI recommendations were to use OR/MS methods, such as Excel spreadsheets or simulation modeling. Such recommendations were trivial and generic.

This is the original consulting request:

"Center Manager-Wockhardt Eye Hospital. Location: Aurangabad, Maharashtra, India.

Dear Mr.Kolker, let me discuss my problem in detail.

I work in an ophthalmology hospital. The ideal situation I wish is: we treat 120 patients a day with 5 doctors from 9 am to 1 pm, 2 doctors would see a preliminary examination of the eye and dilate the patient for 30 minutes, whereas 3 doctors would start seeing the final examination after the dilation and will dispose the patient. We promise a delivery of 2 hours to 80% of patients who come for watering & irritation, normal checkups, headaches, glasses, and redness. The rest 20% would have cataract, glaucoma, and other complex disease which is bound to take more than 2 hours of investigation. We can monitor the waiting time of more than 2 hours in each clinic. But this 'ideal' situation hardly happens because one doctor would be in operation theatre, one doctor would be on leave, or in ward rounds or in camp, so I am left with only 3 doctors in the unit from 9 am to 1 pm.

           With 3 doctors, the bulk of patients arrive at 10 am (40 patients/hour) all these patients have to go for vision examination by nurses (5 min each patient) and 3 nurses would do it.

The challenges I have observed are:

* arrival of complicated cases, where a thorough examination of the posterior segment of the eye is needed (could take up to 30 min/ patient)

* patients who find it hard to place their chin in front of the slit lamp or language barrier, more inquisitive patients who keep loading the doctors with questions

* patients who would go for nature's call or coffee or to the lab for blood tests and will arrive to see their successors in the doctor's consultation room before them.

* trauma case that needs complete attention before shifting the patient to OT

* doctors who are purposefully slow to make sure that they see fewer patients than their colleagues

* missing case sheets by the nurse who has to spend all her time searching the case sheets.

Now, how would I monitor the patient waiting time in a clinic of 120 patients with all these parameters, to make sure patients are seen as early as possible rather than shouting, and arguing, thus disturbing the entire atmosphere of a peaceful work environment?

Can I work this situation with all the challenges in an Excel sheet or simulation software? How should I go about it? Kindly assist me."

OR/MS perspective

 (see section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3 )

            This problem description should first be converted into a more specific and formal description that would make it amenable to quantitative OR/MS analysis.

Below is my own problem summary that I made without any AI input:

- clinic is open 4 hours per day, from 9 am to 1 pm. The daily patient volume is 120.

- 80% of patients are regular ones (normal checkups, minor irritation, etc.). They are all first preliminary examined, then dilated, then finally examined and discharged.

- 20% of patients are complicated (cataract, glaucoma, etc.). They are first preliminary examined, and then proceed for final examination by a doctor skipping dilation.

- Preliminary examination takes 5 min, with the range 4-6 min.

- Dilation takes to work about 30 min with a range of 20 - 35 min.

- Dilated patients wait in a separate area until they are seen by the next available doctor for final examination.

-Final examination by the doctor for regular patients takes about 20 min with a range from 15 min to 30 min.

- Final examination by the doctor for complicated patients is in the range from 30 min (posterior segment of eye) to 120 min (2 hrs) (cataract, glaucoma) with the most likely being 1 hour (65 min).

- Staffing for base (ideal case) model: 2 doctors for preliminary examination and dilation, and 3 doctors for final examination. Total doctors' staffing: 5. No nurses.

- Staffing for the actual case is 3 nurses for preliminary examination and dilation and 3 doctors for final examination.

           Operations performance metrics target: 80% or more of regular patients wait less than 2 hours. Because of a lack of more specific information "the bulk of patients arriving at 10 am…" was interpreted as some patients (20) arriving from 9 to 10 am, while the remaining 100 (bulk) arrive at 10 am (actually spread from 10 am to 12:30 pm).  

           The question in the problem description document was "How would I monitor the patient waiting time in a clinic of 120 patients with all these parameters to make sure patients are seen as early as possible?"

            From the OR/MS standpoint, the question should have been interpreted as 'Given patient arrival pattern uncertainty, what staffing would be needed to meet the target operational performance?'

OR/MS Solution

            Obviously, an Excel spreadsheet would not be sufficient for analyzing such a dynamic problem. Discrete event simulation modeling is the methodology that should be used to determine the optimal staffing of doctors that meets the operational performance criteria. The first model would be a baseline one (an ideal case) that includes two preliminary exam doctors and three final exam doctors. Again, I suggested this approach without any AI help. As an OR/MS expert, I did not need any help. Details of a discrete event simulation model and simulation results were presented in section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3

The main take-away was that the number of doctors (not nurses) was the bottleneck. Adding full and part-time doctors will elevate this bottleneck. The model suggested the required number of full- and part-time doctors.

           It was possible to simulate many other scenarios using this type of simulation modeling to find out the most satisfactory staffing level and shifts for meeting the operational performance targets. The following simulation scenarios were suggested: (i) the effect of additional part-time exam doctors scheduled for different shifts with various availability; (ii) restriction of the total daily patient volume; (iii) changing the percentage of regular and complicated patients; (iv) various patient arrival patterns; (v) extension of clinic hours or allowing overtime; (vi) or combinations of the above.

AI perspective.

I used the above plain language description as an input into several popular open AI platforms to test what value AI can add to OR/MS approach or improve or enhance it in any way.

The following AI platforms have been used:

            OpenAI GPT-4o Mini By OpenAI

GPT-4o Mini is OpenAI's most advanced model in the small models category. It is multimodal (accepting text or image inputs and outputting text), and has higher intelligence than gpt-3.5-turbo but is just as fast. It is meant to be used for smaller tasks, including vision tasks.

            The AI output was:

"Optimizing patient flow and monitoring waiting times in a busy ophthalmology clinic, particularly with the challenges you've described, requires a structured approach. Utilizing tools like Excel for tracking and managing data or simulation software for modeling can be effective strategies. Here's a comprehensive outline to help you navigate this issue:

1. Data Collection and Monitoring Setup:

Using Excel:

- Patient Tracking Sheet:

Create a log where you input patient arrival times, initial examination start, dilation times, and final exam times.

- Use columns for patient ID, arrival time, start time for prelim exams, dilation duration, start time for final exams, and total waiting time.

- Categorization:

- Separate patients based on their complexity (routine vs. complicated cases) in different sheets/tabs.

- Assign a complexity code (e.g., 1 for simple and 2 for complex) to each patient for sorting and analysis.

 - Nurse and Doctor Schedules:

create a schedule that indicates the availability of each doctor and nurse. Make a note of any planned leaves or availability that may affect patient processing.

- Incorporating Break Times/Disruptions:

- Maintain a log for each doctor to note any disruptions (emergency cases, breaks, etc.) to identify patterns affecting wait times.

2. Data Analysis:

- Calculate Waiting Times:

- Analyze the sheet daily or weekly to calculate the average waiting time, broken down by patient complexity.

- Identify instances where patients exceed the 2-hour waiting window.

- Bottleneck Analysis:

- Identify common bottlenecks (e.g., wait time at vision examination, delays due to complications) and their causes.

- Performance Tracking:

- Track the performance of each doctor and each shift. Analyze if some doctors are consistently slower than their colleagues and identify reasons.

3. Optimizing Patient Flow:

Suggestion Batching:

- Establish a triage system upon patient arrival:

- *Fast-Track Routine Patients:* Identify lower complexity patients who can quickly proceed with exams.

- *Complex Cases Protocol:* Set aside specific days or sessions for complex cases to reduce their impact during peak hours.

Improvement Measures:

- Patient Education:

- Have educational materials ready for patients (pamphlets, videos) to reduce questions and prepare them for examinations.

- Checking Mechanism:

- Ensure nurses check all case sheets and necessary forms are ready before patients are called to see doctors.

- Queue Management:

- Use a digital queue management system (or even a physical digital board) to manage patient queues.

4. Simulation Software:

Using simulation software can help model various scenarios, predict patient flow, and identify optimal staffing for different times.

1. Creating a Simulation Model:

- Input your clinic's workflow, including patient arrival rates, doctor availability, exam times, etc.

- Simulate different scenarios (varying the number of doctors, adding a patient education session, etc.) to see impacts on waiting times and patient satisfaction.

Grok 3 Mini

The latest fast model from xAI. A lightweight model that thinks before responding. Fast, smart, and great for logic-based tasks that do not require deep domain knowledge.

The output was:

"I understand your situation in the ophthalmology hospital-it sounds frustrating to manage patient flow with so many variables and disruptions, especially when you're short-staffed and dealing with unpredictable challenges like complicated cases, patient behaviors, and administrative issues. The good news is that you can absolutely monitor patient waiting times effectively, even with all these parameters, by using tools like Microsoft Excel for basic tracking and analysis, or simulation software for more advanced modeling. This will help you identify bottlenecks, optimize workflows, and maintain a peaceful environment without resorting to shouting or arguments.

I'll break this down step by step: first, I'll explain how to approach the monitoring process, then discuss using Excel as a starting point, and finally, suggest when and how to use simulation software. My goal is to help you create a system that's practical, data-driven, and scalable.

Step 1: Define Your Monitoring Goals and Process

Before diving into tools, let's outline a clear plan. You want to track waiting times for 120 patients per day (from 9 am to 1 pm) while accounting for the challenges you mentioned. Here's how to structure this:

- Key Metrics to Track:

- Arrival Time: When the patient arrives (e.g., many at 10 am in bulk).

- Preliminary Exam Time: Time for vision exam by nurses (5 minutes each) and dilation (30 minutes total per patient).

- Final Exam Time: Time with doctors for simple cases (aim for under 2 hours total) vs. complex cases (over 2 hours).

- Waiting Time: Total time from arrival to start of preliminary exam, plus time from preliminary to final exam.

- Total Turnaround Time: From arrival to departure, including any delays.

- Delays and Challenges: Note reasons for delays (e.g., complicated cases, patient interruptions, missing case sheets, doctor absences).

- Bottlenecks: Track how factors like doctor availability (e.g., only 3 doctors), nurse workload, or interruptions affect flow.

- Target Outcomes:

- Ensure 80% of simple cases (watering & irritation, checkups, etc.) are seen within 2 hours.

- Monitor the 20% complex cases (e.g., cataract, glaucoma) separately, as they exceed 2 hours.

- Use data to forecast staffing needs, reduce disruptions, and improve patient satisfaction.

- Data Collection Process:

- Who Collects Data? Assign a dedicated staff member (e.g., a nurse or admin) to log data in real-time using a simple form or app. This could be done on paper initially and transferred to Excel, or directly into a digital tool.

- How Often? Track data for every patient daily, then analyze it weekly or monthly to spot patterns.

- Handle Challenges in Tracking:

- For complicated cases (e.g., 30+ min exams), add a field to note the extra time.

- For disruptions (e.g., patient delays for coffee or trauma cases), log them as "interruption events" with timestamps.

- To address doctor variability (e.g., slow doctors), track individual doctor consultation times anonymously to identify trends without blame.

- For missing case sheets, integrate a quick check-in system to ensure files are ready.

By focusing on these, you'll create a "feedback loop": collect data, analyze it, and use insights to adjust schedules (e.g., stagger patient arrivals or allocate more time for complex cases).

Step 2: Using Excel for Monitoring

Excel is an excellent, accessible tool to start with-it's free (if you have Microsoft Office), easy to learn, and powerful enough to handle your scenario without needing advanced software. You can use it to log data, calculate wait times, and visualize trends. Here's how to set it up:

A. Set Up Your Excel Spreadsheet

Create a template like this (I'll describe it; you can replicate it in Excel):

1. Columns for Patient Data:

- Patient ID: A unique number for each patient (e.g., auto-generated).

- Arrival Time: Timestamp (e.g., 10:00 AM).

- Patient Type: Categorize as "Simple (80%)" or "Complex (20%)" based on initial assessment.

- Preliminary Exam Start Time: When the nurse starts (e.g., after vision check and dilation).

- Preliminary Exam End Time: Approximately 30 minutes later.

- Final Exam Start Time: When the doctor begins.

- Final Exam End Time: When the doctor finishes.

- Departure Time: When the patient leaves.

- Delay Reasons: A dropdown or text field for notes (e.g., "Complicated case - posterior exam," "Patient interruption," "Doctor in OT," "Missing case sheet").

- Total Wait Time: Calculated field (e.g., =Final Exam Start Time - Arrival Time).

- Total Turnaround Time: Calculated field (e.g., =Departure Time - Arrival Time).

Example of a basic row:

| Patient ID | Arrival Time | Patient Type | Prelim Start | Prelim End | Final Start | Final End | Departure Time | Delay Reasons | Total Wait Time | Total Turnaround |

|------------|--------------|--------------|--------------|------------|-------------|-----------|----------------|---------------|-----------------|------------------|

| 001 | 10:00 AM | Simple | 10:05 AM | 10:35 AM | 11:00 AM | 11:30 AM | 11:45 AM | None | 1 hour | 1 hour 45 min |

2. Formulas for Calculations:

- Use Excel's time functions to automate calculations:

- Total Wait Time: =TEXT(Final Start - Arrival Time, "h:mm") (subtracts times and formats as hours:minutes).

- Total Turnaround Time: =TEXT(Departure Time - Arrival Time, "h:mm").

- Conditional Formatting: Highlight cells where Total Turnaround Time > 2 hours in red, so you can quickly spot issues.

- Averages: Add a summary section at the bottom with formulas like =AVERAGE(Total Wait Time column) to calculate daily averages.

3. Incorporate Challenges into the Sheet:

- Variable Patient Arrival: Use a separate tab to log arrival patterns (e.g., 40 patients at 10 am). Then, use graphs to show peaks.

- Complicated Cases: Add a column for "Exam Duration Override" where you manually enter extra time (e.g., +30 min for posterior exams).

- Interruptions: Create a dropdown list in the Delay Reasons column (e.g., options: "Patient Delay," "Trauma Case," "Doctor Absence"). This helps quantify how often these occur.

- Doctor and Nurse Workload: Track which doctor/nurse handled each patient and how long it took. Use pivot tables to analyze if one doctor is consistently slower.

- Bulk Data Entry: If you have many patients, use Excel's data validation for quick entry (e.g., dropdowns for Patient Type).

B. Daily Workflow with Excel

- Morning Prep: Open the Excel file at 9 am. Have staff enter data as patients are processed.

- Real-Time Monitoring: Use a shared Excel file (via OneDrive or Google Sheets for collaboration) so you can check wait times on the fly. If a patient is approaching 2 hours, alert the team.

- End-of-Day Analysis: After 1 pm, review the data. For example:

- Pie chart for patient types (80% simple vs. 20% complex).

- Line graph for wait times over the day to see spikes at 10 am.

- This will help you answer questions like: "How many patients waited over 2 hours today, and why?"

C. Limitations and Tips for Excel

- Pros: It's simple, cost-effective, and you can start immediately.

- Cons: It doesn't handle randomness well (e.g., simulating "what if a doctor is absent?"), so it's better for tracking real data than predicting scenarios.

- Tips:

- Train your team on basic Excel use (e.g., 1-hour session).

- Back up data daily to avoid loss.

- If you have 120 patients, use Excel's filters and sorting to manage the volume.

With Excel, you can monitor your current situation effectively and start making improvements, like adjusting nurse assignments based on arrival peaks.

Step 3: When and How to Use Simulation Software

If Excel feels insufficient (e.g., you want to simulate "what if" scenarios with your challenges), move to simulation software. This is ideal for modeling complex systems like yours, where variables (e.g., doctor absences, patient interruptions) create uncertainty.

Why Use Simulation Software?

- It allows you to create a virtual model of your clinic, incorporating all your challenges to predict wait times and test solutions.

- For example, you could simulate: "If we have only 3 doctors and 40 patients arrive at 10 am, how does that affect wait times?" Or, "What if we add a buffer for complicated cases?"

Recommended Software:

- Free/Entry-Level Options:

- Excel's own simulation tools (e.g., using Monte Carlo add-ins) for simple scenarios.

- AnyLogic Personal Learning Edition (free for basic use) or Simul8 Community Edition-these are user-friendly for healthcare simulations.

- Paid Options: Arena or ExtendSim if your hospital can invest (they're more powerful but might require training).

How to Set It Up:

1. Choose Software: Start with AnyLogic-it's intuitive for beginners.

2. Model Your Process:

- Input Parameters: Define variables based on your description:

- Patient arrival: Bulk at 10 am (40/hour), with 80% simple and 20% complex.

- Resources: 3 doctors, 3 nurses; each preliminary exam takes 5-30 minutes; final exam varies (e.g., 30 minutes for simple, up to 60+ for complex).

- Challenges: Add random events, like a 10% chance of a trauma case interrupting, or a 20% chance of patient delays.

- Simulation Logic: Create a flowchart:

- Step 1: Patient arrives → Goes to nurse for preliminary exam.

- Step 2: If dilated, queues for doctor.

- Step 3: Doctor examines; add delays for interruptions.

- Output: Simulated wait times for 120 patients.

3. Run Scenarios:

- Baseline: Simulate your current setup.

- "What If?": Test changes, like hiring a temp doctor or staggering arrivals.

4. Analyze Results: The software will output reports, e.g., average wait times, probability of meeting the 2-hour promise.

5. Integration with Excel: Use Excel to import simulation results for further analysis.

Getting Started with Simulation:

- Learning Curve: If you're new, start with online tutorials (e.g., YouTube for AnyLogic). It might take a few days to build a basic model.

- Cost and Time: If your hospital can't afford this, stick with Excel for now and scale up later.

- When to Switch: If you notice patterns in Excel data that need deeper analysis (e.g., frequent delays from complicated cases), simulation will help.

Final Thoughts

You can definitely manage this with Excel as your primary tool-it's straightforward and will give you immediate insights into waiting times while accounting for your challenges. Start by creating the spreadsheet I outlined, collect data for a week, and review it to see improvements. If things get more complex, transition to simulation software for predictive modeling.

This approach will help you foster a more efficient, less chaotic environment by focusing on data rather than reactions. If you share more details (e.g., your current workflow or specific Excel questions), I can refine this further. Good luck-I'm here to assist!"

Free ChatGPT by OpenAI

Access Free ChatGPT instantly through our OpenAI API-powered interface. We've made ChatGPT's powerful AI capabilities freely available to everyone, with no registration required and no usage limits.

The output was :

"To effectively monitor patient waiting times in your ophthalmology clinic, especially given the complexities and challenges you've outlined, a systematic approach is important. While Excel can provide a relatively simple way to track and analyze data, a simulation software tool might be better suited for modeling the dynamic and complex nature of your clinic. Here's a step-by-step guide for both approaches:

Option 1: Using Excel

1. Data Collection:

- Gather data on patient arrivals, the type of cases, time spent on examinations, and outcomes. You will need to track:

- Arrival times

- Time taken by nurses for preliminary examination

- Time taken by doctors for final examination

- Types of cases (routine or complex)

- Any delays or disruptions (e.g., nature's call, language barriers)

2. Create a Tracking Template:

- Design an Excel sheet with columns such as:

- Patient ID

- Arrival Time

- Preliminary Examination Start Time

- Preliminary Examination End Time

- Doctor Consultation Start Time

- Doctor Consultation End Time

- Type of Case

- Total Waiting Time

- Notes (for delays or other disruptions)

3. Formulas:

- Use formulas to calculate waiting times. For instance:

- Total Waiting Time = (Doctor Consultation End Time - Arrival Time)

4. Data Analysis:

- Use pivot tables or graphs to analyze waiting times based on different parameters (e.g., types of cases, doctor efficiency).

- Highlight any trends, such as peak times leading to delays or specific issues causing longer waiting times.

5. Regular Reviews:

- Set a schedule to review the data regularly, and adjust workflows or resource allocation based on findings.

Option 2: Using Simulation Software

1. Choose a Simulation Tool:

- Select software that specializes in healthcare operations, such as AnyLogic, Simul8, or Arena, which can model patient flow.

2. Model Your Clinic:

- Create a model that includes:

- The flow of patients from arrival to consultation.

- Time distributions for examinations (e.g., 5 minutes for preliminary, 15-30 minutes for final depending on complexity).

- Random arrivals based on historical data.

- Include factors for delays, such as patients taking breaks, language barriers, and others.

3. Input Data:

- Input your collected data (arrival rates, processing times, etc.) into the simulation model.

4. Run Simulations:

- Simulate various scenarios to observe how changes in staffing levels, patient arrival patterns, and case complexities impact waiting times.

- Explore "what-if" scenarios, such as what happens when one doctor is unavailable or if more nurses are added for preliminary exams.

5. Output Analysis:

- Analyze the results from the simulation to identify bottlenecks and areas for improvement.

- Use the insights gained to inform changes in scheduling, staffing, and processes.

General Recommendations

1. Standard Operating Procedures (SOPs):

- Standardize processes for routine examinations and consultations to minimize variations caused by different doctors.

2. Patient Education:

- Provide clear instructions to patients before their appointment regarding what to expect, helping them to be prepared (e.g., time for blood tests or what to do with delays).

3. Communication:

- Implement a system for keeping patients informed about expected waiting times and managing their expectations.

4. Resource Allocation:

- Consider hiring additional staff during peak hours or cross-training existing staff to fill in as needed.

5. Feedback Mechanism:

- Create a system for collecting patient feedback on waiting times and experiences to continuously improve the process.

By utilizing either Excel for basic tracking or a more advanced simulation for in-depth analysis, you can gain valuable insights into your clinic's operations, ultimately leading to improved patient satisfaction and efficient use of resources."

------------------------------
Alexander Kolker
ge healthcare
MILWAUKEE WI
------------------------------

Warren, I appreciate your taking the time to write your detailed response and suggestions to my post regarding the role of AI for possible synergy with OR/MS.

I think the key to our different assessment of the AI role as an enabler and value-added component to OR/MS is in your phrase: "As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS." Of course, after all, you are the Founder, OptiML AI.

Now, as you can guess, I have an open mind about the value AI, but at this time I am highly skeptical of your belief. But the issue is not someone's personal 'belief' or skepticism. In my consulting capacity, my goal is to deliver to the customer the specific solution for the posed problem and to convince the customer that I chose the most efficient and direct methodology for doing so. With regard to AI, I asked myself: "Do I really need an assistant for solving the specific problem posed to me, be that assistant AI or a human?"  And my answer is resounding no.

      You suggested to me "two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built in." Your suggestions completely changed my and the customer's goals and the subject: instead of focusing on the solution of the customer's business problem, you suggested testing some internal AI issues, such as different prompts, the role that AI should assume, and testing better LLMs. However, I am not interested in spending more of my time exploring the role and the potential of AI with little or no chance of getting additional value. I am interested in solving the customer's business problem using the most appropriate methodology, as I judge it. This difference in focus illustrates the core of the "AI rage" issue: a lot of resources are already spent on AI (LLMs) development. AI promoters, marketers, and investors are desperate to demonstrate 'amazing' applications and the usefulness of this "big beautiful thing". This is the reason why AI is claimed to be an almost universal silver bullet that can assist and significantly enhance human ability for solving difficult problems. AI could potentially be useful in solving ill-defined problems with contradictory multiple goals, non-quantifiable or difficult to formalize factors expressed in natural language, generating new alternative hypotheses, etc. But I am not familiar with examples of such AI applications.  

               Your GPT-5 output included in your reply is a useless word salad that includes everything, from generic JTA to Six Sigma DMAC, from queuing & capacity planning to the list of software with pros & cons (and who is supposed to choose pros & cons-another AI?). From DES to Process Reliability with Lean tools (5S, Kanban, Control charts, FMEA, etc) that are totally irrelevant in this context. And so on and on, page after page that makes an illusion of the deep expertise and value, but actually is a hollow babbling close to hallucination.

Anyway, thank you for the opportunity for discussion.

Alexander,

I sincerely appreciate that you tested a few LLMs to see what kind of assistance they would have provided you in your scenario. That's a great way to learn what works well and what doesn't. The results of your three tests didn't satisfy you that it could help. Might I suggest two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built-in. Also, since you're the expert in the particular area that the client is asking about, maybe a better metric of success for the LLM is not to compare it to what you would have done but what someone with 5-10 years experience in OR/MS would have done. If you had passed this request off to a colleague, what would you have expected to get in return? 

For example, if I prepended this to your client's request: "You are an operations research expert with experience in healthcare. You have read the book "Healthcare Management Engineering in Action" from Springer (2024). You know the way to approach a problem with a fresh perspective and be able to use the INFORMS (informs.org) approach to job task analysis (JTA) and end-to-end solution methodology. Here is a request from a potential client. Outline ways of potentially helping this client, identifying algorithms and software needed. For each, give detailed pros and cons of the approach. Then list clarifying questions that we need to ask the client." and then added the request, submitted it to OpenAI's GPT-5 with 'Thinking' turned on, it gives a different and more detailed answer. I'll post it below - you will need to determine whether or not this is better/worse/same since I'm not experienced in this particular area, but I do think this is a great start that took only a few minutes to get some areas to pursue. As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS.

Here is the GPT-5 output (you can also test on the pro models from X, Anthropic, etc.):

Great brief. Below is a pragmatic, OR-driven plan you can take to leadership. I'll frame it using an INFORMS-style JTA and end-to-end methodology, then outline several complementary solution tracks-with algorithms, software, and concrete pros/cons-followed by the clarifying questions we'll need to ask.

1) Problem framing (JTA + end-to-end method)

Job Task Analysis (JTA) view

• • Purpose: Deliver outpatient ophthalmology consults with an 80% ≤2-hour turnaround, despite variable doctor availability and case complexity.

  • Tasks & resources: Registration → vision exam (nurses) → dilation (30 min for most) → doctor exam → diagnostics (subset) → disposition; resources: 3–5 doctors, 3 nurses, rooms/equipment, paper case sheets.

  • Workflows: Two-stage clinical flow with a fixed delay (dilation), plus branching for complex cases and trauma (priority class).

  • Performance targets: 120 pts/day in 4 clinic hours (9:00–13:00); 80% ≤2h TAT, 20% allowed >2h; minimize >2h waits by clinic; maintain smooth, calm environment.

Method overview (INFORMS/DMAIC)

1. 1. Define: Map the current process (SIPOC + value stream map), define KPIs (TAT, wait time per stage, LWBS/balking, %>2h).

   2. Measure: Time-stamp each handoff; capture arrival pattern (esp. 10–11 a.m. surge), service-time distributions by case type/doctor, routing probabilities, no-shows/walk-aways.

   3. Analyze: Queueing diagnostics; bottleneck identification; variability analysis; capacity vs. demand by 15-min interval.

   4. Improve/Design: Redesign appointment/triage, staffing rosters, pooling rules, priority logic, buffer sizes; add visual controls; 5S for case sheets.

   5. Control/Sustain: Real-time dashboard, escalation rules, control charts; policy for trauma surges and low-staff days; continuous simulation A/B tests for schedule tweaks.

2) Solution tracks (algorithms + software, with pros/cons)

A) Queueing & capacity planning (diagnose the bottlenecks)

What: Model the system as tandem queues: Nurse vision exam → fixed 30-min dilation delay → Doctor exam; with priority classes (trauma high, complex medium, simple low), time-varying arrivals, and server calendars (doctors on leave/OT/rounds).

Algorithms

• • Time-varying Poisson (TVP) arrivals; G/G/c approximations (e.g., square-root-staffing, Allen-Cunneen) for waits.

  • Priority queue formulas (non-preemptive for complex; preemptive for trauma if policy allows).

  • Pooling vs. dedicated servers analysis; Little's Law to sanity-check WIP and TAT.

  • Variability impact (SCV) analysis; bottleneck identification (VUT/Kingman).

Software

• • Excel with add-ins or templates for M/M/c / G/G/c approximations.

  • R (queueing, tidyverse) or Python (NumPy/Pandas + custom calcs).

  • Visualization with Power BI / Tableau.

Pros

• • Fast, low cost, immediate insights.

  • Good for "why is 10–11 a.m. painful?" and staffing what-ifs.

  • Validates feasibility of 120 pts/4 hours with 3 vs. 5 doctors.

Cons

• • Approximations; hard to capture rework, wandering patients, room constraints, and case-sheet losses.

  • Doesn't show minute-by-minute congestion as realistically as simulation.

B) Appointment and arrival smoothing (change the input)

What: Redesign appointment templates and walk-in triage to flatten the 10 a.m. surge; pre-dilation for qualifying patients; stagger doctor start times; buffer slots for trauma/complex.

Algorithms

• • Stochastic appointment scheduling with no-shows and dilation lag; objective: minimize mean/95th-percentile TAT.

  • Overbooking optimization by time block (chance-constrained).

  • Slot mix optimization (simple/complex ratios by 15-min block).

Software

• • Python OR-Tools / PuLP (open-source LP/MIP), or Gurobi/CPLEX if available.

  • Export to Excel scheduling templates for daily use.

Pros

• • Biggest lever: fewer giant peaks → shorter queues everywhere.

  • Works even when only 3 doctors are present.

Cons

• • Requires culture change and booking discipline.

  • Needs reliable complexity triage at scheduling/arrival.

C) Real-time flow control & digital queuing

What: Token-based single queues per stage (avoid "choose your doctor" micro-queues), real-time status boards, SMS recalls for patients who leave for tests/coffee, fixed WIP caps at each step.

Algorithms

• • Dispatch rules: Shortest-processing-time-first for simple pool; triage priority for trauma/complex; pull system from doctor stage to start pre-dilation only when downstream WIP is below cap.

  • Workload leveling (Heijunka) logic.

Software

• • Lightweight kiosk/QR time-stamping + Google Apps Script/Power Apps; or off-the-shelf queue apps.

  • Real-time dashboards in Power BI/Tableau.

Pros

• • Immediate wait-time visibility; fewer arguments at the desk.

  • Reduces "overtaking" by wanderers; controlled WIP lowers TAT variability.

Cons

• • Needs basic IT support and staff training.

  • SMS/WhatsApp workflows must match local patient behavior/languages.

D) Discrete-Event Simulation (DES) of the clinic

What: Build a minute-level simulator of your clinic with actual distributions, calendars, and routing (including dilation, complex cases up to 30 min, trauma preemptions, nurse/room constraints, case-sheet mishaps).

Algorithms

• • DES engine with entity routing, resource seize/release, calendars, priorities, balking/reneging.

  • Simulation-optimization (search over appointment templates, staffing, WIP caps).

Software (choose one)

• • Simul8 Healthcare, AnyLogic, Arena (paid; healthcare libraries, GUIs).

  • SimPy (Python) or simmer (R) (open-source; code-based, very flexible).

  • JaamSim (free GUI) if budget-constrained.

Pros

• • Captures all your "messy realities": wanderers, missing sheets, language delays, doctor-specific speed, trauma preemptions.

  • Lets you test policies safely before rollout; produces persuasive visuals.

Cons

• • Needs clean input data and modeler time.

  • Governance required to keep it updated (avoid "one-off model" trap).

E) Staff rostering and day-of reallocation

What: Build schedules that anticipate OT/leave/camps; cross-train/float roles; day-of robust reallocation when doctors drop from 5 → 3.

Algorithms

• • Integer programming for monthly/weekly rosters with coverage constraints and fairness.

  • Robust optimization or stochastic programming to hedge against absences.

  • Real-time rebalancing heuristic using current WIP + forecast arrivals.

Software

• • OR-Tools CP-SAT, Gurobi/CPLEX; front-end in Excel or a small web app.

Pros

• • Converts variability in staffing into planned buffers.

  • Fairness rules reduce "purposefully slow" behavior incentives.

Cons

• • Requires accurate availability inputs and policy alignment.

  • Optimization outputs must be interpretable and editable by managers.

F) Process reliability: case sheets, 5S, and language support

What: Reduce "special-cause" delays (lost sheets, difficult positioning, language).

Algorithms/tools

• • 5S/visual management for physical files; Kanban for sheet movement.

  • Standard work times with control charts (X-MR) on vision and exam durations.

  • Pareto analysis for delay causes; FMEA for failure modes (lost sheets, device downtime).

  • Interpreter routing (rules-based) and pictorial instruction cards for slit-lamp positioning.

Software

• • Excel/Power BI control charts; lightweight barcode or RFID for case-sheet tracking.

  • Process mining (if EHR timestamps exist) to spot rework loops.

Pros

• • Cheap, immediate waste removal; directly tackles stated pain points.

  • Stabilizes inputs for the DES and scheduling models.

Cons

• • Lean changes require daily management; benefits erode without audits.

G) Performance transparency & incentives (behavioral)

What: Case-mix-adjusted productivity and fairness so no one "wins" by going slow.

Algorithms

• • Multilevel regression to adjust throughput for case complexity and patient mix.

  • Risk-adjusted control charts per doctor; CUSUM to detect sustained drift.

  • Equitable load balancing rules in the dispatcher (pooled queue).

Software

• • R/Python analytics; publish in Power BI/Tableau.

Pros

• • Reduces gaming; focuses debate on data, not personalities.

  • Supports coaching and targeted training.

Cons

• • Needs trust and careful rollout; ensure measures don't encourage cutting corners.

H) Monitoring: how to track waiting time for 120 patients

Minimal viable system

• • Time-stamps at: arrival, start/end vision, start dilation, start/end doctor, diagnostics start/end, disposition. Use QR/short codes on the case sheet or patient token; staff scan at each step (Android phones suffice).

  • Live dashboard: queue lengths per stage, % within 2h today, 95th-percentile wait, per-clinic red/yellow/green.

  • Escalation rules: if predicted >2h exceeds 20% for the next hour, trigger actions (pull in backup doctor, increase simple-case ratio, extend clinic by 15 min, etc.).

Algorithms

• • Short-horizon forecasting of arrivals using moving averages; Erlang-C/G/G/c to predict downstream waits; alerting thresholds.

Software

• • Google Sheets + Apps Script or Power Apps for capture; Power BI for live visuals.

  • Scales later to an EHR-integrated workflow.

Pros

• • Fast to deploy; creates the data spine needed for all other tracks.

  • De-escalates arguments-staff can point to the board, not each other.

Cons

• • Requires consistent scanning; start simple (arrival + doctor start) and add steps.

3) Excel vs. simulation: when to use which

Excel first (weeks 1–2): Queue approximations, simple staffing calculators, appointment templates, daily dashboards.
Simulation next (weeks 3–8): Validate policies with all complexities; run "5→3 doctors" stress tests; design robust playbooks for trauma surges and staff shortages.
Optimization overlays (weeks 4–8): Slot mix, overbooking, and roster optimization informed by the simulation.

4) Quick-win interventions to pilot (low cost)

1. 1. Single pooled queue per stage with token display; ditch room-level micro-queues.

   2. Pre-dilation protocol for clearly simple follow-ups (per clinical policy) to decouple from doctor availability.

   3. Stagger starts (e.g., one doctor at 8:45 for early trickle/trauma buffer; one at 9:15) + buffer slots at 10:00 to absorb the surge.

   4. WIP caps: limit number in dilation buffer and waiting-for-doctor; start pulls only when below cap.

   5. 5S for case sheets + barcode stickers; daily audit.

   6. Interpreter cards and slit-lamp coaching visuals to reduce handling delays.

   7. Daily readiness huddle at 8:50 with forecast and contingency (who is on call if we drop to 2 doctors).

5) Data we'll ask for (minimal to start)

• • Arrivals by 15-min over 4–6 weeks; share of walk-ins vs. booked; no-show rate.

  • Service times (min, mean, 75th/90th/95th percentiles) for: vision exam, doctor exam (simple vs. complex), diagnostics; by doctor if possible.

  • Routing fractions: % needing dilation, diagnostics, procedures; % trauma.

  • Resource calendars: doctor/nurse availability, rooms/equipment counts.

  • Current policies: triage criteria, trauma handling, "overtaking" rules.

  • Outcomes: current %≤2h, LWBS/balking, patient complaints.

6) Clarifying questions for the client

Demand & mix

1. 1. What % of patients truly require dilation, and can any be pre-dilated safely by protocol?

   2. Distribution of simple vs. complex vs. trauma across the day? Are cataract/glaucoma largely pre-booked or walk-ins?

Times & resources
3) Observed time distributions (not just averages) for nurse vision exam, dilation-to-doctor lag beyond 30 min, doctor exam by case type, diagnostics.
4) Number of rooms/slit lamps and any shared equipment constraints.
5) Typical doctor availability patterns (OT days, rounds, camps) and same-day variance.

Policies & priorities
6) Current rules for trauma preemption-can trauma interrupt ongoing consults or is it handled by a designated doctor?
7) Are patients allowed to "hold" their place when they leave for labs/coffee? What is the formal policy vs. actual practice?

Scheduling
8) Share of booked vs. walk-in; appointment template granularity; overbooking currently used?
9) Show-up reliability by time of day/day of week; any patterned late arrivals (e.g., 10–11 a.m. peak due to transport)?

Information flow
10) Do you have EHR time-stamps today? If not, is scanning/QR feasible at each station?
11) Frequency and causes of missing case sheets; can we attach barcodes and define handoff checkpoints?

People & incentives
12) How do you measure doctor productivity now? Are you open to case-mix-adjusted dashboards and pooled queuing to reduce gaming?
13) Language mix of patients; access to interpreters or bilingual staff?

Constraints & goals
14) Acceptable overtime? Willingness to shift some volume to 8:45–9:00 pre-clinic or 13:00–13:15 spillover?
15) Primary success metric: 80th percentile ≤2h, 95th percentile ≤? Specific targets per clinic?

7) Recommended stack (practical)

• • Data capture & dashboards: Google Forms/QR → Google Sheets → Power BI/Tableau.

  • Queue analysis: Excel + Python/R notebooks for G/G/c and priority models.

  • Simulation: Start with SimPy (Python) for flexibility; if budget allows, Simul8 Healthcare for faster stakeholder buy-in.

  • Optimization: OR-Tools (open source) or Gurobi if available; deployment back to Excel templates for scheduling/rosters.

8) What success looks like (within one quarter)

• • Live wait-time board and daily huddles; staff using WIP caps and pooled queues.

  • Appointment template that flattens the 10–11 a.m. spike; buffer for trauma.

  • Case-mix-adjusted productivity view; less variability between doctors.

  • ≥80% ≤2 hours on >80% of clinic days, even when staffing drops to 3 doctors.

If you'd like, I can draft the initial Excel capacity calculator (arrival by 15-min × G/G/c wait estimator) and a SimPy skeleton that mirrors your dilation flow and priority classes, using placeholder distributions until we get your real data.

One More Time About a Synergy of AI and OR/MS

            I have recently attended a webinar organized by the NY Metro INFORMS Chapter on "Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier." https://us06web.zoom.us/webinar/register/WN_HI7YOf3YSaGPYyuC5w6TzQ#/registration

             The panelists aggressively promoted findings from their recent INFORMS Journal on Data Science paper

Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier

This paper revisits Herbert Simon's 1987 vision of a partnership between artificial intelligence (AI) and operations research/management science (OR/MS) to enhance decision-making. 

Two Heads Are Better than One: The Collaboration between AI and OR

I have mixed feelings about this webinar and its conclusions.

Therefore, I decided to test myself what value could be added by AI to OR/MS.

I used, as a test-bed, my consulting work that I had performed some time ago. This example is instructive because it illustrates a typical request for analysis written in plain language with some unstructured numbers by a healthcare manager who is an analytic layman. This is a rather typical example of a problem in a healthcare setting that requires, first, some interpretation and making some reasonable assumptions to perform the analysis using OR/MS.

I used three popular AI platforms (OpenAI GPT-4o Mini by OpenAI; Grok 3 Mini by xAI; and Free ChatGPT by OpenAI). I used, as input, the same consulting request text provided in the attached document.  The AI outcomes and recommendations are also presented in the attached document.

The bottom line:

·       In my opinion, the presenters of the webinar failed to present a business case or a specific example that would clearly demonstrate the AI value added to OR/MS approach.

·       Simon's vision, " we should aspire to increase the impact of MS/OR by incorporating the AI kit of tools that can be applied to ill-structured non-quantitative decision domains, " was and still remains a big beautiful dream.

·       There were no convincing demonstrations of the value added or additional insights to OR/MS methodology by any of the three tested AI platforms. In fact, the AI recommendations were to use OR/MS methods, such as Excel spreadsheets or simulation modeling. Such recommendations were trivial and generic.

This is the original consulting request:

"Center Manager-Wockhardt Eye Hospital. Location: Aurangabad, Maharashtra, India.

Dear Mr.Kolker, let me discuss my problem in detail.

I work in an ophthalmology hospital. The ideal situation I wish is: we treat 120 patients a day with 5 doctors from 9 am to 1 pm, 2 doctors would see a preliminary examination of the eye and dilate the patient for 30 minutes, whereas 3 doctors would start seeing the final examination after the dilation and will dispose the patient. We promise a delivery of 2 hours to 80% of patients who come for watering & irritation, normal checkups, headaches, glasses, and redness. The rest 20% would have cataract, glaucoma, and other complex disease which is bound to take more than 2 hours of investigation. We can monitor the waiting time of more than 2 hours in each clinic. But this 'ideal' situation hardly happens because one doctor would be in operation theatre, one doctor would be on leave, or in ward rounds or in camp, so I am left with only 3 doctors in the unit from 9 am to 1 pm.

           With 3 doctors, the bulk of patients arrive at 10 am (40 patients/hour) all these patients have to go for vision examination by nurses (5 min each patient) and 3 nurses would do it.

The challenges I have observed are:

* arrival of complicated cases, where a thorough examination of the posterior segment of the eye is needed (could take up to 30 min/ patient)

* patients who find it hard to place their chin in front of the slit lamp or language barrier, more inquisitive patients who keep loading the doctors with questions

* patients who would go for nature's call or coffee or to the lab for blood tests and will arrive to see their successors in the doctor's consultation room before them.

* trauma case that needs complete attention before shifting the patient to OT

* doctors who are purposefully slow to make sure that they see fewer patients than their colleagues

* missing case sheets by the nurse who has to spend all her time searching the case sheets.

Now, how would I monitor the patient waiting time in a clinic of 120 patients with all these parameters, to make sure patients are seen as early as possible rather than shouting, and arguing, thus disturbing the entire atmosphere of a peaceful work environment?

Can I work this situation with all the challenges in an Excel sheet or simulation software? How should I go about it? Kindly assist me."

OR/MS perspective

 (see section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3 )

            This problem description should first be converted into a more specific and formal description that would make it amenable to quantitative OR/MS analysis.

Below is my own problem summary that I made without any AI input:

- clinic is open 4 hours per day, from 9 am to 1 pm. The daily patient volume is 120.

- 80% of patients are regular ones (normal checkups, minor irritation, etc.). They are all first preliminary examined, then dilated, then finally examined and discharged.

- 20% of patients are complicated (cataract, glaucoma, etc.). They are first preliminary examined, and then proceed for final examination by a doctor skipping dilation.

- Preliminary examination takes 5 min, with the range 4-6 min.

- Dilation takes to work about 30 min with a range of 20 - 35 min.

- Dilated patients wait in a separate area until they are seen by the next available doctor for final examination.

-Final examination by the doctor for regular patients takes about 20 min with a range from 15 min to 30 min.

- Final examination by the doctor for complicated patients is in the range from 30 min (posterior segment of eye) to 120 min (2 hrs) (cataract, glaucoma) with the most likely being 1 hour (65 min).

- Staffing for base (ideal case) model: 2 doctors for preliminary examination and dilation, and 3 doctors for final examination. Total doctors' staffing: 5. No nurses.

- Staffing for the actual case is 3 nurses for preliminary examination and dilation and 3 doctors for final examination.

           Operations performance metrics target: 80% or more of regular patients wait less than 2 hours. Because of a lack of more specific information "the bulk of patients arriving at 10 am…" was interpreted as some patients (20) arriving from 9 to 10 am, while the remaining 100 (bulk) arrive at 10 am (actually spread from 10 am to 12:30 pm).  

           The question in the problem description document was "How would I monitor the patient waiting time in a clinic of 120 patients with all these parameters to make sure patients are seen as early as possible?"

            From the OR/MS standpoint, the question should have been interpreted as 'Given patient arrival pattern uncertainty, what staffing would be needed to meet the target operational performance?'

OR/MS Solution

            Obviously, an Excel spreadsheet would not be sufficient for analyzing such a dynamic problem. Discrete event simulation modeling is the methodology that should be used to determine the optimal staffing of doctors that meets the operational performance criteria. The first model would be a baseline one (an ideal case) that includes two preliminary exam doctors and three final exam doctors. Again, I suggested this approach without any AI help. As an OR/MS expert, I did not need any help. Details of a discrete event simulation model and simulation results were presented in section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3

The main take-away was that the number of doctors (not nurses) was the bottleneck. Adding full and part-time doctors will elevate this bottleneck. The model suggested the required number of full- and part-time doctors.

           It was possible to simulate many other scenarios using this type of simulation modeling to find out the most satisfactory staffing level and shifts for meeting the operational performance targets. The following simulation scenarios were suggested: (i) the effect of additional part-time exam doctors scheduled for different shifts with various availability; (ii) restriction of the total daily patient volume; (iii) changing the percentage of regular and complicated patients; (iv) various patient arrival patterns; (v) extension of clinic hours or allowing overtime; (vi) or combinations of the above.

AI perspective.

I used the above plain language description as an input into several popular open AI platforms to test what value AI can add to OR/MS approach or improve or enhance it in any way.

The following AI platforms have been used:

            OpenAI GPT-4o Mini By OpenAI

GPT-4o Mini is OpenAI's most advanced model in the small models category. It is multimodal (accepting text or image inputs and outputting text), and has higher intelligence than gpt-3.5-turbo but is just as fast. It is meant to be used for smaller tasks, including vision tasks.

            The AI output was:

"Optimizing patient flow and monitoring waiting times in a busy ophthalmology clinic, particularly with the challenges you've described, requires a structured approach. Utilizing tools like Excel for tracking and managing data or simulation software for modeling can be effective strategies. Here's a comprehensive outline to help you navigate this issue:

1. Data Collection and Monitoring Setup:

Using Excel:

- Patient Tracking Sheet:

Create a log where you input patient arrival times, initial examination start, dilation times, and final exam times.

- Use columns for patient ID, arrival time, start time for prelim exams, dilation duration, start time for final exams, and total waiting time.

- Categorization:

- Separate patients based on their complexity (routine vs. complicated cases) in different sheets/tabs.

- Assign a complexity code (e.g., 1 for simple and 2 for complex) to each patient for sorting and analysis.

 - Nurse and Doctor Schedules:

create a schedule that indicates the availability of each doctor and nurse. Make a note of any planned leaves or availability that may affect patient processing.

- Incorporating Break Times/Disruptions:

- Maintain a log for each doctor to note any disruptions (emergency cases, breaks, etc.) to identify patterns affecting wait times.

2. Data Analysis:

- Calculate Waiting Times:

- Analyze the sheet daily or weekly to calculate the average waiting time, broken down by patient complexity.

- Identify instances where patients exceed the 2-hour waiting window.

- Bottleneck Analysis:

- Identify common bottlenecks (e.g., wait time at vision examination, delays due to complications) and their causes.

- Performance Tracking:

- Track the performance of each doctor and each shift. Analyze if some doctors are consistently slower than their colleagues and identify reasons.

3. Optimizing Patient Flow:

Suggestion Batching:

- Establish a triage system upon patient arrival:

- *Fast-Track Routine Patients:* Identify lower complexity patients who can quickly proceed with exams.

- *Complex Cases Protocol:* Set aside specific days or sessions for complex cases to reduce their impact during peak hours.

Improvement Measures:

- Patient Education:

- Have educational materials ready for patients (pamphlets, videos) to reduce questions and prepare them for examinations.

- Checking Mechanism:

- Ensure nurses check all case sheets and necessary forms are ready before patients are called to see doctors.

- Queue Management:

- Use a digital queue management system (or even a physical digital board) to manage patient queues.

4. Simulation Software:

Using simulation software can help model various scenarios, predict patient flow, and identify optimal staffing for different times.

1. Creating a Simulation Model:

- Input your clinic's workflow, including patient arrival rates, doctor availability, exam times, etc.

- Simulate different scenarios (varying the number of doctors, adding a patient education session, etc.) to see impacts on waiting times and patient satisfaction.

Grok 3 Mini

The latest fast model from xAI. A lightweight model that thinks before responding. Fast, smart, and great for logic-based tasks that do not require deep domain knowledge.

The output was:

"I understand your situation in the ophthalmology hospital-it sounds frustrating to manage patient flow with so many variables and disruptions, especially when you're short-staffed and dealing with unpredictable challenges like complicated cases, patient behaviors, and administrative issues. The good news is that you can absolutely monitor patient waiting times effectively, even with all these parameters, by using tools like Microsoft Excel for basic tracking and analysis, or simulation software for more advanced modeling. This will help you identify bottlenecks, optimize workflows, and maintain a peaceful environment without resorting to shouting or arguments.

I'll break this down step by step: first, I'll explain how to approach the monitoring process, then discuss using Excel as a starting point, and finally, suggest when and how to use simulation software. My goal is to help you create a system that's practical, data-driven, and scalable.

Step 1: Define Your Monitoring Goals and Process

Before diving into tools, let's outline a clear plan. You want to track waiting times for 120 patients per day (from 9 am to 1 pm) while accounting for the challenges you mentioned. Here's how to structure this:

- Key Metrics to Track:

- Arrival Time: When the patient arrives (e.g., many at 10 am in bulk).

- Preliminary Exam Time: Time for vision exam by nurses (5 minutes each) and dilation (30 minutes total per patient).

- Final Exam Time: Time with doctors for simple cases (aim for under 2 hours total) vs. complex cases (over 2 hours).

- Waiting Time: Total time from arrival to start of preliminary exam, plus time from preliminary to final exam.

- Total Turnaround Time: From arrival to departure, including any delays.

- Delays and Challenges: Note reasons for delays (e.g., complicated cases, patient interruptions, missing case sheets, doctor absences).

- Bottlenecks: Track how factors like doctor availability (e.g., only 3 doctors), nurse workload, or interruptions affect flow.

- Target Outcomes:

- Ensure 80% of simple cases (watering & irritation, checkups, etc.) are seen within 2 hours.

- Monitor the 20% complex cases (e.g., cataract, glaucoma) separately, as they exceed 2 hours.

- Use data to forecast staffing needs, reduce disruptions, and improve patient satisfaction.

- Data Collection Process:

- Who Collects Data? Assign a dedicated staff member (e.g., a nurse or admin) to log data in real-time using a simple form or app. This could be done on paper initially and transferred to Excel, or directly into a digital tool.

- How Often? Track data for every patient daily, then analyze it weekly or monthly to spot patterns.

- Handle Challenges in Tracking:

- For complicated cases (e.g., 30+ min exams), add a field to note the extra time.

- For disruptions (e.g., patient delays for coffee or trauma cases), log them as "interruption events" with timestamps.

- To address doctor variability (e.g., slow doctors), track individual doctor consultation times anonymously to identify trends without blame.

- For missing case sheets, integrate a quick check-in system to ensure files are ready.

By focusing on these, you'll create a "feedback loop": collect data, analyze it, and use insights to adjust schedules (e.g., stagger patient arrivals or allocate more time for complex cases).

Step 2: Using Excel for Monitoring

Excel is an excellent, accessible tool to start with-it's free (if you have Microsoft Office), easy to learn, and powerful enough to handle your scenario without needing advanced software. You can use it to log data, calculate wait times, and visualize trends. Here's how to set it up:

A. Set Up Your Excel Spreadsheet

Create a template like this (I'll describe it; you can replicate it in Excel):

1. Columns for Patient Data:

- Patient ID: A unique number for each patient (e.g., auto-generated).

- Arrival Time: Timestamp (e.g., 10:00 AM).

- Patient Type: Categorize as "Simple (80%)" or "Complex (20%)" based on initial assessment.

- Preliminary Exam Start Time: When the nurse starts (e.g., after vision check and dilation).

- Preliminary Exam End Time: Approximately 30 minutes later.

- Final Exam Start Time: When the doctor begins.

- Final Exam End Time: When the doctor finishes.

- Departure Time: When the patient leaves.

- Delay Reasons: A dropdown or text field for notes (e.g., "Complicated case - posterior exam," "Patient interruption," "Doctor in OT," "Missing case sheet").

- Total Wait Time: Calculated field (e.g., =Final Exam Start Time - Arrival Time).

- Total Turnaround Time: Calculated field (e.g., =Departure Time - Arrival Time).

Example of a basic row:

| Patient ID | Arrival Time | Patient Type | Prelim Start | Prelim End | Final Start | Final End | Departure Time | Delay Reasons | Total Wait Time | Total Turnaround |

|------------|--------------|--------------|--------------|------------|-------------|-----------|----------------|---------------|-----------------|------------------|

| 001 | 10:00 AM | Simple | 10:05 AM | 10:35 AM | 11:00 AM | 11:30 AM | 11:45 AM | None | 1 hour | 1 hour 45 min |

2. Formulas for Calculations:

- Use Excel's time functions to automate calculations:

- Total Wait Time: =TEXT(Final Start - Arrival Time, "h:mm") (subtracts times and formats as hours:minutes).

- Total Turnaround Time: =TEXT(Departure Time - Arrival Time, "h:mm").

- Conditional Formatting: Highlight cells where Total Turnaround Time > 2 hours in red, so you can quickly spot issues.

- Averages: Add a summary section at the bottom with formulas like =AVERAGE(Total Wait Time column) to calculate daily averages.

3. Incorporate Challenges into the Sheet:

- Variable Patient Arrival: Use a separate tab to log arrival patterns (e.g., 40 patients at 10 am). Then, use graphs to show peaks.

- Complicated Cases: Add a column for "Exam Duration Override" where you manually enter extra time (e.g., +30 min for posterior exams).

- Interruptions: Create a dropdown list in the Delay Reasons column (e.g., options: "Patient Delay," "Trauma Case," "Doctor Absence"). This helps quantify how often these occur.

- Doctor and Nurse Workload: Track which doctor/nurse handled each patient and how long it took. Use pivot tables to analyze if one doctor is consistently slower.

- Bulk Data Entry: If you have many patients, use Excel's data validation for quick entry (e.g., dropdowns for Patient Type).

B. Daily Workflow with Excel

- Morning Prep: Open the Excel file at 9 am. Have staff enter data as patients are processed.

- Real-Time Monitoring: Use a shared Excel file (via OneDrive or Google Sheets for collaboration) so you can check wait times on the fly. If a patient is approaching 2 hours, alert the team.

- End-of-Day Analysis: After 1 pm, review the data. For example:

- Pie chart for patient types (80% simple vs. 20% complex).

- Line graph for wait times over the day to see spikes at 10 am.

- This will help you answer questions like: "How many patients waited over 2 hours today, and why?"

C. Limitations and Tips for Excel

- Pros: It's simple, cost-effective, and you can start immediately.

- Cons: It doesn't handle randomness well (e.g., simulating "what if a doctor is absent?"), so it's better for tracking real data than predicting scenarios.

- Tips:

- Train your team on basic Excel use (e.g., 1-hour session).

- Back up data daily to avoid loss.

- If you have 120 patients, use Excel's filters and sorting to manage the volume.

With Excel, you can monitor your current situation effectively and start making improvements, like adjusting nurse assignments based on arrival peaks.

Step 3: When and How to Use Simulation Software

If Excel feels insufficient (e.g., you want to simulate "what if" scenarios with your challenges), move to simulation software. This is ideal for modeling complex systems like yours, where variables (e.g., doctor absences, patient interruptions) create uncertainty.

Why Use Simulation Software?

- It allows you to create a virtual model of your clinic, incorporating all your challenges to predict wait times and test solutions.

- For example, you could simulate: "If we have only 3 doctors and 40 patients arrive at 10 am, how does that affect wait times?" Or, "What if we add a buffer for complicated cases?"

Recommended Software:

- Free/Entry-Level Options:

- Excel's own simulation tools (e.g., using Monte Carlo add-ins) for simple scenarios.

- AnyLogic Personal Learning Edition (free for basic use) or Simul8 Community Edition-these are user-friendly for healthcare simulations.

- Paid Options: Arena or ExtendSim if your hospital can invest (they're more powerful but might require training).

How to Set It Up:

1. Choose Software: Start with AnyLogic-it's intuitive for beginners.

2. Model Your Process:

- Input Parameters: Define variables based on your description:

- Patient arrival: Bulk at 10 am (40/hour), with 80% simple and 20% complex.

- Resources: 3 doctors, 3 nurses; each preliminary exam takes 5-30 minutes; final exam varies (e.g., 30 minutes for simple, up to 60+ for complex).

- Challenges: Add random events, like a 10% chance of a trauma case interrupting, or a 20% chance of patient delays.

- Simulation Logic: Create a flowchart:

- Step 1: Patient arrives → Goes to nurse for preliminary exam.

- Step 2: If dilated, queues for doctor.

- Step 3: Doctor examines; add delays for interruptions.

- Output: Simulated wait times for 120 patients.

3. Run Scenarios:

- Baseline: Simulate your current setup.

- "What If?": Test changes, like hiring a temp doctor or staggering arrivals.

4. Analyze Results: The software will output reports, e.g., average wait times, probability of meeting the 2-hour promise.

5. Integration with Excel: Use Excel to import simulation results for further analysis.

Getting Started with Simulation:

- Learning Curve: If you're new, start with online tutorials (e.g., YouTube for AnyLogic). It might take a few days to build a basic model.

- Cost and Time: If your hospital can't afford this, stick with Excel for now and scale up later.

- When to Switch: If you notice patterns in Excel data that need deeper analysis (e.g., frequent delays from complicated cases), simulation will help.

Final Thoughts

You can definitely manage this with Excel as your primary tool-it's straightforward and will give you immediate insights into waiting times while accounting for your challenges. Start by creating the spreadsheet I outlined, collect data for a week, and review it to see improvements. If things get more complex, transition to simulation software for predictive modeling.

This approach will help you foster a more efficient, less chaotic environment by focusing on data rather than reactions. If you share more details (e.g., your current workflow or specific Excel questions), I can refine this further. Good luck-I'm here to assist!"

Free ChatGPT by OpenAI

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The output was :

"To effectively monitor patient waiting times in your ophthalmology clinic, especially given the complexities and challenges you've outlined, a systematic approach is important. While Excel can provide a relatively simple way to track and analyze data, a simulation software tool might be better suited for modeling the dynamic and complex nature of your clinic. Here's a step-by-step guide for both approaches:

Option 1: Using Excel

1. Data Collection:

- Gather data on patient arrivals, the type of cases, time spent on examinations, and outcomes. You will need to track:

- Arrival times

- Time taken by nurses for preliminary examination

- Time taken by doctors for final examination

- Types of cases (routine or complex)

- Any delays or disruptions (e.g., nature's call, language barriers)

2. Create a Tracking Template:

- Design an Excel sheet with columns such as:

- Patient ID

- Arrival Time

- Preliminary Examination Start Time

- Preliminary Examination End Time

- Doctor Consultation Start Time

- Doctor Consultation End Time

- Type of Case

- Total Waiting Time

- Notes (for delays or other disruptions)

3. Formulas:

- Use formulas to calculate waiting times. For instance:

- Total Waiting Time = (Doctor Consultation End Time - Arrival Time)

4. Data Analysis:

- Use pivot tables or graphs to analyze waiting times based on different parameters (e.g., types of cases, doctor efficiency).

- Highlight any trends, such as peak times leading to delays or specific issues causing longer waiting times.

5. Regular Reviews:

- Set a schedule to review the data regularly, and adjust workflows or resource allocation based on findings.

Option 2: Using Simulation Software

1. Choose a Simulation Tool:

- Select software that specializes in healthcare operations, such as AnyLogic, Simul8, or Arena, which can model patient flow.

2. Model Your Clinic:

- Create a model that includes:

- The flow of patients from arrival to consultation.

- Time distributions for examinations (e.g., 5 minutes for preliminary, 15-30 minutes for final depending on complexity).

- Random arrivals based on historical data.

- Include factors for delays, such as patients taking breaks, language barriers, and others.

3. Input Data:

- Input your collected data (arrival rates, processing times, etc.) into the simulation model.

4. Run Simulations:

- Simulate various scenarios to observe how changes in staffing levels, patient arrival patterns, and case complexities impact waiting times.

- Explore "what-if" scenarios, such as what happens when one doctor is unavailable or if more nurses are added for preliminary exams.

5. Output Analysis:

- Analyze the results from the simulation to identify bottlenecks and areas for improvement.

- Use the insights gained to inform changes in scheduling, staffing, and processes.

General Recommendations

1. Standard Operating Procedures (SOPs):

- Standardize processes for routine examinations and consultations to minimize variations caused by different doctors.

2. Patient Education:

- Provide clear instructions to patients before their appointment regarding what to expect, helping them to be prepared (e.g., time for blood tests or what to do with delays).

3. Communication:

- Implement a system for keeping patients informed about expected waiting times and managing their expectations.

4. Resource Allocation:

- Consider hiring additional staff during peak hours or cross-training existing staff to fill in as needed.

5. Feedback Mechanism:

- Create a system for collecting patient feedback on waiting times and experiences to continuously improve the process.

By utilizing either Excel for basic tracking or a more advanced simulation for in-depth analysis, you can gain valuable insights into your clinic's operations, ultimately leading to improved patient satisfaction and efficient use of resources."

------------------------------
Alexander Kolker
ge healthcare
MILWAUKEE WI
------------------------------

Alexander:
I admit that I have my own reservations about using LLMs and GenAI, and that care must be taken when using this new technology.   You wrote:

In my consulting capacity, my goal is to deliver to the customer the specific solution for the posed problem and to convince the customer that I chose the most efficient and direct methodology for doing so.

I think that Warren is suggesting that a GenAI/LLM can assist you in determining that you have made the right decisions in how you approached your customer's problem.  Here, the AI is not the problem solver.  But it can be a handy assistant.

You might want to take a look at this article from OR/MS Today:  https://pubsonline.informs.org/do/10.1287/orms.20 written over a year ago about an experiment done by a workgroup that used ChatGPT to help guide a solution to a proposed analytics problem. Both this article and Warren are suggesting that leveraging this technology can help us as OR practitioners.

As a practitioner, I am on the lookout for applications of GenAI/LLMs that truly deliver value and that justify the investment of time and money. One way is by making our own work easier, which is the thing you have experimented with, and is illustrated in the aforementioned article.  But another is how we can use the technology in the solutions that we deliver to clients so that we enhance the client experience.  One such example is described nicely in this INFORMS MSOM webinar from people at Microsoft:  https://www.informs.org/Resource-Center/Video-Library/Videos-for-Topic-Pages/M-SOM-Practice-Platform-Using-Optimization-and-LLMs-to-Enhance-Cloud-Supply-Chain-Operations .  I believe we'll see additional examples like this in the future.

Warren, I appreciate your taking the time to write your detailed response and suggestions to my post regarding the role of AI for possible synergy with OR/MS.

I think the key to our different assessment of the AI role as an enabler and value-added component to OR/MS is in your phrase: "As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS." Of course, after all, you are the Founder, OptiML AI.

Now, as you can guess, I have an open mind about the value AI, but at this time I am highly skeptical of your belief. But the issue is not someone's personal 'belief' or skepticism. In my consulting capacity, my goal is to deliver to the customer the specific solution for the posed problem and to convince the customer that I chose the most efficient and direct methodology for doing so. With regard to AI, I asked myself: "Do I really need an assistant for solving the specific problem posed to me, be that assistant AI or a human?"  And my answer is resounding no.

      You suggested to me "two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built in." Your suggestions completely changed my and the customer's goals and the subject: instead of focusing on the solution of the customer's business problem, you suggested testing some internal AI issues, such as different prompts, the role that AI should assume, and testing better LLMs. However, I am not interested in spending more of my time exploring the role and the potential of AI with little or no chance of getting additional value. I am interested in solving the customer's business problem using the most appropriate methodology, as I judge it. This difference in focus illustrates the core of the "AI rage" issue: a lot of resources are already spent on AI (LLMs) development. AI promoters, marketers, and investors are desperate to demonstrate 'amazing' applications and the usefulness of this "big beautiful thing". This is the reason why AI is claimed to be an almost universal silver bullet that can assist and significantly enhance human ability for solving difficult problems. AI could potentially be useful in solving ill-defined problems with contradictory multiple goals, non-quantifiable or difficult to formalize factors expressed in natural language, generating new alternative hypotheses, etc. But I am not familiar with examples of such AI applications.  

               Your GPT-5 output included in your reply is a useless word salad that includes everything, from generic JTA to Six Sigma DMAC, from queuing & capacity planning to the list of software with pros & cons (and who is supposed to choose pros & cons-another AI?). From DES to Process Reliability with Lean tools (5S, Kanban, Control charts, FMEA, etc) that are totally irrelevant in this context. And so on and on, page after page that makes an illusion of the deep expertise and value, but actually is a hollow babbling close to hallucination.

Anyway, thank you for the opportunity for discussion.

Alexander,

I sincerely appreciate that you tested a few LLMs to see what kind of assistance they would have provided you in your scenario. That's a great way to learn what works well and what doesn't. The results of your three tests didn't satisfy you that it could help. Might I suggest two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built-in. Also, since you're the expert in the particular area that the client is asking about, maybe a better metric of success for the LLM is not to compare it to what you would have done but what someone with 5-10 years experience in OR/MS would have done. If you had passed this request off to a colleague, what would you have expected to get in return? 

For example, if I prepended this to your client's request: "You are an operations research expert with experience in healthcare. You have read the book "Healthcare Management Engineering in Action" from Springer (2024). You know the way to approach a problem with a fresh perspective and be able to use the INFORMS (informs.org) approach to job task analysis (JTA) and end-to-end solution methodology. Here is a request from a potential client. Outline ways of potentially helping this client, identifying algorithms and software needed. For each, give detailed pros and cons of the approach. Then list clarifying questions that we need to ask the client." and then added the request, submitted it to OpenAI's GPT-5 with 'Thinking' turned on, it gives a different and more detailed answer. I'll post it below - you will need to determine whether or not this is better/worse/same since I'm not experienced in this particular area, but I do think this is a great start that took only a few minutes to get some areas to pursue. As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS.

Here is the GPT-5 output (you can also test on the pro models from X, Anthropic, etc.):

Great brief. Below is a pragmatic, OR-driven plan you can take to leadership. I'll frame it using an INFORMS-style JTA and end-to-end methodology, then outline several complementary solution tracks-with algorithms, software, and concrete pros/cons-followed by the clarifying questions we'll need to ask.

1) Problem framing (JTA + end-to-end method)

Job Task Analysis (JTA) view

• • Purpose: Deliver outpatient ophthalmology consults with an 80% ≤2-hour turnaround, despite variable doctor availability and case complexity.

  • Tasks & resources: Registration → vision exam (nurses) → dilation (30 min for most) → doctor exam → diagnostics (subset) → disposition; resources: 3–5 doctors, 3 nurses, rooms/equipment, paper case sheets.

  • Workflows: Two-stage clinical flow with a fixed delay (dilation), plus branching for complex cases and trauma (priority class).

  • Performance targets: 120 pts/day in 4 clinic hours (9:00–13:00); 80% ≤2h TAT, 20% allowed >2h; minimize >2h waits by clinic; maintain smooth, calm environment.

Method overview (INFORMS/DMAIC)

1. 1. Define: Map the current process (SIPOC + value stream map), define KPIs (TAT, wait time per stage, LWBS/balking, %>2h).

   2. Measure: Time-stamp each handoff; capture arrival pattern (esp. 10–11 a.m. surge), service-time distributions by case type/doctor, routing probabilities, no-shows/walk-aways.

   3. Analyze: Queueing diagnostics; bottleneck identification; variability analysis; capacity vs. demand by 15-min interval.

   4. Improve/Design: Redesign appointment/triage, staffing rosters, pooling rules, priority logic, buffer sizes; add visual controls; 5S for case sheets.

   5. Control/Sustain: Real-time dashboard, escalation rules, control charts; policy for trauma surges and low-staff days; continuous simulation A/B tests for schedule tweaks.

2) Solution tracks (algorithms + software, with pros/cons)

A) Queueing & capacity planning (diagnose the bottlenecks)

What: Model the system as tandem queues: Nurse vision exam → fixed 30-min dilation delay → Doctor exam; with priority classes (trauma high, complex medium, simple low), time-varying arrivals, and server calendars (doctors on leave/OT/rounds).

Algorithms

• • Time-varying Poisson (TVP) arrivals; G/G/c approximations (e.g., square-root-staffing, Allen-Cunneen) for waits.

  • Priority queue formulas (non-preemptive for complex; preemptive for trauma if policy allows).

  • Pooling vs. dedicated servers analysis; Little's Law to sanity-check WIP and TAT.

  • Variability impact (SCV) analysis; bottleneck identification (VUT/Kingman).

Software

• • Excel with add-ins or templates for M/M/c / G/G/c approximations.

  • R (queueing, tidyverse) or Python (NumPy/Pandas + custom calcs).

  • Visualization with Power BI / Tableau.

Pros

• • Fast, low cost, immediate insights.

  • Good for "why is 10–11 a.m. painful?" and staffing what-ifs.

  • Validates feasibility of 120 pts/4 hours with 3 vs. 5 doctors.

Cons

• • Approximations; hard to capture rework, wandering patients, room constraints, and case-sheet losses.

  • Doesn't show minute-by-minute congestion as realistically as simulation.

B) Appointment and arrival smoothing (change the input)

What: Redesign appointment templates and walk-in triage to flatten the 10 a.m. surge; pre-dilation for qualifying patients; stagger doctor start times; buffer slots for trauma/complex.

Algorithms

• • Stochastic appointment scheduling with no-shows and dilation lag; objective: minimize mean/95th-percentile TAT.

  • Overbooking optimization by time block (chance-constrained).

  • Slot mix optimization (simple/complex ratios by 15-min block).

Software

• • Python OR-Tools / PuLP (open-source LP/MIP), or Gurobi/CPLEX if available.

  • Export to Excel scheduling templates for daily use.

Pros

• • Biggest lever: fewer giant peaks → shorter queues everywhere.

  • Works even when only 3 doctors are present.

Cons

• • Requires culture change and booking discipline.

  • Needs reliable complexity triage at scheduling/arrival.

C) Real-time flow control & digital queuing

What: Token-based single queues per stage (avoid "choose your doctor" micro-queues), real-time status boards, SMS recalls for patients who leave for tests/coffee, fixed WIP caps at each step.

Algorithms

• • Dispatch rules: Shortest-processing-time-first for simple pool; triage priority for trauma/complex; pull system from doctor stage to start pre-dilation only when downstream WIP is below cap.

  • Workload leveling (Heijunka) logic.

Software

• • Lightweight kiosk/QR time-stamping + Google Apps Script/Power Apps; or off-the-shelf queue apps.

  • Real-time dashboards in Power BI/Tableau.

Pros

• • Immediate wait-time visibility; fewer arguments at the desk.

  • Reduces "overtaking" by wanderers; controlled WIP lowers TAT variability.

Cons

• • Needs basic IT support and staff training.

  • SMS/WhatsApp workflows must match local patient behavior/languages.

D) Discrete-Event Simulation (DES) of the clinic

What: Build a minute-level simulator of your clinic with actual distributions, calendars, and routing (including dilation, complex cases up to 30 min, trauma preemptions, nurse/room constraints, case-sheet mishaps).

Algorithms

• • DES engine with entity routing, resource seize/release, calendars, priorities, balking/reneging.

  • Simulation-optimization (search over appointment templates, staffing, WIP caps).

Software (choose one)

• • Simul8 Healthcare, AnyLogic, Arena (paid; healthcare libraries, GUIs).

  • SimPy (Python) or simmer (R) (open-source; code-based, very flexible).

  • JaamSim (free GUI) if budget-constrained.

Pros

• • Captures all your "messy realities": wanderers, missing sheets, language delays, doctor-specific speed, trauma preemptions.

  • Lets you test policies safely before rollout; produces persuasive visuals.

Cons

• • Needs clean input data and modeler time.

  • Governance required to keep it updated (avoid "one-off model" trap).

E) Staff rostering and day-of reallocation

What: Build schedules that anticipate OT/leave/camps; cross-train/float roles; day-of robust reallocation when doctors drop from 5 → 3.

Algorithms

• • Integer programming for monthly/weekly rosters with coverage constraints and fairness.

  • Robust optimization or stochastic programming to hedge against absences.

  • Real-time rebalancing heuristic using current WIP + forecast arrivals.

Software

• • OR-Tools CP-SAT, Gurobi/CPLEX; front-end in Excel or a small web app.

Pros

• • Converts variability in staffing into planned buffers.

  • Fairness rules reduce "purposefully slow" behavior incentives.

Cons

• • Requires accurate availability inputs and policy alignment.

  • Optimization outputs must be interpretable and editable by managers.

F) Process reliability: case sheets, 5S, and language support

What: Reduce "special-cause" delays (lost sheets, difficult positioning, language).

Algorithms/tools

• • 5S/visual management for physical files; Kanban for sheet movement.

  • Standard work times with control charts (X-MR) on vision and exam durations.

  • Pareto analysis for delay causes; FMEA for failure modes (lost sheets, device downtime).

  • Interpreter routing (rules-based) and pictorial instruction cards for slit-lamp positioning.

Software

• • Excel/Power BI control charts; lightweight barcode or RFID for case-sheet tracking.

  • Process mining (if EHR timestamps exist) to spot rework loops.

Pros

• • Cheap, immediate waste removal; directly tackles stated pain points.

  • Stabilizes inputs for the DES and scheduling models.

Cons

• • Lean changes require daily management; benefits erode without audits.

G) Performance transparency & incentives (behavioral)

What: Case-mix-adjusted productivity and fairness so no one "wins" by going slow.

Algorithms

• • Multilevel regression to adjust throughput for case complexity and patient mix.

  • Risk-adjusted control charts per doctor; CUSUM to detect sustained drift.

  • Equitable load balancing rules in the dispatcher (pooled queue).

Software

• • R/Python analytics; publish in Power BI/Tableau.

Pros

• • Reduces gaming; focuses debate on data, not personalities.

  • Supports coaching and targeted training.

Cons

• • Needs trust and careful rollout; ensure measures don't encourage cutting corners.

H) Monitoring: how to track waiting time for 120 patients

Minimal viable system

• • Time-stamps at: arrival, start/end vision, start dilation, start/end doctor, diagnostics start/end, disposition. Use QR/short codes on the case sheet or patient token; staff scan at each step (Android phones suffice).

  • Live dashboard: queue lengths per stage, % within 2h today, 95th-percentile wait, per-clinic red/yellow/green.

  • Escalation rules: if predicted >2h exceeds 20% for the next hour, trigger actions (pull in backup doctor, increase simple-case ratio, extend clinic by 15 min, etc.).

Algorithms

• • Short-horizon forecasting of arrivals using moving averages; Erlang-C/G/G/c to predict downstream waits; alerting thresholds.

Software

• • Google Sheets + Apps Script or Power Apps for capture; Power BI for live visuals.

  • Scales later to an EHR-integrated workflow.

Pros

• • Fast to deploy; creates the data spine needed for all other tracks.

  • De-escalates arguments-staff can point to the board, not each other.

Cons

• • Requires consistent scanning; start simple (arrival + doctor start) and add steps.

3) Excel vs. simulation: when to use which

Excel first (weeks 1–2): Queue approximations, simple staffing calculators, appointment templates, daily dashboards.
Simulation next (weeks 3–8): Validate policies with all complexities; run "5→3 doctors" stress tests; design robust playbooks for trauma surges and staff shortages.
Optimization overlays (weeks 4–8): Slot mix, overbooking, and roster optimization informed by the simulation.

4) Quick-win interventions to pilot (low cost)

1. 1. Single pooled queue per stage with token display; ditch room-level micro-queues.

   2. Pre-dilation protocol for clearly simple follow-ups (per clinical policy) to decouple from doctor availability.

   3. Stagger starts (e.g., one doctor at 8:45 for early trickle/trauma buffer; one at 9:15) + buffer slots at 10:00 to absorb the surge.

   4. WIP caps: limit number in dilation buffer and waiting-for-doctor; start pulls only when below cap.

   5. 5S for case sheets + barcode stickers; daily audit.

   6. Interpreter cards and slit-lamp coaching visuals to reduce handling delays.

   7. Daily readiness huddle at 8:50 with forecast and contingency (who is on call if we drop to 2 doctors).

5) Data we'll ask for (minimal to start)

• • Arrivals by 15-min over 4–6 weeks; share of walk-ins vs. booked; no-show rate.

  • Service times (min, mean, 75th/90th/95th percentiles) for: vision exam, doctor exam (simple vs. complex), diagnostics; by doctor if possible.

  • Routing fractions: % needing dilation, diagnostics, procedures; % trauma.

  • Resource calendars: doctor/nurse availability, rooms/equipment counts.

  • Current policies: triage criteria, trauma handling, "overtaking" rules.

  • Outcomes: current %≤2h, LWBS/balking, patient complaints.

6) Clarifying questions for the client

Demand & mix

1. 1. What % of patients truly require dilation, and can any be pre-dilated safely by protocol?

   2. Distribution of simple vs. complex vs. trauma across the day? Are cataract/glaucoma largely pre-booked or walk-ins?

Times & resources
3) Observed time distributions (not just averages) for nurse vision exam, dilation-to-doctor lag beyond 30 min, doctor exam by case type, diagnostics.
4) Number of rooms/slit lamps and any shared equipment constraints.
5) Typical doctor availability patterns (OT days, rounds, camps) and same-day variance.

Policies & priorities
6) Current rules for trauma preemption-can trauma interrupt ongoing consults or is it handled by a designated doctor?
7) Are patients allowed to "hold" their place when they leave for labs/coffee? What is the formal policy vs. actual practice?

Scheduling
8) Share of booked vs. walk-in; appointment template granularity; overbooking currently used?
9) Show-up reliability by time of day/day of week; any patterned late arrivals (e.g., 10–11 a.m. peak due to transport)?

Information flow
10) Do you have EHR time-stamps today? If not, is scanning/QR feasible at each station?
11) Frequency and causes of missing case sheets; can we attach barcodes and define handoff checkpoints?

People & incentives
12) How do you measure doctor productivity now? Are you open to case-mix-adjusted dashboards and pooled queuing to reduce gaming?
13) Language mix of patients; access to interpreters or bilingual staff?

Constraints & goals
14) Acceptable overtime? Willingness to shift some volume to 8:45–9:00 pre-clinic or 13:00–13:15 spillover?
15) Primary success metric: 80th percentile ≤2h, 95th percentile ≤? Specific targets per clinic?

7) Recommended stack (practical)

• • Data capture & dashboards: Google Forms/QR → Google Sheets → Power BI/Tableau.

  • Queue analysis: Excel + Python/R notebooks for G/G/c and priority models.

  • Simulation: Start with SimPy (Python) for flexibility; if budget allows, Simul8 Healthcare for faster stakeholder buy-in.

  • Optimization: OR-Tools (open source) or Gurobi if available; deployment back to Excel templates for scheduling/rosters.

8) What success looks like (within one quarter)

• • Live wait-time board and daily huddles; staff using WIP caps and pooled queues.

  • Appointment template that flattens the 10–11 a.m. spike; buffer for trauma.

  • Case-mix-adjusted productivity view; less variability between doctors.

  • ≥80% ≤2 hours on >80% of clinic days, even when staffing drops to 3 doctors.

If you'd like, I can draft the initial Excel capacity calculator (arrival by 15-min × G/G/c wait estimator) and a SimPy skeleton that mirrors your dilation flow and priority classes, using placeholder distributions until we get your real data.

One More Time About a Synergy of AI and OR/MS

            I have recently attended a webinar organized by the NY Metro INFORMS Chapter on "Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier." https://us06web.zoom.us/webinar/register/WN_HI7YOf3YSaGPYyuC5w6TzQ#/registration

             The panelists aggressively promoted findings from their recent INFORMS Journal on Data Science paper

Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier

This paper revisits Herbert Simon's 1987 vision of a partnership between artificial intelligence (AI) and operations research/management science (OR/MS) to enhance decision-making. 

Two Heads Are Better than One: The Collaboration between AI and OR

I have mixed feelings about this webinar and its conclusions.

Therefore, I decided to test myself what value could be added by AI to OR/MS.

I used, as a test-bed, my consulting work that I had performed some time ago. This example is instructive because it illustrates a typical request for analysis written in plain language with some unstructured numbers by a healthcare manager who is an analytic layman. This is a rather typical example of a problem in a healthcare setting that requires, first, some interpretation and making some reasonable assumptions to perform the analysis using OR/MS.

I used three popular AI platforms (OpenAI GPT-4o Mini by OpenAI; Grok 3 Mini by xAI; and Free ChatGPT by OpenAI). I used, as input, the same consulting request text provided in the attached document.  The AI outcomes and recommendations are also presented in the attached document.

The bottom line:

·       In my opinion, the presenters of the webinar failed to present a business case or a specific example that would clearly demonstrate the AI value added to OR/MS approach.

·       Simon's vision, " we should aspire to increase the impact of MS/OR by incorporating the AI kit of tools that can be applied to ill-structured non-quantitative decision domains, " was and still remains a big beautiful dream.

·       There were no convincing demonstrations of the value added or additional insights to OR/MS methodology by any of the three tested AI platforms. In fact, the AI recommendations were to use OR/MS methods, such as Excel spreadsheets or simulation modeling. Such recommendations were trivial and generic.

This is the original consulting request:

"Center Manager-Wockhardt Eye Hospital. Location: Aurangabad, Maharashtra, India.

Dear Mr.Kolker, let me discuss my problem in detail.

I work in an ophthalmology hospital. The ideal situation I wish is: we treat 120 patients a day with 5 doctors from 9 am to 1 pm, 2 doctors would see a preliminary examination of the eye and dilate the patient for 30 minutes, whereas 3 doctors would start seeing the final examination after the dilation and will dispose the patient. We promise a delivery of 2 hours to 80% of patients who come for watering & irritation, normal checkups, headaches, glasses, and redness. The rest 20% would have cataract, glaucoma, and other complex disease which is bound to take more than 2 hours of investigation. We can monitor the waiting time of more than 2 hours in each clinic. But this 'ideal' situation hardly happens because one doctor would be in operation theatre, one doctor would be on leave, or in ward rounds or in camp, so I am left with only 3 doctors in the unit from 9 am to 1 pm.

           With 3 doctors, the bulk of patients arrive at 10 am (40 patients/hour) all these patients have to go for vision examination by nurses (5 min each patient) and 3 nurses would do it.

The challenges I have observed are:

* arrival of complicated cases, where a thorough examination of the posterior segment of the eye is needed (could take up to 30 min/ patient)

* patients who find it hard to place their chin in front of the slit lamp or language barrier, more inquisitive patients who keep loading the doctors with questions

* patients who would go for nature's call or coffee or to the lab for blood tests and will arrive to see their successors in the doctor's consultation room before them.

* trauma case that needs complete attention before shifting the patient to OT

* doctors who are purposefully slow to make sure that they see fewer patients than their colleagues

* missing case sheets by the nurse who has to spend all her time searching the case sheets.

Now, how would I monitor the patient waiting time in a clinic of 120 patients with all these parameters, to make sure patients are seen as early as possible rather than shouting, and arguing, thus disturbing the entire atmosphere of a peaceful work environment?

Can I work this situation with all the challenges in an Excel sheet or simulation software? How should I go about it? Kindly assist me."

OR/MS perspective

 (see section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3 )

            This problem description should first be converted into a more specific and formal description that would make it amenable to quantitative OR/MS analysis.

Below is my own problem summary that I made without any AI input:

- clinic is open 4 hours per day, from 9 am to 1 pm. The daily patient volume is 120.

- 80% of patients are regular ones (normal checkups, minor irritation, etc.). They are all first preliminary examined, then dilated, then finally examined and discharged.

- 20% of patients are complicated (cataract, glaucoma, etc.). They are first preliminary examined, and then proceed for final examination by a doctor skipping dilation.

- Preliminary examination takes 5 min, with the range 4-6 min.

- Dilation takes to work about 30 min with a range of 20 - 35 min.

- Dilated patients wait in a separate area until they are seen by the next available doctor for final examination.

-Final examination by the doctor for regular patients takes about 20 min with a range from 15 min to 30 min.

- Final examination by the doctor for complicated patients is in the range from 30 min (posterior segment of eye) to 120 min (2 hrs) (cataract, glaucoma) with the most likely being 1 hour (65 min).

- Staffing for base (ideal case) model: 2 doctors for preliminary examination and dilation, and 3 doctors for final examination. Total doctors' staffing: 5. No nurses.

- Staffing for the actual case is 3 nurses for preliminary examination and dilation and 3 doctors for final examination.

           Operations performance metrics target: 80% or more of regular patients wait less than 2 hours. Because of a lack of more specific information "the bulk of patients arriving at 10 am…" was interpreted as some patients (20) arriving from 9 to 10 am, while the remaining 100 (bulk) arrive at 10 am (actually spread from 10 am to 12:30 pm).  

           The question in the problem description document was "How would I monitor the patient waiting time in a clinic of 120 patients with all these parameters to make sure patients are seen as early as possible?"

            From the OR/MS standpoint, the question should have been interpreted as 'Given patient arrival pattern uncertainty, what staffing would be needed to meet the target operational performance?'

OR/MS Solution

            Obviously, an Excel spreadsheet would not be sufficient for analyzing such a dynamic problem. Discrete event simulation modeling is the methodology that should be used to determine the optimal staffing of doctors that meets the operational performance criteria. The first model would be a baseline one (an ideal case) that includes two preliminary exam doctors and three final exam doctors. Again, I suggested this approach without any AI help. As an OR/MS expert, I did not need any help. Details of a discrete event simulation model and simulation results were presented in section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3

The main take-away was that the number of doctors (not nurses) was the bottleneck. Adding full and part-time doctors will elevate this bottleneck. The model suggested the required number of full- and part-time doctors.

           It was possible to simulate many other scenarios using this type of simulation modeling to find out the most satisfactory staffing level and shifts for meeting the operational performance targets. The following simulation scenarios were suggested: (i) the effect of additional part-time exam doctors scheduled for different shifts with various availability; (ii) restriction of the total daily patient volume; (iii) changing the percentage of regular and complicated patients; (iv) various patient arrival patterns; (v) extension of clinic hours or allowing overtime; (vi) or combinations of the above.

AI perspective.

I used the above plain language description as an input into several popular open AI platforms to test what value AI can add to OR/MS approach or improve or enhance it in any way.

The following AI platforms have been used:

            OpenAI GPT-4o Mini By OpenAI

GPT-4o Mini is OpenAI's most advanced model in the small models category. It is multimodal (accepting text or image inputs and outputting text), and has higher intelligence than gpt-3.5-turbo but is just as fast. It is meant to be used for smaller tasks, including vision tasks.

            The AI output was:

"Optimizing patient flow and monitoring waiting times in a busy ophthalmology clinic, particularly with the challenges you've described, requires a structured approach. Utilizing tools like Excel for tracking and managing data or simulation software for modeling can be effective strategies. Here's a comprehensive outline to help you navigate this issue:

1. Data Collection and Monitoring Setup:

Using Excel:

- Patient Tracking Sheet:

Create a log where you input patient arrival times, initial examination start, dilation times, and final exam times.

- Use columns for patient ID, arrival time, start time for prelim exams, dilation duration, start time for final exams, and total waiting time.

- Categorization:

- Separate patients based on their complexity (routine vs. complicated cases) in different sheets/tabs.

- Assign a complexity code (e.g., 1 for simple and 2 for complex) to each patient for sorting and analysis.

 - Nurse and Doctor Schedules:

create a schedule that indicates the availability of each doctor and nurse. Make a note of any planned leaves or availability that may affect patient processing.

- Incorporating Break Times/Disruptions:

- Maintain a log for each doctor to note any disruptions (emergency cases, breaks, etc.) to identify patterns affecting wait times.

2. Data Analysis:

- Calculate Waiting Times:

- Analyze the sheet daily or weekly to calculate the average waiting time, broken down by patient complexity.

- Identify instances where patients exceed the 2-hour waiting window.

- Bottleneck Analysis:

- Identify common bottlenecks (e.g., wait time at vision examination, delays due to complications) and their causes.

- Performance Tracking:

- Track the performance of each doctor and each shift. Analyze if some doctors are consistently slower than their colleagues and identify reasons.

3. Optimizing Patient Flow:

Suggestion Batching:

- Establish a triage system upon patient arrival:

- *Fast-Track Routine Patients:* Identify lower complexity patients who can quickly proceed with exams.

- *Complex Cases Protocol:* Set aside specific days or sessions for complex cases to reduce their impact during peak hours.

Improvement Measures:

- Patient Education:

- Have educational materials ready for patients (pamphlets, videos) to reduce questions and prepare them for examinations.

- Checking Mechanism:

- Ensure nurses check all case sheets and necessary forms are ready before patients are called to see doctors.

- Queue Management:

- Use a digital queue management system (or even a physical digital board) to manage patient queues.

4. Simulation Software:

Using simulation software can help model various scenarios, predict patient flow, and identify optimal staffing for different times.

1. Creating a Simulation Model:

- Input your clinic's workflow, including patient arrival rates, doctor availability, exam times, etc.

- Simulate different scenarios (varying the number of doctors, adding a patient education session, etc.) to see impacts on waiting times and patient satisfaction.

Grok 3 Mini

The latest fast model from xAI. A lightweight model that thinks before responding. Fast, smart, and great for logic-based tasks that do not require deep domain knowledge.

The output was:

"I understand your situation in the ophthalmology hospital-it sounds frustrating to manage patient flow with so many variables and disruptions, especially when you're short-staffed and dealing with unpredictable challenges like complicated cases, patient behaviors, and administrative issues. The good news is that you can absolutely monitor patient waiting times effectively, even with all these parameters, by using tools like Microsoft Excel for basic tracking and analysis, or simulation software for more advanced modeling. This will help you identify bottlenecks, optimize workflows, and maintain a peaceful environment without resorting to shouting or arguments.

I'll break this down step by step: first, I'll explain how to approach the monitoring process, then discuss using Excel as a starting point, and finally, suggest when and how to use simulation software. My goal is to help you create a system that's practical, data-driven, and scalable.

Step 1: Define Your Monitoring Goals and Process

Before diving into tools, let's outline a clear plan. You want to track waiting times for 120 patients per day (from 9 am to 1 pm) while accounting for the challenges you mentioned. Here's how to structure this:

- Key Metrics to Track:

- Arrival Time: When the patient arrives (e.g., many at 10 am in bulk).

- Preliminary Exam Time: Time for vision exam by nurses (5 minutes each) and dilation (30 minutes total per patient).

- Final Exam Time: Time with doctors for simple cases (aim for under 2 hours total) vs. complex cases (over 2 hours).

- Waiting Time: Total time from arrival to start of preliminary exam, plus time from preliminary to final exam.

- Total Turnaround Time: From arrival to departure, including any delays.

- Delays and Challenges: Note reasons for delays (e.g., complicated cases, patient interruptions, missing case sheets, doctor absences).

- Bottlenecks: Track how factors like doctor availability (e.g., only 3 doctors), nurse workload, or interruptions affect flow.

- Target Outcomes:

- Ensure 80% of simple cases (watering & irritation, checkups, etc.) are seen within 2 hours.

- Monitor the 20% complex cases (e.g., cataract, glaucoma) separately, as they exceed 2 hours.

- Use data to forecast staffing needs, reduce disruptions, and improve patient satisfaction.

- Data Collection Process:

- Who Collects Data? Assign a dedicated staff member (e.g., a nurse or admin) to log data in real-time using a simple form or app. This could be done on paper initially and transferred to Excel, or directly into a digital tool.

- How Often? Track data for every patient daily, then analyze it weekly or monthly to spot patterns.

- Handle Challenges in Tracking:

- For complicated cases (e.g., 30+ min exams), add a field to note the extra time.

- For disruptions (e.g., patient delays for coffee or trauma cases), log them as "interruption events" with timestamps.

- To address doctor variability (e.g., slow doctors), track individual doctor consultation times anonymously to identify trends without blame.

- For missing case sheets, integrate a quick check-in system to ensure files are ready.

By focusing on these, you'll create a "feedback loop": collect data, analyze it, and use insights to adjust schedules (e.g., stagger patient arrivals or allocate more time for complex cases).

Step 2: Using Excel for Monitoring

Excel is an excellent, accessible tool to start with-it's free (if you have Microsoft Office), easy to learn, and powerful enough to handle your scenario without needing advanced software. You can use it to log data, calculate wait times, and visualize trends. Here's how to set it up:

A. Set Up Your Excel Spreadsheet

Create a template like this (I'll describe it; you can replicate it in Excel):

1. Columns for Patient Data:

- Patient ID: A unique number for each patient (e.g., auto-generated).

- Arrival Time: Timestamp (e.g., 10:00 AM).

- Patient Type: Categorize as "Simple (80%)" or "Complex (20%)" based on initial assessment.

- Preliminary Exam Start Time: When the nurse starts (e.g., after vision check and dilation).

- Preliminary Exam End Time: Approximately 30 minutes later.

- Final Exam Start Time: When the doctor begins.

- Final Exam End Time: When the doctor finishes.

- Departure Time: When the patient leaves.

- Delay Reasons: A dropdown or text field for notes (e.g., "Complicated case - posterior exam," "Patient interruption," "Doctor in OT," "Missing case sheet").

- Total Wait Time: Calculated field (e.g., =Final Exam Start Time - Arrival Time).

- Total Turnaround Time: Calculated field (e.g., =Departure Time - Arrival Time).

Example of a basic row:

| Patient ID | Arrival Time | Patient Type | Prelim Start | Prelim End | Final Start | Final End | Departure Time | Delay Reasons | Total Wait Time | Total Turnaround |

|------------|--------------|--------------|--------------|------------|-------------|-----------|----------------|---------------|-----------------|------------------|

| 001 | 10:00 AM | Simple | 10:05 AM | 10:35 AM | 11:00 AM | 11:30 AM | 11:45 AM | None | 1 hour | 1 hour 45 min |

2. Formulas for Calculations:

- Use Excel's time functions to automate calculations:

- Total Wait Time: =TEXT(Final Start - Arrival Time, "h:mm") (subtracts times and formats as hours:minutes).

- Total Turnaround Time: =TEXT(Departure Time - Arrival Time, "h:mm").

- Conditional Formatting: Highlight cells where Total Turnaround Time > 2 hours in red, so you can quickly spot issues.

- Averages: Add a summary section at the bottom with formulas like =AVERAGE(Total Wait Time column) to calculate daily averages.

3. Incorporate Challenges into the Sheet:

- Variable Patient Arrival: Use a separate tab to log arrival patterns (e.g., 40 patients at 10 am). Then, use graphs to show peaks.

- Complicated Cases: Add a column for "Exam Duration Override" where you manually enter extra time (e.g., +30 min for posterior exams).

- Interruptions: Create a dropdown list in the Delay Reasons column (e.g., options: "Patient Delay," "Trauma Case," "Doctor Absence"). This helps quantify how often these occur.

- Doctor and Nurse Workload: Track which doctor/nurse handled each patient and how long it took. Use pivot tables to analyze if one doctor is consistently slower.

- Bulk Data Entry: If you have many patients, use Excel's data validation for quick entry (e.g., dropdowns for Patient Type).

B. Daily Workflow with Excel

- Morning Prep: Open the Excel file at 9 am. Have staff enter data as patients are processed.

- Real-Time Monitoring: Use a shared Excel file (via OneDrive or Google Sheets for collaboration) so you can check wait times on the fly. If a patient is approaching 2 hours, alert the team.

- End-of-Day Analysis: After 1 pm, review the data. For example:

- Pie chart for patient types (80% simple vs. 20% complex).

- Line graph for wait times over the day to see spikes at 10 am.

- This will help you answer questions like: "How many patients waited over 2 hours today, and why?"

C. Limitations and Tips for Excel

- Pros: It's simple, cost-effective, and you can start immediately.

- Cons: It doesn't handle randomness well (e.g., simulating "what if a doctor is absent?"), so it's better for tracking real data than predicting scenarios.

- Tips:

- Train your team on basic Excel use (e.g., 1-hour session).

- Back up data daily to avoid loss.

- If you have 120 patients, use Excel's filters and sorting to manage the volume.

With Excel, you can monitor your current situation effectively and start making improvements, like adjusting nurse assignments based on arrival peaks.

Step 3: When and How to Use Simulation Software

If Excel feels insufficient (e.g., you want to simulate "what if" scenarios with your challenges), move to simulation software. This is ideal for modeling complex systems like yours, where variables (e.g., doctor absences, patient interruptions) create uncertainty.

Why Use Simulation Software?

- It allows you to create a virtual model of your clinic, incorporating all your challenges to predict wait times and test solutions.

- For example, you could simulate: "If we have only 3 doctors and 40 patients arrive at 10 am, how does that affect wait times?" Or, "What if we add a buffer for complicated cases?"

Recommended Software:

- Free/Entry-Level Options:

- Excel's own simulation tools (e.g., using Monte Carlo add-ins) for simple scenarios.

- AnyLogic Personal Learning Edition (free for basic use) or Simul8 Community Edition-these are user-friendly for healthcare simulations.

- Paid Options: Arena or ExtendSim if your hospital can invest (they're more powerful but might require training).

How to Set It Up:

1. Choose Software: Start with AnyLogic-it's intuitive for beginners.

2. Model Your Process:

- Input Parameters: Define variables based on your description:

- Patient arrival: Bulk at 10 am (40/hour), with 80% simple and 20% complex.

- Resources: 3 doctors, 3 nurses; each preliminary exam takes 5-30 minutes; final exam varies (e.g., 30 minutes for simple, up to 60+ for complex).

- Challenges: Add random events, like a 10% chance of a trauma case interrupting, or a 20% chance of patient delays.

- Simulation Logic: Create a flowchart:

- Step 1: Patient arrives → Goes to nurse for preliminary exam.

- Step 2: If dilated, queues for doctor.

- Step 3: Doctor examines; add delays for interruptions.

- Output: Simulated wait times for 120 patients.

3. Run Scenarios:

- Baseline: Simulate your current setup.

- "What If?": Test changes, like hiring a temp doctor or staggering arrivals.

4. Analyze Results: The software will output reports, e.g., average wait times, probability of meeting the 2-hour promise.

5. Integration with Excel: Use Excel to import simulation results for further analysis.

Getting Started with Simulation:

- Learning Curve: If you're new, start with online tutorials (e.g., YouTube for AnyLogic). It might take a few days to build a basic model.

- Cost and Time: If your hospital can't afford this, stick with Excel for now and scale up later.

- When to Switch: If you notice patterns in Excel data that need deeper analysis (e.g., frequent delays from complicated cases), simulation will help.

Final Thoughts

You can definitely manage this with Excel as your primary tool-it's straightforward and will give you immediate insights into waiting times while accounting for your challenges. Start by creating the spreadsheet I outlined, collect data for a week, and review it to see improvements. If things get more complex, transition to simulation software for predictive modeling.

This approach will help you foster a more efficient, less chaotic environment by focusing on data rather than reactions. If you share more details (e.g., your current workflow or specific Excel questions), I can refine this further. Good luck-I'm here to assist!"

Free ChatGPT by OpenAI

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The output was :

"To effectively monitor patient waiting times in your ophthalmology clinic, especially given the complexities and challenges you've outlined, a systematic approach is important. While Excel can provide a relatively simple way to track and analyze data, a simulation software tool might be better suited for modeling the dynamic and complex nature of your clinic. Here's a step-by-step guide for both approaches:

Option 1: Using Excel

1. Data Collection:

- Gather data on patient arrivals, the type of cases, time spent on examinations, and outcomes. You will need to track:

- Arrival times

- Time taken by nurses for preliminary examination

- Time taken by doctors for final examination

- Types of cases (routine or complex)

- Any delays or disruptions (e.g., nature's call, language barriers)

2. Create a Tracking Template:

- Design an Excel sheet with columns such as:

- Patient ID

- Arrival Time

- Preliminary Examination Start Time

- Preliminary Examination End Time

- Doctor Consultation Start Time

- Doctor Consultation End Time

- Type of Case

- Total Waiting Time

- Notes (for delays or other disruptions)

3. Formulas:

- Use formulas to calculate waiting times. For instance:

- Total Waiting Time = (Doctor Consultation End Time - Arrival Time)

4. Data Analysis:

- Use pivot tables or graphs to analyze waiting times based on different parameters (e.g., types of cases, doctor efficiency).

- Highlight any trends, such as peak times leading to delays or specific issues causing longer waiting times.

5. Regular Reviews:

- Set a schedule to review the data regularly, and adjust workflows or resource allocation based on findings.

Option 2: Using Simulation Software

1. Choose a Simulation Tool:

- Select software that specializes in healthcare operations, such as AnyLogic, Simul8, or Arena, which can model patient flow.

2. Model Your Clinic:

- Create a model that includes:

- The flow of patients from arrival to consultation.

- Time distributions for examinations (e.g., 5 minutes for preliminary, 15-30 minutes for final depending on complexity).

- Random arrivals based on historical data.

- Include factors for delays, such as patients taking breaks, language barriers, and others.

3. Input Data:

- Input your collected data (arrival rates, processing times, etc.) into the simulation model.

4. Run Simulations:

- Simulate various scenarios to observe how changes in staffing levels, patient arrival patterns, and case complexities impact waiting times.

- Explore "what-if" scenarios, such as what happens when one doctor is unavailable or if more nurses are added for preliminary exams.

5. Output Analysis:

- Analyze the results from the simulation to identify bottlenecks and areas for improvement.

- Use the insights gained to inform changes in scheduling, staffing, and processes.

General Recommendations

1. Standard Operating Procedures (SOPs):

- Standardize processes for routine examinations and consultations to minimize variations caused by different doctors.

2. Patient Education:

- Provide clear instructions to patients before their appointment regarding what to expect, helping them to be prepared (e.g., time for blood tests or what to do with delays).

3. Communication:

- Implement a system for keeping patients informed about expected waiting times and managing their expectations.

4. Resource Allocation:

- Consider hiring additional staff during peak hours or cross-training existing staff to fill in as needed.

5. Feedback Mechanism:

- Create a system for collecting patient feedback on waiting times and experiences to continuously improve the process.

By utilizing either Excel for basic tracking or a more advanced simulation for in-depth analysis, you can gain valuable insights into your clinic's operations, ultimately leading to improved patient satisfaction and efficient use of resources."

------------------------------
Alexander Kolker
ge healthcare
MILWAUKEE WI
------------------------------

Irv,

Thank you for your feedback.

1st, the link that you suggested https://pubsonline.informs.org/do/10.1287/orms.20

does not work, returning 'An Error has occurred, please contact your System Administrator.' With no exact reference to the authors, I am unable to read this paper.

2nd, you write "Warren is suggesting that a GenAI/LLM can assist you in determining that you have made the right decisions in how you approached your customer's problem.  Here, the AI is not the problem solver.  But it can be a handy assistant."

But I made clear in my post, "With regard to AI, I asked myself: "Do I really need an assistant for solving the specific problem posed to me, be that assistant AI or a human?"  And my answer is resounding no."

Certainly, everybody (even the most experienced consultant) can make a mistake in choosing the best approach to solving the client's problem.  However, what makes you think that AI will better "help guide a solution to a proposed analytics problem. Both this article and Warren are suggesting that leveraging this technology can help us as OR practitioners."?? Why do I have to trust AI more than my own judgment and experience? How do we know, as OR practitioners, that AI technology will truly "enhance the client experience'??

I see the whole issue from a different angle: if someone, be it AI or a human, suggests something, I will have to spend my time verifying the validity and value of this suggestion. After all, I am responsible for the outcome of my work, not AI nor anyone else. I

Thanks again

Alexander:
I admit that I have my own reservations about using LLMs and GenAI, and that care must be taken when using this new technology.   You wrote:

In my consulting capacity, my goal is to deliver to the customer the specific solution for the posed problem and to convince the customer that I chose the most efficient and direct methodology for doing so.

I think that Warren is suggesting that a GenAI/LLM can assist you in determining that you have made the right decisions in how you approached your customer's problem.  Here, the AI is not the problem solver.  But it can be a handy assistant.

You might want to take a look at this article from OR/MS Today:  https://pubsonline.informs.org/do/10.1287/orms.20 written over a year ago about an experiment done by a workgroup that used ChatGPT to help guide a solution to a proposed analytics problem. Both this article and Warren are suggesting that leveraging this technology can help us as OR practitioners.

As a practitioner, I am on the lookout for applications of GenAI/LLMs that truly deliver value and that justify the investment of time and money. One way is by making our own work easier, which is the thing you have experimented with, and is illustrated in the aforementioned article.  But another is how we can use the technology in the solutions that we deliver to clients so that we enhance the client experience.  One such example is described nicely in this INFORMS MSOM webinar from people at Microsoft:  https://www.informs.org/Resource-Center/Video-Library/Videos-for-Topic-Pages/M-SOM-Practice-Platform-Using-Optimization-and-LLMs-to-Enhance-Cloud-Supply-Chain-Operations .  I believe we'll see additional examples like this in the future.

Warren, I appreciate your taking the time to write your detailed response and suggestions to my post regarding the role of AI for possible synergy with OR/MS.

I think the key to our different assessment of the AI role as an enabler and value-added component to OR/MS is in your phrase: "As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS." Of course, after all, you are the Founder, OptiML AI.

Now, as you can guess, I have an open mind about the value AI, but at this time I am highly skeptical of your belief. But the issue is not someone's personal 'belief' or skepticism. In my consulting capacity, my goal is to deliver to the customer the specific solution for the posed problem and to convince the customer that I chose the most efficient and direct methodology for doing so. With regard to AI, I asked myself: "Do I really need an assistant for solving the specific problem posed to me, be that assistant AI or a human?"  And my answer is resounding no.

      You suggested to me "two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built in." Your suggestions completely changed my and the customer's goals and the subject: instead of focusing on the solution of the customer's business problem, you suggested testing some internal AI issues, such as different prompts, the role that AI should assume, and testing better LLMs. However, I am not interested in spending more of my time exploring the role and the potential of AI with little or no chance of getting additional value. I am interested in solving the customer's business problem using the most appropriate methodology, as I judge it. This difference in focus illustrates the core of the "AI rage" issue: a lot of resources are already spent on AI (LLMs) development. AI promoters, marketers, and investors are desperate to demonstrate 'amazing' applications and the usefulness of this "big beautiful thing". This is the reason why AI is claimed to be an almost universal silver bullet that can assist and significantly enhance human ability for solving difficult problems. AI could potentially be useful in solving ill-defined problems with contradictory multiple goals, non-quantifiable or difficult to formalize factors expressed in natural language, generating new alternative hypotheses, etc. But I am not familiar with examples of such AI applications.  

               Your GPT-5 output included in your reply is a useless word salad that includes everything, from generic JTA to Six Sigma DMAC, from queuing & capacity planning to the list of software with pros & cons (and who is supposed to choose pros & cons-another AI?). From DES to Process Reliability with Lean tools (5S, Kanban, Control charts, FMEA, etc) that are totally irrelevant in this context. And so on and on, page after page that makes an illusion of the deep expertise and value, but actually is a hollow babbling close to hallucination.

Anyway, thank you for the opportunity for discussion.

Alexander,

I sincerely appreciate that you tested a few LLMs to see what kind of assistance they would have provided you in your scenario. That's a great way to learn what works well and what doesn't. The results of your three tests didn't satisfy you that it could help. Might I suggest two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built-in. Also, since you're the expert in the particular area that the client is asking about, maybe a better metric of success for the LLM is not to compare it to what you would have done but what someone with 5-10 years experience in OR/MS would have done. If you had passed this request off to a colleague, what would you have expected to get in return? 

For example, if I prepended this to your client's request: "You are an operations research expert with experience in healthcare. You have read the book "Healthcare Management Engineering in Action" from Springer (2024). You know the way to approach a problem with a fresh perspective and be able to use the INFORMS (informs.org) approach to job task analysis (JTA) and end-to-end solution methodology. Here is a request from a potential client. Outline ways of potentially helping this client, identifying algorithms and software needed. For each, give detailed pros and cons of the approach. Then list clarifying questions that we need to ask the client." and then added the request, submitted it to OpenAI's GPT-5 with 'Thinking' turned on, it gives a different and more detailed answer. I'll post it below - you will need to determine whether or not this is better/worse/same since I'm not experienced in this particular area, but I do think this is a great start that took only a few minutes to get some areas to pursue. As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS.

Here is the GPT-5 output (you can also test on the pro models from X, Anthropic, etc.):

Great brief. Below is a pragmatic, OR-driven plan you can take to leadership. I'll frame it using an INFORMS-style JTA and end-to-end methodology, then outline several complementary solution tracks-with algorithms, software, and concrete pros/cons-followed by the clarifying questions we'll need to ask.

1) Problem framing (JTA + end-to-end method)

Job Task Analysis (JTA) view

• • Purpose: Deliver outpatient ophthalmology consults with an 80% ≤2-hour turnaround, despite variable doctor availability and case complexity.

  • Tasks & resources: Registration → vision exam (nurses) → dilation (30 min for most) → doctor exam → diagnostics (subset) → disposition; resources: 3–5 doctors, 3 nurses, rooms/equipment, paper case sheets.

  • Workflows: Two-stage clinical flow with a fixed delay (dilation), plus branching for complex cases and trauma (priority class).

  • Performance targets: 120 pts/day in 4 clinic hours (9:00–13:00); 80% ≤2h TAT, 20% allowed >2h; minimize >2h waits by clinic; maintain smooth, calm environment.

Method overview (INFORMS/DMAIC)

1. 1. Define: Map the current process (SIPOC + value stream map), define KPIs (TAT, wait time per stage, LWBS/balking, %>2h).

   2. Measure: Time-stamp each handoff; capture arrival pattern (esp. 10–11 a.m. surge), service-time distributions by case type/doctor, routing probabilities, no-shows/walk-aways.

   3. Analyze: Queueing diagnostics; bottleneck identification; variability analysis; capacity vs. demand by 15-min interval.

   4. Improve/Design: Redesign appointment/triage, staffing rosters, pooling rules, priority logic, buffer sizes; add visual controls; 5S for case sheets.

   5. Control/Sustain: Real-time dashboard, escalation rules, control charts; policy for trauma surges and low-staff days; continuous simulation A/B tests for schedule tweaks.

2) Solution tracks (algorithms + software, with pros/cons)

A) Queueing & capacity planning (diagnose the bottlenecks)

What: Model the system as tandem queues: Nurse vision exam → fixed 30-min dilation delay → Doctor exam; with priority classes (trauma high, complex medium, simple low), time-varying arrivals, and server calendars (doctors on leave/OT/rounds).

Algorithms

• • Time-varying Poisson (TVP) arrivals; G/G/c approximations (e.g., square-root-staffing, Allen-Cunneen) for waits.

  • Priority queue formulas (non-preemptive for complex; preemptive for trauma if policy allows).

  • Pooling vs. dedicated servers analysis; Little's Law to sanity-check WIP and TAT.

  • Variability impact (SCV) analysis; bottleneck identification (VUT/Kingman).

Software

• • Excel with add-ins or templates for M/M/c / G/G/c approximations.

  • R (queueing, tidyverse) or Python (NumPy/Pandas + custom calcs).

  • Visualization with Power BI / Tableau.

Pros

• • Fast, low cost, immediate insights.

  • Good for "why is 10–11 a.m. painful?" and staffing what-ifs.

  • Validates feasibility of 120 pts/4 hours with 3 vs. 5 doctors.

Cons

• • Approximations; hard to capture rework, wandering patients, room constraints, and case-sheet losses.

  • Doesn't show minute-by-minute congestion as realistically as simulation.

B) Appointment and arrival smoothing (change the input)

What: Redesign appointment templates and walk-in triage to flatten the 10 a.m. surge; pre-dilation for qualifying patients; stagger doctor start times; buffer slots for trauma/complex.

Algorithms

• • Stochastic appointment scheduling with no-shows and dilation lag; objective: minimize mean/95th-percentile TAT.

  • Overbooking optimization by time block (chance-constrained).

  • Slot mix optimization (simple/complex ratios by 15-min block).

Software

• • Python OR-Tools / PuLP (open-source LP/MIP), or Gurobi/CPLEX if available.

  • Export to Excel scheduling templates for daily use.

Pros

• • Biggest lever: fewer giant peaks → shorter queues everywhere.

  • Works even when only 3 doctors are present.

Cons

• • Requires culture change and booking discipline.

  • Needs reliable complexity triage at scheduling/arrival.

C) Real-time flow control & digital queuing

What: Token-based single queues per stage (avoid "choose your doctor" micro-queues), real-time status boards, SMS recalls for patients who leave for tests/coffee, fixed WIP caps at each step.

Algorithms

• • Dispatch rules: Shortest-processing-time-first for simple pool; triage priority for trauma/complex; pull system from doctor stage to start pre-dilation only when downstream WIP is below cap.

  • Workload leveling (Heijunka) logic.

Software

• • Lightweight kiosk/QR time-stamping + Google Apps Script/Power Apps; or off-the-shelf queue apps.

  • Real-time dashboards in Power BI/Tableau.

Pros

• • Immediate wait-time visibility; fewer arguments at the desk.

  • Reduces "overtaking" by wanderers; controlled WIP lowers TAT variability.

Cons

• • Needs basic IT support and staff training.

  • SMS/WhatsApp workflows must match local patient behavior/languages.

D) Discrete-Event Simulation (DES) of the clinic

What: Build a minute-level simulator of your clinic with actual distributions, calendars, and routing (including dilation, complex cases up to 30 min, trauma preemptions, nurse/room constraints, case-sheet mishaps).

Algorithms

• • DES engine with entity routing, resource seize/release, calendars, priorities, balking/reneging.

  • Simulation-optimization (search over appointment templates, staffing, WIP caps).

Software (choose one)

• • Simul8 Healthcare, AnyLogic, Arena (paid; healthcare libraries, GUIs).

  • SimPy (Python) or simmer (R) (open-source; code-based, very flexible).

  • JaamSim (free GUI) if budget-constrained.

Pros

• • Captures all your "messy realities": wanderers, missing sheets, language delays, doctor-specific speed, trauma preemptions.

  • Lets you test policies safely before rollout; produces persuasive visuals.

Cons

• • Needs clean input data and modeler time.

  • Governance required to keep it updated (avoid "one-off model" trap).

E) Staff rostering and day-of reallocation

What: Build schedules that anticipate OT/leave/camps; cross-train/float roles; day-of robust reallocation when doctors drop from 5 → 3.

Algorithms

• • Integer programming for monthly/weekly rosters with coverage constraints and fairness.

  • Robust optimization or stochastic programming to hedge against absences.

  • Real-time rebalancing heuristic using current WIP + forecast arrivals.

Software

• • OR-Tools CP-SAT, Gurobi/CPLEX; front-end in Excel or a small web app.

Pros

• • Converts variability in staffing into planned buffers.

  • Fairness rules reduce "purposefully slow" behavior incentives.

Cons

• • Requires accurate availability inputs and policy alignment.

  • Optimization outputs must be interpretable and editable by managers.

F) Process reliability: case sheets, 5S, and language support

What: Reduce "special-cause" delays (lost sheets, difficult positioning, language).

Algorithms/tools

• • 5S/visual management for physical files; Kanban for sheet movement.

  • Standard work times with control charts (X-MR) on vision and exam durations.

  • Pareto analysis for delay causes; FMEA for failure modes (lost sheets, device downtime).

  • Interpreter routing (rules-based) and pictorial instruction cards for slit-lamp positioning.

Software

• • Excel/Power BI control charts; lightweight barcode or RFID for case-sheet tracking.

  • Process mining (if EHR timestamps exist) to spot rework loops.

Pros

• • Cheap, immediate waste removal; directly tackles stated pain points.

  • Stabilizes inputs for the DES and scheduling models.

Cons

• • Lean changes require daily management; benefits erode without audits.

G) Performance transparency & incentives (behavioral)

What: Case-mix-adjusted productivity and fairness so no one "wins" by going slow.

Algorithms

• • Multilevel regression to adjust throughput for case complexity and patient mix.

  • Risk-adjusted control charts per doctor; CUSUM to detect sustained drift.

  • Equitable load balancing rules in the dispatcher (pooled queue).

Software

• • R/Python analytics; publish in Power BI/Tableau.

Pros

• • Reduces gaming; focuses debate on data, not personalities.

  • Supports coaching and targeted training.

Cons

• • Needs trust and careful rollout; ensure measures don't encourage cutting corners.

H) Monitoring: how to track waiting time for 120 patients

Minimal viable system

• • Time-stamps at: arrival, start/end vision, start dilation, start/end doctor, diagnostics start/end, disposition. Use QR/short codes on the case sheet or patient token; staff scan at each step (Android phones suffice).

  • Live dashboard: queue lengths per stage, % within 2h today, 95th-percentile wait, per-clinic red/yellow/green.

  • Escalation rules: if predicted >2h exceeds 20% for the next hour, trigger actions (pull in backup doctor, increase simple-case ratio, extend clinic by 15 min, etc.).

Algorithms

• • Short-horizon forecasting of arrivals using moving averages; Erlang-C/G/G/c to predict downstream waits; alerting thresholds.

Software

• • Google Sheets + Apps Script or Power Apps for capture; Power BI for live visuals.

  • Scales later to an EHR-integrated workflow.

Pros

• • Fast to deploy; creates the data spine needed for all other tracks.

  • De-escalates arguments-staff can point to the board, not each other.

Cons

• • Requires consistent scanning; start simple (arrival + doctor start) and add steps.

3) Excel vs. simulation: when to use which

Excel first (weeks 1–2): Queue approximations, simple staffing calculators, appointment templates, daily dashboards.
Simulation next (weeks 3–8): Validate policies with all complexities; run "5→3 doctors" stress tests; design robust playbooks for trauma surges and staff shortages.
Optimization overlays (weeks 4–8): Slot mix, overbooking, and roster optimization informed by the simulation.

4) Quick-win interventions to pilot (low cost)

1. 1. Single pooled queue per stage with token display; ditch room-level micro-queues.

   2. Pre-dilation protocol for clearly simple follow-ups (per clinical policy) to decouple from doctor availability.

   3. Stagger starts (e.g., one doctor at 8:45 for early trickle/trauma buffer; one at 9:15) + buffer slots at 10:00 to absorb the surge.

   4. WIP caps: limit number in dilation buffer and waiting-for-doctor; start pulls only when below cap.

   5. 5S for case sheets + barcode stickers; daily audit.

   6. Interpreter cards and slit-lamp coaching visuals to reduce handling delays.

   7. Daily readiness huddle at 8:50 with forecast and contingency (who is on call if we drop to 2 doctors).

5) Data we'll ask for (minimal to start)

• • Arrivals by 15-min over 4–6 weeks; share of walk-ins vs. booked; no-show rate.

  • Service times (min, mean, 75th/90th/95th percentiles) for: vision exam, doctor exam (simple vs. complex), diagnostics; by doctor if possible.

  • Routing fractions: % needing dilation, diagnostics, procedures; % trauma.

  • Resource calendars: doctor/nurse availability, rooms/equipment counts.

  • Current policies: triage criteria, trauma handling, "overtaking" rules.

  • Outcomes: current %≤2h, LWBS/balking, patient complaints.

6) Clarifying questions for the client

Demand & mix

1. 1. What % of patients truly require dilation, and can any be pre-dilated safely by protocol?

   2. Distribution of simple vs. complex vs. trauma across the day? Are cataract/glaucoma largely pre-booked or walk-ins?

Times & resources
3) Observed time distributions (not just averages) for nurse vision exam, dilation-to-doctor lag beyond 30 min, doctor exam by case type, diagnostics.
4) Number of rooms/slit lamps and any shared equipment constraints.
5) Typical doctor availability patterns (OT days, rounds, camps) and same-day variance.

Policies & priorities
6) Current rules for trauma preemption-can trauma interrupt ongoing consults or is it handled by a designated doctor?
7) Are patients allowed to "hold" their place when they leave for labs/coffee? What is the formal policy vs. actual practice?

Scheduling
8) Share of booked vs. walk-in; appointment template granularity; overbooking currently used?
9) Show-up reliability by time of day/day of week; any patterned late arrivals (e.g., 10–11 a.m. peak due to transport)?

Information flow
10) Do you have EHR time-stamps today? If not, is scanning/QR feasible at each station?
11) Frequency and causes of missing case sheets; can we attach barcodes and define handoff checkpoints?

People & incentives
12) How do you measure doctor productivity now? Are you open to case-mix-adjusted dashboards and pooled queuing to reduce gaming?
13) Language mix of patients; access to interpreters or bilingual staff?

Constraints & goals
14) Acceptable overtime? Willingness to shift some volume to 8:45–9:00 pre-clinic or 13:00–13:15 spillover?
15) Primary success metric: 80th percentile ≤2h, 95th percentile ≤? Specific targets per clinic?

7) Recommended stack (practical)

• • Data capture & dashboards: Google Forms/QR → Google Sheets → Power BI/Tableau.

  • Queue analysis: Excel + Python/R notebooks for G/G/c and priority models.

  • Simulation: Start with SimPy (Python) for flexibility; if budget allows, Simul8 Healthcare for faster stakeholder buy-in.

  • Optimization: OR-Tools (open source) or Gurobi if available; deployment back to Excel templates for scheduling/rosters.

8) What success looks like (within one quarter)

• • Live wait-time board and daily huddles; staff using WIP caps and pooled queues.

  • Appointment template that flattens the 10–11 a.m. spike; buffer for trauma.

  • Case-mix-adjusted productivity view; less variability between doctors.

  • ≥80% ≤2 hours on >80% of clinic days, even when staffing drops to 3 doctors.

If you'd like, I can draft the initial Excel capacity calculator (arrival by 15-min × G/G/c wait estimator) and a SimPy skeleton that mirrors your dilation flow and priority classes, using placeholder distributions until we get your real data.

One More Time About a Synergy of AI and OR/MS

            I have recently attended a webinar organized by the NY Metro INFORMS Chapter on "Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier." https://us06web.zoom.us/webinar/register/WN_HI7YOf3YSaGPYyuC5w6TzQ#/registration

             The panelists aggressively promoted findings from their recent INFORMS Journal on Data Science paper

Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier

This paper revisits Herbert Simon's 1987 vision of a partnership between artificial intelligence (AI) and operations research/management science (OR/MS) to enhance decision-making. 

Two Heads Are Better than One: The Collaboration between AI and OR

I have mixed feelings about this webinar and its conclusions.

Therefore, I decided to test myself what value could be added by AI to OR/MS.

I used, as a test-bed, my consulting work that I had performed some time ago. This example is instructive because it illustrates a typical request for analysis written in plain language with some unstructured numbers by a healthcare manager who is an analytic layman. This is a rather typical example of a problem in a healthcare setting that requires, first, some interpretation and making some reasonable assumptions to perform the analysis using OR/MS.

I used three popular AI platforms (OpenAI GPT-4o Mini by OpenAI; Grok 3 Mini by xAI; and Free ChatGPT by OpenAI). I used, as input, the same consulting request text provided in the attached document.  The AI outcomes and recommendations are also presented in the attached document.

The bottom line:

·       In my opinion, the presenters of the webinar failed to present a business case or a specific example that would clearly demonstrate the AI value added to OR/MS approach.

·       Simon's vision, " we should aspire to increase the impact of MS/OR by incorporating the AI kit of tools that can be applied to ill-structured non-quantitative decision domains, " was and still remains a big beautiful dream.

·       There were no convincing demonstrations of the value added or additional insights to OR/MS methodology by any of the three tested AI platforms. In fact, the AI recommendations were to use OR/MS methods, such as Excel spreadsheets or simulation modeling. Such recommendations were trivial and generic.

This is the original consulting request:

"Center Manager-Wockhardt Eye Hospital. Location: Aurangabad, Maharashtra, India.

Dear Mr.Kolker, let me discuss my problem in detail.

I work in an ophthalmology hospital. The ideal situation I wish is: we treat 120 patients a day with 5 doctors from 9 am to 1 pm, 2 doctors would see a preliminary examination of the eye and dilate the patient for 30 minutes, whereas 3 doctors would start seeing the final examination after the dilation and will dispose the patient. We promise a delivery of 2 hours to 80% of patients who come for watering & irritation, normal checkups, headaches, glasses, and redness. The rest 20% would have cataract, glaucoma, and other complex disease which is bound to take more than 2 hours of investigation. We can monitor the waiting time of more than 2 hours in each clinic. But this 'ideal' situation hardly happens because one doctor would be in operation theatre, one doctor would be on leave, or in ward rounds or in camp, so I am left with only 3 doctors in the unit from 9 am to 1 pm.

           With 3 doctors, the bulk of patients arrive at 10 am (40 patients/hour) all these patients have to go for vision examination by nurses (5 min each patient) and 3 nurses would do it.

The challenges I have observed are:

* arrival of complicated cases, where a thorough examination of the posterior segment of the eye is needed (could take up to 30 min/ patient)

* patients who find it hard to place their chin in front of the slit lamp or language barrier, more inquisitive patients who keep loading the doctors with questions

* patients who would go for nature's call or coffee or to the lab for blood tests and will arrive to see their successors in the doctor's consultation room before them.

* trauma case that needs complete attention before shifting the patient to OT

* doctors who are purposefully slow to make sure that they see fewer patients than their colleagues

* missing case sheets by the nurse who has to spend all her time searching the case sheets.

Now, how would I monitor the patient waiting time in a clinic of 120 patients with all these parameters, to make sure patients are seen as early as possible rather than shouting, and arguing, thus disturbing the entire atmosphere of a peaceful work environment?

Can I work this situation with all the challenges in an Excel sheet or simulation software? How should I go about it? Kindly assist me."

OR/MS perspective

 (see section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3 )

            This problem description should first be converted into a more specific and formal description that would make it amenable to quantitative OR/MS analysis.

Below is my own problem summary that I made without any AI input:

- clinic is open 4 hours per day, from 9 am to 1 pm. The daily patient volume is 120.

- 80% of patients are regular ones (normal checkups, minor irritation, etc.). They are all first preliminary examined, then dilated, then finally examined and discharged.

- 20% of patients are complicated (cataract, glaucoma, etc.). They are first preliminary examined, and then proceed for final examination by a doctor skipping dilation.

- Preliminary examination takes 5 min, with the range 4-6 min.

- Dilation takes to work about 30 min with a range of 20 - 35 min.

- Dilated patients wait in a separate area until they are seen by the next available doctor for final examination.

-Final examination by the doctor for regular patients takes about 20 min with a range from 15 min to 30 min.

- Final examination by the doctor for complicated patients is in the range from 30 min (posterior segment of eye) to 120 min (2 hrs) (cataract, glaucoma) with the most likely being 1 hour (65 min).

- Staffing for base (ideal case) model: 2 doctors for preliminary examination and dilation, and 3 doctors for final examination. Total doctors' staffing: 5. No nurses.

- Staffing for the actual case is 3 nurses for preliminary examination and dilation and 3 doctors for final examination.

           Operations performance metrics target: 80% or more of regular patients wait less than 2 hours. Because of a lack of more specific information "the bulk of patients arriving at 10 am…" was interpreted as some patients (20) arriving from 9 to 10 am, while the remaining 100 (bulk) arrive at 10 am (actually spread from 10 am to 12:30 pm).  

           The question in the problem description document was "How would I monitor the patient waiting time in a clinic of 120 patients with all these parameters to make sure patients are seen as early as possible?"

            From the OR/MS standpoint, the question should have been interpreted as 'Given patient arrival pattern uncertainty, what staffing would be needed to meet the target operational performance?'

OR/MS Solution

            Obviously, an Excel spreadsheet would not be sufficient for analyzing such a dynamic problem. Discrete event simulation modeling is the methodology that should be used to determine the optimal staffing of doctors that meets the operational performance criteria. The first model would be a baseline one (an ideal case) that includes two preliminary exam doctors and three final exam doctors. Again, I suggested this approach without any AI help. As an OR/MS expert, I did not need any help. Details of a discrete event simulation model and simulation results were presented in section 3.6 in the book "Healthcare Management Engineering in Action", Springer, 2024.

https://link.springer.com/chapter/10.1007/978-3-031-53663-2_3

The main take-away was that the number of doctors (not nurses) was the bottleneck. Adding full and part-time doctors will elevate this bottleneck. The model suggested the required number of full- and part-time doctors.

           It was possible to simulate many other scenarios using this type of simulation modeling to find out the most satisfactory staffing level and shifts for meeting the operational performance targets. The following simulation scenarios were suggested: (i) the effect of additional part-time exam doctors scheduled for different shifts with various availability; (ii) restriction of the total daily patient volume; (iii) changing the percentage of regular and complicated patients; (iv) various patient arrival patterns; (v) extension of clinic hours or allowing overtime; (vi) or combinations of the above.

AI perspective.

I used the above plain language description as an input into several popular open AI platforms to test what value AI can add to OR/MS approach or improve or enhance it in any way.

The following AI platforms have been used:

            OpenAI GPT-4o Mini By OpenAI

GPT-4o Mini is OpenAI's most advanced model in the small models category. It is multimodal (accepting text or image inputs and outputting text), and has higher intelligence than gpt-3.5-turbo but is just as fast. It is meant to be used for smaller tasks, including vision tasks.

            The AI output was:

"Optimizing patient flow and monitoring waiting times in a busy ophthalmology clinic, particularly with the challenges you've described, requires a structured approach. Utilizing tools like Excel for tracking and managing data or simulation software for modeling can be effective strategies. Here's a comprehensive outline to help you navigate this issue:

1. Data Collection and Monitoring Setup:

Using Excel:

- Patient Tracking Sheet:

Create a log where you input patient arrival times, initial examination start, dilation times, and final exam times.

- Use columns for patient ID, arrival time, start time for prelim exams, dilation duration, start time for final exams, and total waiting time.

- Categorization:

- Separate patients based on their complexity (routine vs. complicated cases) in different sheets/tabs.

- Assign a complexity code (e.g., 1 for simple and 2 for complex) to each patient for sorting and analysis.

 - Nurse and Doctor Schedules:

create a schedule that indicates the availability of each doctor and nurse. Make a note of any planned leaves or availability that may affect patient processing.

- Incorporating Break Times/Disruptions:

- Maintain a log for each doctor to note any disruptions (emergency cases, breaks, etc.) to identify patterns affecting wait times.

2. Data Analysis:

- Calculate Waiting Times:

- Analyze the sheet daily or weekly to calculate the average waiting time, broken down by patient complexity.

- Identify instances where patients exceed the 2-hour waiting window.

- Bottleneck Analysis:

- Identify common bottlenecks (e.g., wait time at vision examination, delays due to complications) and their causes.

- Performance Tracking:

- Track the performance of each doctor and each shift. Analyze if some doctors are consistently slower than their colleagues and identify reasons.

3. Optimizing Patient Flow:

Suggestion Batching:

- Establish a triage system upon patient arrival:

- *Fast-Track Routine Patients:* Identify lower complexity patients who can quickly proceed with exams.

- *Complex Cases Protocol:* Set aside specific days or sessions for complex cases to reduce their impact during peak hours.

Improvement Measures:

- Patient Education:

- Have educational materials ready for patients (pamphlets, videos) to reduce questions and prepare them for examinations.

- Checking Mechanism:

- Ensure nurses check all case sheets and necessary forms are ready before patients are called to see doctors.

- Queue Management:

- Use a digital queue management system (or even a physical digital board) to manage patient queues.

4. Simulation Software:

Using simulation software can help model various scenarios, predict patient flow, and identify optimal staffing for different times.

1. Creating a Simulation Model:

- Input your clinic's workflow, including patient arrival rates, doctor availability, exam times, etc.

- Simulate different scenarios (varying the number of doctors, adding a patient education session, etc.) to see impacts on waiting times and patient satisfaction.

Grok 3 Mini

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The output was:

"I understand your situation in the ophthalmology hospital-it sounds frustrating to manage patient flow with so many variables and disruptions, especially when you're short-staffed and dealing with unpredictable challenges like complicated cases, patient behaviors, and administrative issues. The good news is that you can absolutely monitor patient waiting times effectively, even with all these parameters, by using tools like Microsoft Excel for basic tracking and analysis, or simulation software for more advanced modeling. This will help you identify bottlenecks, optimize workflows, and maintain a peaceful environment without resorting to shouting or arguments.

I'll break this down step by step: first, I'll explain how to approach the monitoring process, then discuss using Excel as a starting point, and finally, suggest when and how to use simulation software. My goal is to help you create a system that's practical, data-driven, and scalable.

Step 1: Define Your Monitoring Goals and Process

Before diving into tools, let's outline a clear plan. You want to track waiting times for 120 patients per day (from 9 am to 1 pm) while accounting for the challenges you mentioned. Here's how to structure this:

- Key Metrics to Track:

- Arrival Time: When the patient arrives (e.g., many at 10 am in bulk).

- Preliminary Exam Time: Time for vision exam by nurses (5 minutes each) and dilation (30 minutes total per patient).

- Final Exam Time: Time with doctors for simple cases (aim for under 2 hours total) vs. complex cases (over 2 hours).

- Waiting Time: Total time from arrival to start of preliminary exam, plus time from preliminary to final exam.

- Total Turnaround Time: From arrival to departure, including any delays.

- Delays and Challenges: Note reasons for delays (e.g., complicated cases, patient interruptions, missing case sheets, doctor absences).

- Bottlenecks: Track how factors like doctor availability (e.g., only 3 doctors), nurse workload, or interruptions affect flow.

- Target Outcomes:

- Ensure 80% of simple cases (watering & irritation, checkups, etc.) are seen within 2 hours.

- Monitor the 20% complex cases (e.g., cataract, glaucoma) separately, as they exceed 2 hours.

- Use data to forecast staffing needs, reduce disruptions, and improve patient satisfaction.

- Data Collection Process:

- Who Collects Data? Assign a dedicated staff member (e.g., a nurse or admin) to log data in real-time using a simple form or app. This could be done on paper initially and transferred to Excel, or directly into a digital tool.

- How Often? Track data for every patient daily, then analyze it weekly or monthly to spot patterns.

- Handle Challenges in Tracking:

- For complicated cases (e.g., 30+ min exams), add a field to note the extra time.

- For disruptions (e.g., patient delays for coffee or trauma cases), log them as "interruption events" with timestamps.

- To address doctor variability (e.g., slow doctors), track individual doctor consultation times anonymously to identify trends without blame.

- For missing case sheets, integrate a quick check-in system to ensure files are ready.

By focusing on these, you'll create a "feedback loop": collect data, analyze it, and use insights to adjust schedules (e.g., stagger patient arrivals or allocate more time for complex cases).

Step 2: Using Excel for Monitoring

Excel is an excellent, accessible tool to start with-it's free (if you have Microsoft Office), easy to learn, and powerful enough to handle your scenario without needing advanced software. You can use it to log data, calculate wait times, and visualize trends. Here's how to set it up:

A. Set Up Your Excel Spreadsheet

Create a template like this (I'll describe it; you can replicate it in Excel):

1. Columns for Patient Data:

- Patient ID: A unique number for each patient (e.g., auto-generated).

- Arrival Time: Timestamp (e.g., 10:00 AM).

- Patient Type: Categorize as "Simple (80%)" or "Complex (20%)" based on initial assessment.

- Preliminary Exam Start Time: When the nurse starts (e.g., after vision check and dilation).

- Preliminary Exam End Time: Approximately 30 minutes later.

- Final Exam Start Time: When the doctor begins.

- Final Exam End Time: When the doctor finishes.

- Departure Time: When the patient leaves.

- Delay Reasons: A dropdown or text field for notes (e.g., "Complicated case - posterior exam," "Patient interruption," "Doctor in OT," "Missing case sheet").

- Total Wait Time: Calculated field (e.g., =Final Exam Start Time - Arrival Time).

- Total Turnaround Time: Calculated field (e.g., =Departure Time - Arrival Time).

Example of a basic row:

| Patient ID | Arrival Time | Patient Type | Prelim Start | Prelim End | Final Start | Final End | Departure Time | Delay Reasons | Total Wait Time | Total Turnaround |

|------------|--------------|--------------|--------------|------------|-------------|-----------|----------------|---------------|-----------------|------------------|

| 001 | 10:00 AM | Simple | 10:05 AM | 10:35 AM | 11:00 AM | 11:30 AM | 11:45 AM | None | 1 hour | 1 hour 45 min |

2. Formulas for Calculations:

- Use Excel's time functions to automate calculations:

- Total Wait Time: =TEXT(Final Start - Arrival Time, "h:mm") (subtracts times and formats as hours:minutes).

- Total Turnaround Time: =TEXT(Departure Time - Arrival Time, "h:mm").

- Conditional Formatting: Highlight cells where Total Turnaround Time > 2 hours in red, so you can quickly spot issues.

- Averages: Add a summary section at the bottom with formulas like =AVERAGE(Total Wait Time column) to calculate daily averages.

3. Incorporate Challenges into the Sheet:

- Variable Patient Arrival: Use a separate tab to log arrival patterns (e.g., 40 patients at 10 am). Then, use graphs to show peaks.

- Complicated Cases: Add a column for "Exam Duration Override" where you manually enter extra time (e.g., +30 min for posterior exams).

- Interruptions: Create a dropdown list in the Delay Reasons column (e.g., options: "Patient Delay," "Trauma Case," "Doctor Absence"). This helps quantify how often these occur.

- Doctor and Nurse Workload: Track which doctor/nurse handled each patient and how long it took. Use pivot tables to analyze if one doctor is consistently slower.

- Bulk Data Entry: If you have many patients, use Excel's data validation for quick entry (e.g., dropdowns for Patient Type).

B. Daily Workflow with Excel

- Morning Prep: Open the Excel file at 9 am. Have staff enter data as patients are processed.

- Real-Time Monitoring: Use a shared Excel file (via OneDrive or Google Sheets for collaboration) so you can check wait times on the fly. If a patient is approaching 2 hours, alert the team.

- End-of-Day Analysis: After 1 pm, review the data. For example:

- Pie chart for patient types (80% simple vs. 20% complex).

- Line graph for wait times over the day to see spikes at 10 am.

- This will help you answer questions like: "How many patients waited over 2 hours today, and why?"

C. Limitations and Tips for Excel

- Pros: It's simple, cost-effective, and you can start immediately.

- Cons: It doesn't handle randomness well (e.g., simulating "what if a doctor is absent?"), so it's better for tracking real data than predicting scenarios.

- Tips:

- Train your team on basic Excel use (e.g., 1-hour session).

- Back up data daily to avoid loss.

- If you have 120 patients, use Excel's filters and sorting to manage the volume.

With Excel, you can monitor your current situation effectively and start making improvements, like adjusting nurse assignments based on arrival peaks.

Step 3: When and How to Use Simulation Software

If Excel feels insufficient (e.g., you want to simulate "what if" scenarios with your challenges), move to simulation software. This is ideal for modeling complex systems like yours, where variables (e.g., doctor absences, patient interruptions) create uncertainty.

Why Use Simulation Software?

- It allows you to create a virtual model of your clinic, incorporating all your challenges to predict wait times and test solutions.

- For example, you could simulate: "If we have only 3 doctors and 40 patients arrive at 10 am, how does that affect wait times?" Or, "What if we add a buffer for complicated cases?"

Recommended Software:

- Free/Entry-Level Options:

- Excel's own simulation tools (e.g., using Monte Carlo add-ins) for simple scenarios.

- AnyLogic Personal Learning Edition (free for basic use) or Simul8 Community Edition-these are user-friendly for healthcare simulations.

- Paid Options: Arena or ExtendSim if your hospital can invest (they're more powerful but might require training).

How to Set It Up:

1. Choose Software: Start with AnyLogic-it's intuitive for beginners.

2. Model Your Process:

- Input Parameters: Define variables based on your description:

- Patient arrival: Bulk at 10 am (40/hour), with 80% simple and 20% complex.

- Resources: 3 doctors, 3 nurses; each preliminary exam takes 5-30 minutes; final exam varies (e.g., 30 minutes for simple, up to 60+ for complex).

- Challenges: Add random events, like a 10% chance of a trauma case interrupting, or a 20% chance of patient delays.

- Simulation Logic: Create a flowchart:

- Step 1: Patient arrives → Goes to nurse for preliminary exam.

- Step 2: If dilated, queues for doctor.

- Step 3: Doctor examines; add delays for interruptions.

- Output: Simulated wait times for 120 patients.

3. Run Scenarios:

- Baseline: Simulate your current setup.

- "What If?": Test changes, like hiring a temp doctor or staggering arrivals.

4. Analyze Results: The software will output reports, e.g., average wait times, probability of meeting the 2-hour promise.

5. Integration with Excel: Use Excel to import simulation results for further analysis.

Getting Started with Simulation:

- Learning Curve: If you're new, start with online tutorials (e.g., YouTube for AnyLogic). It might take a few days to build a basic model.

- Cost and Time: If your hospital can't afford this, stick with Excel for now and scale up later.

- When to Switch: If you notice patterns in Excel data that need deeper analysis (e.g., frequent delays from complicated cases), simulation will help.

Final Thoughts

You can definitely manage this with Excel as your primary tool-it's straightforward and will give you immediate insights into waiting times while accounting for your challenges. Start by creating the spreadsheet I outlined, collect data for a week, and review it to see improvements. If things get more complex, transition to simulation software for predictive modeling.

This approach will help you foster a more efficient, less chaotic environment by focusing on data rather than reactions. If you share more details (e.g., your current workflow or specific Excel questions), I can refine this further. Good luck-I'm here to assist!"

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The output was :

"To effectively monitor patient waiting times in your ophthalmology clinic, especially given the complexities and challenges you've outlined, a systematic approach is important. While Excel can provide a relatively simple way to track and analyze data, a simulation software tool might be better suited for modeling the dynamic and complex nature of your clinic. Here's a step-by-step guide for both approaches:

Option 1: Using Excel

1. Data Collection:

- Gather data on patient arrivals, the type of cases, time spent on examinations, and outcomes. You will need to track:

- Arrival times

- Time taken by nurses for preliminary examination

- Time taken by doctors for final examination

- Types of cases (routine or complex)

- Any delays or disruptions (e.g., nature's call, language barriers)

2. Create a Tracking Template:

- Design an Excel sheet with columns such as:

- Patient ID

- Arrival Time

- Preliminary Examination Start Time

- Preliminary Examination End Time

- Doctor Consultation Start Time

- Doctor Consultation End Time

- Type of Case

- Total Waiting Time

- Notes (for delays or other disruptions)

3. Formulas:

- Use formulas to calculate waiting times. For instance:

- Total Waiting Time = (Doctor Consultation End Time - Arrival Time)

4. Data Analysis:

- Use pivot tables or graphs to analyze waiting times based on different parameters (e.g., types of cases, doctor efficiency).

- Highlight any trends, such as peak times leading to delays or specific issues causing longer waiting times.

5. Regular Reviews:

- Set a schedule to review the data regularly, and adjust workflows or resource allocation based on findings.

Option 2: Using Simulation Software

1. Choose a Simulation Tool:

- Select software that specializes in healthcare operations, such as AnyLogic, Simul8, or Arena, which can model patient flow.

2. Model Your Clinic:

- Create a model that includes:

- The flow of patients from arrival to consultation.

- Time distributions for examinations (e.g., 5 minutes for preliminary, 15-30 minutes for final depending on complexity).

- Random arrivals based on historical data.

- Include factors for delays, such as patients taking breaks, language barriers, and others.

3. Input Data:

- Input your collected data (arrival rates, processing times, etc.) into the simulation model.

4. Run Simulations:

- Simulate various scenarios to observe how changes in staffing levels, patient arrival patterns, and case complexities impact waiting times.

- Explore "what-if" scenarios, such as what happens when one doctor is unavailable or if more nurses are added for preliminary exams.

5. Output Analysis:

- Analyze the results from the simulation to identify bottlenecks and areas for improvement.

- Use the insights gained to inform changes in scheduling, staffing, and processes.

General Recommendations

1. Standard Operating Procedures (SOPs):

- Standardize processes for routine examinations and consultations to minimize variations caused by different doctors.

2. Patient Education:

- Provide clear instructions to patients before their appointment regarding what to expect, helping them to be prepared (e.g., time for blood tests or what to do with delays).

3. Communication:

- Implement a system for keeping patients informed about expected waiting times and managing their expectations.

4. Resource Allocation:

- Consider hiring additional staff during peak hours or cross-training existing staff to fill in as needed.

5. Feedback Mechanism:

- Create a system for collecting patient feedback on waiting times and experiences to continuously improve the process.

By utilizing either Excel for basic tracking or a more advanced simulation for in-depth analysis, you can gain valuable insights into your clinic's operations, ultimately leading to improved patient satisfaction and efficient use of resources."

------------------------------
Alexander Kolker
ge healthcare
MILWAUKEE WI
------------------------------

Sorry, a copy/paste error.  The link is https://pubsonline.informs.org/do/10.1287/orms.2024.02.03/full/

You wrote:

"With regard to AI, I asked myself: "Do I really need an assistant for solving the specific problem posed to me, be that assistant AI or a human?"

I think the more appropriate question is "Would ideas from an assistant for solving the specific problem posed to me be useful, be that assistant AI or a human?"  

At least in my experience, collaborating with colleagues on solutions for specific problems is quite valuable, and an AI assistant becomes just another collaborator providing ideas that I nor my other collaborators might have thought of.

Irv,

Thank you for your feedback.

1st, the link that you suggested https://pubsonline.informs.org/do/10.1287/orms.20

does not work, returning 'An Error has occurred, please contact your System Administrator.' With no exact reference to the authors, I am unable to read this paper.

2nd, you write "Warren is suggesting that a GenAI/LLM can assist you in determining that you have made the right decisions in how you approached your customer's problem.  Here, the AI is not the problem solver.  But it can be a handy assistant."

But I made clear in my post, "With regard to AI, I asked myself: "Do I really need an assistant for solving the specific problem posed to me, be that assistant AI or a human?"  And my answer is resounding no."

Certainly, everybody (even the most experienced consultant) can make a mistake in choosing the best approach to solving the client's problem.  However, what makes you think that AI will better "help guide a solution to a proposed analytics problem. Both this article and Warren are suggesting that leveraging this technology can help us as OR practitioners."?? Why do I have to trust AI more than my own judgment and experience? How do we know, as OR practitioners, that AI technology will truly "enhance the client experience'??

I see the whole issue from a different angle: if someone, be it AI or a human, suggests something, I will have to spend my time verifying the validity and value of this suggestion. After all, I am responsible for the outcome of my work, not AI nor anyone else. I

Thanks again

Alexander:
I admit that I have my own reservations about using LLMs and GenAI, and that care must be taken when using this new technology.   You wrote:

In my consulting capacity, my goal is to deliver to the customer the specific solution for the posed problem and to convince the customer that I chose the most efficient and direct methodology for doing so.

I think that Warren is suggesting that a GenAI/LLM can assist you in determining that you have made the right decisions in how you approached your customer's problem.  Here, the AI is not the problem solver.  But it can be a handy assistant.

You might want to take a look at this article from OR/MS Today:  https://pubsonline.informs.org/do/10.1287/orms.20 written over a year ago about an experiment done by a workgroup that used ChatGPT to help guide a solution to a proposed analytics problem. Both this article and Warren are suggesting that leveraging this technology can help us as OR practitioners.

As a practitioner, I am on the lookout for applications of GenAI/LLMs that truly deliver value and that justify the investment of time and money. One way is by making our own work easier, which is the thing you have experimented with, and is illustrated in the aforementioned article.  But another is how we can use the technology in the solutions that we deliver to clients so that we enhance the client experience.  One such example is described nicely in this INFORMS MSOM webinar from people at Microsoft:  https://www.informs.org/Resource-Center/Video-Library/Videos-for-Topic-Pages/M-SOM-Practice-Platform-Using-Optimization-and-LLMs-to-Enhance-Cloud-Supply-Chain-Operations .  I believe we'll see additional examples like this in the future.

Warren, I appreciate your taking the time to write your detailed response and suggestions to my post regarding the role of AI for possible synergy with OR/MS.

I think the key to our different assessment of the AI role as an enabler and value-added component to OR/MS is in your phrase: "As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS." Of course, after all, you are the Founder, OptiML AI.

Now, as you can guess, I have an open mind about the value AI, but at this time I am highly skeptical of your belief. But the issue is not someone's personal 'belief' or skepticism. In my consulting capacity, my goal is to deliver to the customer the specific solution for the posed problem and to convince the customer that I chose the most efficient and direct methodology for doing so. With regard to AI, I asked myself: "Do I really need an assistant for solving the specific problem posed to me, be that assistant AI or a human?"  And my answer is resounding no.

      You suggested to me "two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built in." Your suggestions completely changed my and the customer's goals and the subject: instead of focusing on the solution of the customer's business problem, you suggested testing some internal AI issues, such as different prompts, the role that AI should assume, and testing better LLMs. However, I am not interested in spending more of my time exploring the role and the potential of AI with little or no chance of getting additional value. I am interested in solving the customer's business problem using the most appropriate methodology, as I judge it. This difference in focus illustrates the core of the "AI rage" issue: a lot of resources are already spent on AI (LLMs) development. AI promoters, marketers, and investors are desperate to demonstrate 'amazing' applications and the usefulness of this "big beautiful thing". This is the reason why AI is claimed to be an almost universal silver bullet that can assist and significantly enhance human ability for solving difficult problems. AI could potentially be useful in solving ill-defined problems with contradictory multiple goals, non-quantifiable or difficult to formalize factors expressed in natural language, generating new alternative hypotheses, etc. But I am not familiar with examples of such AI applications.  

               Your GPT-5 output included in your reply is a useless word salad that includes everything, from generic JTA to Six Sigma DMAC, from queuing & capacity planning to the list of software with pros & cons (and who is supposed to choose pros & cons-another AI?). From DES to Process Reliability with Lean tools (5S, Kanban, Control charts, FMEA, etc) that are totally irrelevant in this context. And so on and on, page after page that makes an illusion of the deep expertise and value, but actually is a hollow babbling close to hallucination.

Anyway, thank you for the opportunity for discussion.

Alexander,

I sincerely appreciate that you tested a few LLMs to see what kind of assistance they would have provided you in your scenario. That's a great way to learn what works well and what doesn't. The results of your three tests didn't satisfy you that it could help. Might I suggest two things: (1) experiment with different prompts that give some guidance as to the role that the AI should assume and what it should focus on, and (2) use more recent (and better) LLMs, especially ones that have reasoning built-in. Also, since you're the expert in the particular area that the client is asking about, maybe a better metric of success for the LLM is not to compare it to what you would have done but what someone with 5-10 years experience in OR/MS would have done. If you had passed this request off to a colleague, what would you have expected to get in return? 

For example, if I prepended this to your client's request: "You are an operations research expert with experience in healthcare. You have read the book "Healthcare Management Engineering in Action" from Springer (2024). You know the way to approach a problem with a fresh perspective and be able to use the INFORMS (informs.org) approach to job task analysis (JTA) and end-to-end solution methodology. Here is a request from a potential client. Outline ways of potentially helping this client, identifying algorithms and software needed. For each, give detailed pros and cons of the approach. Then list clarifying questions that we need to ask the client." and then added the request, submitted it to OpenAI's GPT-5 with 'Thinking' turned on, it gives a different and more detailed answer. I'll post it below - you will need to determine whether or not this is better/worse/same since I'm not experienced in this particular area, but I do think this is a great start that took only a few minutes to get some areas to pursue. As you can guess, I'm of the firm belief that AI can greatly assist in many areas, including OR/MS.

Here is the GPT-5 output (you can also test on the pro models from X, Anthropic, etc.):

Great brief. Below is a pragmatic, OR-driven plan you can take to leadership. I'll frame it using an INFORMS-style JTA and end-to-end methodology, then outline several complementary solution tracks-with algorithms, software, and concrete pros/cons-followed by the clarifying questions we'll need to ask.

1) Problem framing (JTA + end-to-end method)

Job Task Analysis (JTA) view

• • Purpose: Deliver outpatient ophthalmology consults with an 80% ≤2-hour turnaround, despite variable doctor availability and case complexity.

  • Tasks & resources: Registration → vision exam (nurses) → dilation (30 min for most) → doctor exam → diagnostics (subset) → disposition; resources: 3–5 doctors, 3 nurses, rooms/equipment, paper case sheets.

  • Workflows: Two-stage clinical flow with a fixed delay (dilation), plus branching for complex cases and trauma (priority class).

  • Performance targets: 120 pts/day in 4 clinic hours (9:00–13:00); 80% ≤2h TAT, 20% allowed >2h; minimize >2h waits by clinic; maintain smooth, calm environment.

Method overview (INFORMS/DMAIC)

1. 1. Define: Map the current process (SIPOC + value stream map), define KPIs (TAT, wait time per stage, LWBS/balking, %>2h).

   2. Measure: Time-stamp each handoff; capture arrival pattern (esp. 10–11 a.m. surge), service-time distributions by case type/doctor, routing probabilities, no-shows/walk-aways.

   3. Analyze: Queueing diagnostics; bottleneck identification; variability analysis; capacity vs. demand by 15-min interval.

   4. Improve/Design: Redesign appointment/triage, staffing rosters, pooling rules, priority logic, buffer sizes; add visual controls; 5S for case sheets.

   5. Control/Sustain: Real-time dashboard, escalation rules, control charts; policy for trauma surges and low-staff days; continuous simulation A/B tests for schedule tweaks.

2) Solution tracks (algorithms + software, with pros/cons)

A) Queueing & capacity planning (diagnose the bottlenecks)

What: Model the system as tandem queues: Nurse vision exam → fixed 30-min dilation delay → Doctor exam; with priority classes (trauma high, complex medium, simple low), time-varying arrivals, and server calendars (doctors on leave/OT/rounds).

Algorithms

• • Time-varying Poisson (TVP) arrivals; G/G/c approximations (e.g., square-root-staffing, Allen-Cunneen) for waits.

  • Priority queue formulas (non-preemptive for complex; preemptive for trauma if policy allows).

  • Pooling vs. dedicated servers analysis; Little's Law to sanity-check WIP and TAT.

  • Variability impact (SCV) analysis; bottleneck identification (VUT/Kingman).

Software

• • Excel with add-ins or templates for M/M/c / G/G/c approximations.

  • R (queueing, tidyverse) or Python (NumPy/Pandas + custom calcs).

  • Visualization with Power BI / Tableau.

Pros

• • Fast, low cost, immediate insights.

  • Good for "why is 10–11 a.m. painful?" and staffing what-ifs.

  • Validates feasibility of 120 pts/4 hours with 3 vs. 5 doctors.

Cons

• • Approximations; hard to capture rework, wandering patients, room constraints, and case-sheet losses.

  • Doesn't show minute-by-minute congestion as realistically as simulation.

B) Appointment and arrival smoothing (change the input)

What: Redesign appointment templates and walk-in triage to flatten the 10 a.m. surge; pre-dilation for qualifying patients; stagger doctor start times; buffer slots for trauma/complex.

Algorithms

• • Stochastic appointment scheduling with no-shows and dilation lag; objective: minimize mean/95th-percentile TAT.

  • Overbooking optimization by time block (chance-constrained).

  • Slot mix optimization (simple/complex ratios by 15-min block).

Software

• • Python OR-Tools / PuLP (open-source LP/MIP), or Gurobi/CPLEX if available.

  • Export to Excel scheduling templates for daily use.

Pros

• • Biggest lever: fewer giant peaks → shorter queues everywhere.

  • Works even when only 3 doctors are present.

Cons

• • Requires culture change and booking discipline.

  • Needs reliable complexity triage at scheduling/arrival.

C) Real-time flow control & digital queuing

What: Token-based single queues per stage (avoid "choose your doctor" micro-queues), real-time status boards, SMS recalls for patients who leave for tests/coffee, fixed WIP caps at each step.

Algorithms

• • Dispatch rules: Shortest-processing-time-first for simple pool; triage priority for trauma/complex; pull system from doctor stage to start pre-dilation only when downstream WIP is below cap.

  • Workload leveling (Heijunka) logic.

Software

• • Lightweight kiosk/QR time-stamping + Google Apps Script/Power Apps; or off-the-shelf queue apps.

  • Real-time dashboards in Power BI/Tableau.

Pros

• • Immediate wait-time visibility; fewer arguments at the desk.

  • Reduces "overtaking" by wanderers; controlled WIP lowers TAT variability.

Cons

• • Needs basic IT support and staff training.

  • SMS/WhatsApp workflows must match local patient behavior/languages.

D) Discrete-Event Simulation (DES) of the clinic

What: Build a minute-level simulator of your clinic with actual distributions, calendars, and routing (including dilation, complex cases up to 30 min, trauma preemptions, nurse/room constraints, case-sheet mishaps).

Algorithms

• • DES engine with entity routing, resource seize/release, calendars, priorities, balking/reneging.

  • Simulation-optimization (search over appointment templates, staffing, WIP caps).

Software (choose one)

• • Simul8 Healthcare, AnyLogic, Arena (paid; healthcare libraries, GUIs).

  • SimPy (Python) or simmer (R) (open-source; code-based, very flexible).

  • JaamSim (free GUI) if budget-constrained.

Pros

• • Captures all your "messy realities": wanderers, missing sheets, language delays, doctor-specific speed, trauma preemptions.

  • Lets you test policies safely before rollout; produces persuasive visuals.

Cons

• • Needs clean input data and modeler time.

  • Governance required to keep it updated (avoid "one-off model" trap).

E) Staff rostering and day-of reallocation

What: Build schedules that anticipate OT/leave/camps; cross-train/float roles; day-of robust reallocation when doctors drop from 5 → 3.

Algorithms

• • Integer programming for monthly/weekly rosters with coverage constraints and fairness.

  • Robust optimization or stochastic programming to hedge against absences.

  • Real-time rebalancing heuristic using current WIP + forecast arrivals.

Software

• • OR-Tools CP-SAT, Gurobi/CPLEX; front-end in Excel or a small web app.

Pros

• • Converts variability in staffing into planned buffers.

  • Fairness rules reduce "purposefully slow" behavior incentives.

Cons

• • Requires accurate availability inputs and policy alignment.

  • Optimization outputs must be interpretable and editable by managers.

F) Process reliability: case sheets, 5S, and language support

What: Reduce "special-cause" delays (lost sheets, difficult positioning, language).

Algorithms/tools

• • 5S/visual management for physical files; Kanban for sheet movement.

  • Standard work times with control charts (X-MR) on vision and exam durations.

  • Pareto analysis for delay causes; FMEA for failure modes (lost sheets, device downtime).

  • Interpreter routing (rules-based) and pictorial instruction cards for slit-lamp positioning.

Software

• • Excel/Power BI control charts; lightweight barcode or RFID for case-sheet tracking.

  • Process mining (if EHR timestamps exist) to spot rework loops.

Pros

• • Cheap, immediate waste removal; directly tackles stated pain points.

  • Stabilizes inputs for the DES and scheduling models.

Cons

• • Lean changes require daily management; benefits erode without audits.

G) Performance transparency & incentives (behavioral)

What: Case-mix-adjusted productivity and fairness so no one "wins" by going slow.

Algorithms

• • Multilevel regression to adjust throughput for case complexity and patient mix.

  • Risk-adjusted control charts per doctor; CUSUM to detect sustained drift.

  • Equitable load balancing rules in the dispatcher (pooled queue).

Software

• • R/Python analytics; publish in Power BI/Tableau.

Pros

• • Reduces gaming; focuses debate on data, not personalities.

  • Supports coaching and targeted training.

Cons

• • Needs trust and careful rollout; ensure measures don't encourage cutting corners.