AI Prompt

ASC OR Capacity & Scheduling Optimization Model

OR capacity and scheduling optimization model using facility constraints, conversion rates, referral flow, internal referrals, and cancellation rates to determine true weekly throughput under 1–2 OR scenarios.

You are a healthcare operations analyst building a surgical capacity and scheduling model using facility constraints, referral data, conversion rates, and operational performance metrics.

Objective:

Determine realistic OR throughput, required clinic volume, and scheduling capacity under bay, staffing, and time constraints. Clearly distinguish between current-state capacity and future-state expansion scenarios.

## Facility constraints

Use the following operating parameters:

* Two OR rooms are available, with one OR currently in use.
* Operating schedule: 4–5 days per week, with Friday optional.
* Six patient bays available per day, except Wednesday, which has four bays.
* Each patient occupies a bay for the full care cycle:

* 90 minutes pre-op.
* OR time.
* Recovery time.
* Average OR time: 44 minutes per case, including turnover.
* Recovery time: 27 minutes.
* Operating hours: 480 minutes per day.

## Historical baseline (current 1 OR model)

Use the following historical weekly case volumes:

* Monday: 7 cases (OBL).
* Tuesday: 14 cases (OBL).
* Wednesday: 9 cases (ASC).
* Thursday: 14 cases (ASC).
* Friday: 7 cases (OBL follow-up only).

## Capacity rules

Do not estimate two-OR capacity using simple doubling.

Use the bay constraint formula:

For six-bay operating days:

Capacity = FLOOR((6 × 480) ÷ (90 + OR time + recovery time))

For four-bay operating days:

Capacity = FLOOR((4 × 480) ÷ (90 + OR time + recovery time))

Calculate:

* Maximum cases per operating day.
* Weekly capacity for one OR.
* Weekly capacity for two ORs.
* Four-day operating scenario.
* Five-day operating scenario.

## Conversion inputs

Use the following conversion metrics:

* Order-to-OR conversion: 78.7%.
* New Patient visit-to-order conversion: 34.1%.
* Follow-Up visit-to-order conversion: 10.6%.
* Follow-Up conversion scenario toggle: model improvement up to 30%.
* Referral-to-New Patient conversion: 70.3%.
* Staging multiplier: 1.29x.

Current operational inputs:

* Current OR orders per week: 20.
* Follow-Up visits per week: 77.

## Referral data

Analyze monthly referral data from January through March 2026, including:

* Monthly referrals.
* Monthly scheduled volume.
* Non-referral sources: approximately 36 per month.
* Referral share: approximately 74%.

Analyze internal referrals, including:

* Monthly internal referral order rates.
* Monthly internal referral scheduling rates.
* Category mix, including:

* Pain.
* Interventional Radiology (IR).
* Cardiology.
* Vein.
* Other categories.

Evaluate the impact of internal referral volume on the staging multiplier, including a potential increase from 1.35x to 1.40x.

## Cancellation and no-show analysis

Analyze:

* OBL/OR cancellation and no-show rates.
* New Patient cancellation and no-show rates.
* Follow-Up cancellation and no-show rates.
* Monthly trends from January through March 2026.

Compare against targets:

* OBL: 8%.
* New Patient: 10%.
* Follow-Up: 5%.

## Model outputs

Generate the following sections:

## Section 1: OR Capacity

Provide:

* One OR versus two OR weekly capacity.
* Four-day versus five-day operating scenarios.
* Bay-constrained maximum cases per day.
* Weekly case capacity.
* Comparison of current utilization versus available capacity.

## Section 2: Demand Cascade

Calculate the required upstream demand needed to support OR volume.

Show:

* Required OR cases.
* Required orders.
* Required clinic visits.

Separate contribution from:

* New Patient visits.
* Follow-Up visits.

Output:

* Total required clinic visits per week.
* Clinic volume required to support projected OR demand.

## Section 3: Referral Requirements

Calculate:

* Monthly referral demand required.
* Required referral volume to support OR capacity.
* Gap between required and current referral volume.
* Impact of referral conversion changes on demand requirements.

## Section 4: Internal Referral Impact

Evaluate:

* Contribution of internal referral pipelines.
* Impact of internal referral growth on staging multiplier.
* Effect on OR demand and scheduling requirements.
* Potential improvement opportunities by referral category.

## Section 5: Capacity vs. Demand Gap

Compare:

* Current OR volume versus required OR volume.
* Current clinic volume versus required clinic volume.
* Available capacity versus projected demand.

Identify the primary bottleneck:

* OR availability.
* Bay capacity.
* Referral volume.
* Clinic conversion.
* Order conversion.
* Cancellation/no-show rates.

## Section 6: Scheduling and Cancellation Impact

Evaluate:

* Actual scheduled volume compared with required scheduled volume.
* Additional scheduling needed after applying cancellation and no-show rates.
* Operational risk by service line.

Rules:

* Use formulas and provided inputs only. Do not introduce unsupported assumptions.
* Clearly separate one-OR and two-OR scenarios.
* Highlight limiting constraints, including bay capacity, referral volume, conversion rates, and cancellation rates.
* Keep outputs structured, quantitative, and decision-oriented.