Big data simulations for capacity improvement in a general ophthalmology clinic

Area of Science:

• Healthcare Operations Research
• Clinical Informatics
• Health Services Research

Background:

• Long total waiting times (TWT) negatively impact patient satisfaction in clinic visits.
• Optimizing patient scheduling is crucial for efficient healthcare delivery.
• Previous methods for reducing TWT have had limited success.

Purpose of the Study:

• To utilize big data simulations to model patient scheduling and its effect on TWT.
• To implement and verify changes to a clinic's scheduling calendar based on simulation results.
• To reduce TWT in a general ophthalmology clinic setting.

Main Methods:

• A retrospective simulation study using a discrete event simulation (DES) model.
• Data from 4,401 clinic visits were exported from the clinical warehouse.
• Various patient scheduling models were simulated to identify the most effective for TWT reduction.

Main Results:

• The DES model showed high agreement with real-world TWT data (225 ± 112 min vs. 229 ± 100 min).
• Implementation of a new calendar with block intervals and extended time windows reduced simulated TWT to 153 min.
• In clinical practice, TWT decreased from 229 ± 100 min to 183 ± 89 min.

Conclusions:

• Big data simulation enabled a cost-neutral 21% reduction in mean TWT.
• Simulation allows for evaluating system changes before real-world implementation.
• This approach offers a cost-effective method for improving patient flow and reducing clinic capacity loads.