Gambling on Patient Flow? Using Monte Carlo Simulation to Model Delays
Patients hate to wait. We hate for patients to wait. So why are lengthy wait times so common? The answer has many aspects to it - one of which is the provider's master schedule template.
We will show how patient flow study data was captured used in conjunction with Monte Carlo simulation to demonstrate the impact of ad hoc schedule changes.
During the Fall of 2008, detailed patient flow data was collected over a period of three months at Emory Healthcare's Orthopeadic and Spine Center in Atlanta, Ga. This data was entered into a MS Access relational database. The data painted a picture of variable, and sometimes lengthy, waits and delays.
Analysis of the delays was performed looking for associated root causes. Some causes were associated with resource constraints - many of which were fixed and difficult to change. One root cause, completely under the control of the provider, was their scheduling practices - both the configuration of their master scheduling template and their ad hoc scheduling practices.
One doctor's schedule was analyzed and modeled in a decision support system (DSS). To further frame the impact of the provider's schedule, we used probabilistic data with Monte Carlo simulation (Oracle Crystal Ball) to graphically show the impact of different scenarios. The provider could readily see the impact of his schedule changes, both from his perspective and the patients'. My presentation will demonstrate the use of this Monte Carlo simulation.