Robust Prediction Of Treatment Times In Concurrent Patient Care

Outpatient centers comprised of many concurrent clinics increasingly see higher patient volumes. In these centers, decisions to improve clinic flow must account for the high degree of interdependence when critical personnel or equipment is shared between clinics. Discrete event simulation models have provided clinical decision support, but rarely address high-volume clinics with shared resources. While highly complex models are now capable of representing clinics in detail, validation techniques often do not evaluate model predictive performance when presented with new data. Cross-validation provides a means to evaluate the robustness of model treatment time predictions when ongoing data collection in clinics is impractical. Ensuring robust predictions assures validity in the use of models to optimize clinic performance. We apply cross-validation in evaluating a model of glaucoma clinic service at Duke Eye Center. In-person observation is used to verify the accuracy of operations data collected through electronic health records (EHR). From the EHR data, we formulate a stochastic reward net model, employing phase-type distributions to represent treatment durations, and solved through discrete event simulation. The model is formulated in two configurations to represent (1) concurrent demand on clinic staff, or (2) independently functioning clinics. Evaluating these two alternatives in cross-validation studies, we find model prediction accuracy improves when interdependence is explicitly modeled in the examined setting.