Forecasting Weekly Outpatient Demands at Clinics within a Large Medical Center

Large regional health care systems face challenges in planning physician schedules and appointments to achieve patient-access goals and efficient use of resources. We describe the development of a planning and forecasting system that predicts outpatient visits (OPVs) for 23 primary and specialty care clinics at a large medical center in Rochester, Minn. We develop and compare univariate, multivariate, and combined methods for forecasting 12-week horizons of OPVs. The multivariate method provides forecasts and information that can be shared among the 23 clinics, a distinct advantage of this approach. The combined method—the average of the univariate and multivariate forecasts—is the most effective method for forecasting OPVs. Univariate, multivariate, and combined methods have the lowest forecast root mean squared errors (RMSEs) for 26.1%, 30.4%, and 43.5% of the clinics, respectively. In addition, when the symmetric median absolute percent error (SMdAPE) is used to evaluate models, the combined, univariate, and multivariate methods yield forecasts with medians of 6.8%, 7.1%, and 7.9%, respectively—again confirming the effectiveness of the combined method.