Predictive Modeling for Telemedicine Service Demand
Introduction: Emergency teleneurology care has grown in magnitude, impact, and validation. Stroke is a leading cause of death in the US and the timely treatment of stroke results in better outcomes for patients. Teleneurology provides evidence-based care to patients even when a board-certified neurologist is not physically on site. Determining staffing demand for telemedicine consultation for a specific period of time is an integral part of the decision-making activities of providers of acute care telemedicine services. This study aims to build a forecasting model to predict consultation demand to optimize telemedicine provider staffing. Such forecasting models acquire added importance in emergency situations such as the current COVID-19 pandemic.
Materials and Methods: This study trained consultation data of SOC Telemed, a private telemedicine provider, from 411 hospitals nationwide and involving 97,593 incidents of consultations. The forecasting model analyzes characteristics including hospital size (number of beds), annual volume, patient demographics, time of consultation, and reason for consultation.
Results: Several regression techniques were used to demonstrate a strong correlation between these features and weekly demand with r2 = 0.7821. Reason for consult in the past week was the strongest predictor for the demand in the next week with r2 = 0.7899.
Conclusion: A predictive model for demand forecasting can optimize telemedicine resources to improve patient care and help telemedicine providers decide how many physicians to staff. The goal of the forecasting model is to improve patient care and outcomes by providing physicians timely and efficiently to meet consultation demand. The ability to predict demand and calculate expected volume allow telemedicine providers to schedule physicians in advance. This mitigates the clinical risk of excess patient demand.and long waiting time, as well as the financial risk of surplus of physicians.
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