Machine Learning and Demand Forecasting Reduce Neurology Patient Wait Times

Across the health care industry, hospitals and health systems are challenged to provide patients timely access to the care they need. The Neurology department at Mayo Clinic Florida was challenged with high wait times for patient appointments. Practice leadership was eager to better understand the right mix of appointment slots by specific Neurology subspecialty in order to appropriately resolve their issues. 

Existing systems did not account for subspecialty information, which was determined to be a critical variable in scheduling, along with a variety of factors that could impact optimal use of appointment slots: Time (Daily, Weekly, Yearly), Appointment Type, Appointment Duration, Patient Geographic Location.
In order to understand the supply of appointments needed, a demand forecast by each subspecialty was evaluated to understand trending and seasonality. Machine learning techniques were used to minimize forecast error. Optimization algorithms minimized the number of staff needed while matching existing calendar templates with forecasted demand.
Subspecialty forecasts, combined with an understanding of the patient’s preference in terms of wait time for an appointment, were used to set the correct level and mix of slots on Neurology’s calendars.

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