Improving Pet Veterinary Hospital Staffing with Predictive Modeling

Introduction:

iLink helped one of the top pet veterinary hospital chains in the United States to develop a predictive model for forecasting the demand for Doctor of Veterinary Medicine (DVM) needed to treat patients.

Client Requirement:

The client was facing challenges with DVM staffing due to the unpredictable demand for their services. The client needed a solution to forecast the number of patients that would require DVM services to optimize their staffing and improve the quality of care they provide to their patients.

Process:

iLink worked with the client to understand their business objectives and the challenges they were facing. The team then analyzed the client’s data to build a predictive model that would forecast the number of patient visits that require a DVM. The input parameters for the model included the day and season of the visit, the geography of the hospital, the number of appointments scheduled, and the number of visits.

The model was built using machine learning algorithms that learned from the data to predict. The iLink team then tested the model to ensure its accuracy and provided recommendations on how to optimize DVM staffing based on the model’s predictions.

Results:

The predictive model built by iLink helped the client improve the accuracy of their DVM staffing predictions by 40%. This resulted in the client being able to optimize their DVM staffing levels to meet the demand for their services. The client was also able to reduce costs associated with overstaffing and understaffing while providing the best possible care to their patients.