A Markov model for forecasting inventory levels for Navy Medical Service Corps healthcare administrators

Abstract

The United States Navy Medical Service Corps is a diverse group of healthcare professionals that functions as a support community, providing administrative and clinical services as an integral part of Navy Medicine. There are currently more than 3,000 active and reserve Medical Service Corps officers serving around the globe, approximately 40 percent of whom are healthcare administrators. This thesis develops a Markov model to estimate the number of HCA accessions necessary to meet inventory requirements from FY14 to FY18. The general HCA model validation and analysis show that aggregate annual transition rates pass the stationary assumption required of Markov models. Models the study develops for some subspecialties perform better than others and are consistent and accurate. Consistency and accuracy are important because budget planners and recruiting command rely on manpower estimates during the fiscal year. These results suggest that the Markov model is a useful tool for HCA community managers to forecast inventory levels across rank and subspecialties, and is effective for determining force structure. Determining the end strength of HCA officers is an important part of the accession planning process for manpower planners to balance the force structure to effectively minimize deviation from target inventory levels that impact training and labor costs, as well as to manage career progression.