Forecasting health care expenditures and utilization based on a Markov process and a deterministic cost function in managed care settings

Abstract

This study is exploratory in nature with a goal of extending the application of stochastic modeling to health economics research. The aim of this study is to investigate whether the prediction of utilization and attendant costs through the development of a stochastic model, specifically a firstorder Markov chain, can be adapted to specific diseases and/or events. The original study considered three diseases. They included both chronic and acute diseases. The choices were diabetes, hypertension and myocardial infarction. For the purposes of this article the application is illustrated by looking at the group with hypertension. The group of n = 1019 was randomly split into two groups. They were then categorized into age groups {over 66/under 66} and gender {male/female}. The first group was used to generate the transition probabilities and second was used to validate the results. Chi-square analysis was performed and there were no significant differences between the groups. The costs were computed and presented.

Personalized Healthcare System for Chronic Disease Care in Cloud Environment

“…be the fundamental matrix for a Markov chain with an ergodic state [16]. The next consideration is the number of times that a transient state is occupied for an individual.…”

“…The Markov process is being employed as the first component of this model to predict the utilization for this healthcare problem [16]. The transition or change in utilization from state i to state j is influenced by the prior state, and is denoted as…”

“…The method for determining the expected number of cycles during which an individual stays in a resident transition state, conditional on having just entered the system, follows [16]. The mean number of days spent in m j after starting in state m l is It should be observed that…”

Demand Forecasts for Chronic Cardiovascular Diseases Medication Based on Markov Chains

“…The application of the Markov chain theory in healthcare resource management has shown to be diverse, going from forecasting healthcare expenditures using intrinsic patients' attributes such as diagnostic, gender, and age [6], to controlling the intermittent demand for certain products [7]. The popularity of this approach, besides lying in the convenient "memory-less" characteristic, might be due to the fact that Markov chains are flexible enough to manage different types of criteria, as several authors have pointed out.…”