Modeling patient's value using a stochastic approach: An empirical study in the medical industry

Abstract

Background and objective: The rapid growth of computer methods encourages and creates competitive advantages in the medical industry. Nowadays many health centers try to build successful and beneficial relationships with their patients using customer relationship management (CRM) methods, to recognize target patients, attract potential patients, increase patient loyalty and maximize profitability. Customer lifetime value (CLV) is a metric that can help organizations to calculate their customers' value or group them; therefore in this research we aim to develop a new CLV model for the medical industry that groups patients using computer-based methods.

Methods: To model CLV for the medical industry, we will use two computer-based methods. First, to model patients' behavior, a data mining approach is required: the K-means algorithm is used to cluster patients and the decision tree technique is used to analyze patient clusters. Next, Markov chain model, a stochastic approach, is utilized to predict future behavior of customers RESULTS: This paper proposes a new CLV model for the medical industry that has some benefits over other CLV papers. It is patient behavior based, helping us to predict the future behavior of each patient as well as helping to modify managerial strategies for each type of patient. The derived CLV model includes less than 0.08 error rates.

Conclusions: Using the derived CLV model helps health centers to group their patients by computer-based methods, which makes their decision making more accurate and trustworthy. The present research helps organizations within the health industry to group and rank their patients by a new CLV model and fit their strategies to each patient group, based on his/her behavior type.

Keywords: CLV; Computer-based methods in medical; Data mining; Markov chain model; Medical industry; Patient's value.