Case Study: An Application of Logistic Regression in a Six Sigma Project in Health Care

ABSTRACT

Health care today is facing serious problems: quality of care does not meet patients' needs and costs are exploding. In the cardiology department of the Virga Jesse Hospital in Belgium, discharged patients are advised to participate in a rehabilitation program. However, many of the discharged patients do not join the program, and others quit before being declared cured (a so-called dropout). An improvement project was started that aims to increase revenues by either attracting more patients to the rehabilitation program or reducing the fraction of dropouts.

A large data set with 516 treated patients was available. We model the probability that a patient joins the program as a function of various numerical and categorical influence factors. First an exploratory data analysis is performed, using bar charts and box plots. This is followed by a more formal statistical analysis using logistic regression.

The logistic regression model reveals the important influence factors. The probability of joining the program depends on whether a patient has a car at his or her disposal and the distance from a patient's home to the hospital. As a solution, various measures to stimulate carpooling were implemented. Prior to the implementation, a cost–benefit analysis was conducted using the fitted regression model.

KEYWORDS:

• DMAIC
• generalized additive model
• logistic regression