Title: Reducing the Bias: Practical Application of Propensity Score Matching in Healthcare Program Evaluation
Abstract: To stay competitive in the marketplace, healthcare programs must be capable of reporting the true savings to clients. This is a tall order considering most healthcare programs are setup to be available to the client’s entire population; thus, the program cannot be conducted as a randomized control trial. In order to evaluate the performance of the program for the client, we use an observational study design which has inherent selection bias due to its inability to randomly assign participants. To reduce the impact of bias, we apply propensity score matching to the analysis. This technique is beneficial to healthcare program evaluations because it helps reduce selection bias in the observational analysis and in turn provides a clearer view of the client’s savings. This paper will explore how to develop a propensity score, evaluate the use of inverse propensity weighting versus propensity matching, and determine the overall impact of the propensity score matching method on the observational study population. All results shown are drawn from a savings analysis using a participant (cases) versus non-participant (controls) observational study design for a healthcare decision support program aiming to reduce emergency room visits.
Publication Year: 2014
Publication Date: 2014-01-01
Language: en
Type: article
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot