Unlocking Cost Savings in Healthcare: How Difference-in-Differences (DID) Can Measure the Impact of Interventions

Vidya Rajasekhara Reddy Tetala

Abstract: Difference-in-Differences is a powerful means of obtaining an estimate for the causal effect of interventions from observational data, especially in health care, economics and social sciences where random controlled trials (RCT) are often impracticable. This paper explores how DID can be applied to healthcare cost-saving interventions by balancing treatment and control groups with propensity score matching and weighting with the inverse propensity score. It also discusses the recent development of this method, integrating machine learning into DID, which strengthens the capability of DID. Case study on cost reduction of hospital readmission illustrates the usefulness of the methodology. It is further elaborated with a detailed explanation of the calculation process and the application of the propensity score to remove the confounding biases.

Keywords: Difference-in-Differences, DID, healthcare treatment or interventions, cost reduction, calculation of propensity score, inverse of the propensity score, machine learning, causal inference, cloud computing, SRE

How to Cite?: Vidya Rajasekhara Reddy Tetala, "Unlocking Cost Savings in Healthcare: How Difference-in-Differences (DID) Can Measure the Impact of Interventions", Volume 13 Issue 10, October 2024, International Journal of Science and Research (IJSR), Pages: 408-411, https://www.ijsr.net/getabstract.php?paperid=SR241004074146, DOI: https://dx.doi.org/10.21275/SR241004074146