Toward understanding variations in price and billing in US healthcare services: A predictive analytics approach

Sagnika Sen, Amit V. Deokar

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The practice of excess charge where healthcare providers bill Medicare more than the allowed limit, is pervasive in the United States. Previous research has argued that it is possibly used to set private insurance prices, at times to account for inadequate Medicare reimbursements, ultimately leading to high price variations and causing inequities in healthcare service delivery. The objective of this study is to predict a provider's excess charge quartile and identify the features predictive of their membership in that billing group. We employ distinct multi-class prediction models for three common medical procedures with the highest degree of price variation. The models incorporate four different dimensions of healthcare service- healthcare provider, medical procedure, medical practice, and socioeconomic and demographics of a provider's patient base. Our analysis of the top predictive features demonstrates that billing patterns are characteristic of a provider's overall practice rather than specific medical procedures. Also, our results bear important public health implications in the US as our study reveals socioeconomic and demographic factors to be significant predictors of a provider's billing patterns. The features used in our prediction model can be adapted to other country-specific settings as well. Our work thus provides policymakers with a data-driven foundation for using a holistic framework that has not yet been utilized in healthcare pricing decisions.

Original languageEnglish (US)
Article number118241
JournalExpert Systems With Applications
Volume209
DOIs
StatePublished - Dec 15 2022

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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