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Estimating Variability in Hospital Charges: The Case of Cesarean Section

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Listed:
  • Anna Perfilyeva
  • Vittal Raghavendra Miskin
  • Ryan Aven
  • Craig Drohan
  • Huthaifa I. Ashqar

Abstract

This study sought to better understand the causes of price disparity in cesarean sections, using newly released hospital data. Beginning January 1, 2021, Centers for Medicare and Medicaid Services (CMS) requires hospitals functioning in the United States to publish online pricing information for items and services these hospitals provide in a machine-readable format and a consumer friendly shoppable format. Initial analyses of these data have shown that the price for a given procedure can differ in a hospital and across hospitals. The cesarean section (C-section) is one of the most common inpatient procedures performed across all hospitals in the United States as of 2018. This preliminary study found that for C-section procedures, pricing varied from as little as \$162 to as high as \$115,483 for a single procedure. Overall, indicators for quality and whether or not the hospital was a teaching hospital were found to be significantly significant, while variables including median income and the gini coefficient for wealth inequality were not shown to be statistically significant.

Suggested Citation

  • Anna Perfilyeva & Vittal Raghavendra Miskin & Ryan Aven & Craig Drohan & Huthaifa I. Ashqar, 2024. "Estimating Variability in Hospital Charges: The Case of Cesarean Section," Papers 2411.08174, arXiv.org.
    • Handle: RePEc:arx:papers:2411.08174
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    References listed on IDEAS

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    1. Ahmad Radwan & Mohannad Amarneh & Hussam Alawneh & Huthaifa I. Ashqar & Anas AlSobeh & Aws Abed Al Raheem Magableh, 2024. "Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis," International Journal of Web Services Research (IJWSR), IGI Global, vol. 21(1), pages 1-22, January.
    2. Lee Whieldon & Huthaifa I. Ashqar, 2022. "Predicting residential property value: a comparison of multiple regression techniques," SN Business & Economics, Springer, vol. 2(11), pages 1-16, November.
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