Managing healthcare costs by peer-group modeling

Sholom M. Weiss, Casimir Kulikowski, Robert S. Galen, Peder A. Olsen, Ramesh Natarajan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We describe statistical methods for managing healthcare costs using peer-group models and outlier detection. A peer group is a collection of similar entities such as patients, physicians, clinics, hospitals or pharmacies. In an empirical study of drug volumes prescribed by physicians, we examined the billing and prescription records for all patients covered by a major insurer over a 6 month period, encompassing over twenty million individual patient-physician encounters. During this period, 21,243 physicians prescribed a major pain-control medication which is frequently the subject of abuse - oxycodone. Profiles were computed for each physician based on their specialty and the clinical characteristics of their patients. For each physician, the average prescription volume within the corresponding peer group of similar physicians is an estimate of the expected volume of prescriptions for that physician. Strategies were developed to select outliers from the expected values as the ones that are candidates for potential cost reduction. Overall, the prediction of actual outcomes from peer profiles is significantly better than chance, with a reduction of average error of 45.5 %. For the 10 % of physicians that prescribed the most medications, there were extreme and highly significant differences found between their expected and predicted outcomes.

Original languageEnglish (US)
Pages (from-to)752-759
Number of pages8
JournalApplied Intelligence
Volume43
Issue number4
DOIs
StatePublished - May 30 2015

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Keywords

  • Healthcare
  • Machine learning
  • Nearest neighbor
  • Peer profiles
  • Prediction

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