Learning bundled care opportunities from electronic medical records

You Chen*, Abel N. Kho, David Liebovitz, Catherine Ivory, Sarah Osmundson, Jiang Bian, Bradley A. Malin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Objective The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-driven approach can automatically learn potential bundles. Methods We designed a framework to infer health condition collections (HCCs) based on the similarity of their clinical workflows, according to electronic medical record (EMR) utilization. We evaluated the framework with data from over 16,500 inpatient stays from Northwestern Memorial Hospital in Chicago, Illinois. The plausibility of the inferred HCCs for bundled care was assessed through an online survey of a panel of five experts, whose responses were analyzed via an analysis of variance (ANOVA) at a 95% confidence level. We further assessed the face validity of the HCCs using evidence in the published literature. Results The framework inferred four HCCs, indicative of (1) fetal abnormalities, (2) late pregnancies, (3) prostate problems, and (4) chronic diseases, with congestive heart failure featuring prominently. Each HCC was substantiated with evidence in the literature and was deemed plausible for bundled care by the experts at a statistically significant level. Conclusions The findings suggest that an automated EMR data-driven framework conducted can provide a basis for discovering bundled care opportunities. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalJournal of Biomedical Informatics
Volume77
DOIs
StatePublished - Jan 2018

Keywords

  • Bundled care
  • Clinical phenotyping
  • Data mining
  • Electronic medical record
  • Network analysis
  • Phenotype clusters
  • Topic modeling
  • Workflow

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Fingerprint Dive into the research topics of 'Learning bundled care opportunities from electronic medical records'. Together they form a unique fingerprint.

Cite this