Learning bundled care opportunities from electronic medical records

J Biomed Inform. 2018 Jan:77:1-10. doi: 10.1016/j.jbi.2017.11.014. Epub 2017 Nov 22.


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.

Keywords: Bundled care; Clinical phenotyping; Data mining; Electronic medical record; Network analysis; Phenotype clusters; Topic modeling; Workflow.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Comorbidity
  • Data Mining / methods*
  • Delivery of Health Care / organization & administration*
  • Electronic Health Records*
  • Humans
  • Machine Learning
  • Medical Informatics
  • Patient Care Bundles*
  • Patient Care Management
  • Phenotype
  • Workflow