Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

J Am Med Inform Assoc. 2017 May 1;24(3):472-480. doi: 10.1093/jamia/ocw136.


Objective: Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets.

Materials and methods: The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders.

Results: Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P < 10 -20 ) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation").

Discussion: Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability.

Conclusion: Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support.

Keywords: clinical decision support systems; clinical summarization; data mining; electronic health records; order sets; probabilistic topic modeling.

Publication types

  • Comparative Study

MeSH terms

  • Algorithms*
  • Data Mining
  • Decision Support Systems, Clinical*
  • Diagnostic Tests, Routine
  • Electronic Health Records*
  • Hospitalization
  • Humans
  • Medical Order Entry Systems*
  • Models, Statistical*
  • Patient Care*
  • ROC Curve