title: Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.
abstract: 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.
  
  
  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").
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Medicine, Stanford University, Stanford, CA, USA.
authors.forename: Jonathan H
authors.initials: JH
authors.lastname: Chen
authors.affiliation: Geriatrics Research Education and Clinical Center, Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA.|Primary Care and Outcomes Research (PCOR), Stanford University, Stanford, CA, USA.
authors.forename: Mary K
authors.initials: MK
authors.lastname: Goldstein
authors.affiliation: Department of Medicine, Stanford University, Stanford, CA, USA.|Center for Innovation to Implementation (Ci2i), Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Asch
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA, USA.
authors.forename: Lester
authors.initials: L
authors.lastname: Mackey
authors.affiliation: Department of Medicine, Stanford University, Stanford, CA, USA.|Department of Bioengineering, Stanford University, Stanford, CA, USA.|Department of Genetics, Stanford University, Stanford, CA, USA.
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D000465:Algorithms; D057225:Data Mining; D020000:Decision Support Systems, Clinical; D003955:Diagnostic Tests, Routine; D057286:Electronic Health Records; D006760:Hospitalization; D006801:Humans; D050316:Medical Order Entry Systems; D015233:Models, Statistical; D005791:Patient Care; D012372:ROC Curve
keywords: clinical decision support systems; clinical summarization; data mining; electronic health records; order sets; probabilistic topic modeling