Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.
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.
['Chen|Jonathan H|JH|', 'Goldstein|Mary K|MK|', 'Asch|Steven M|SM|', 'Mackey|Lester|L|', 'Altman|Russ B|RB|']
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
probabilistic topic modeling order sets electronic health records clinical decision support systems clinical summarization data mining