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

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 24; no. 3; pp. 472 - 480
• Main Authors: Chen, Jonathan H, Goldstein, Mary K, Asch, Steven M, Mackey, Lester, Altman, Russ B
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.05.2017
• Subjects: Algorithms; Data Mining; Decision Support Systems, Clinical; Diagnostic Tests, Routine; Editor's Choice; Electronic Health Records; Hospitalization; Humans; Medical Order Entry Systems; Models, Statistical; Patient Care; Research and Applications; ROC Curve; probabilistic topic modeling; electronic health records; clinical decision support systems; data mining; order sets; clinical summarization
• ISSN: 1067-5027; 1527-974X; 1527-974X
• 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.