Journal
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Volume 24, Issue 3, Pages 472-480Publisher
OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocw136
Keywords
clinical decision support systems; electronic health records; data mining; probabilistic topic modeling; clinical summarization; order sets
Categories
Funding
- National Institute of Environmental Health Sciences of the National Institutes of Health [K01ES026837]
- Stanford Translational Research and Applied Medicine program in the Department of Medicine
- Stanford Learning Healthcare Systems Innovation Fund
- Stanford Clinical and Translational Science Award (CTSA) [UL1 TR001085]
- VA Office of Academic Affiliations and Health Services Research and Development Service Research funds
- National Institutes of Health/National Institute of General Medical Sciences PharmGKB [R24GM61374, LM05652, GM102365]
- Stanford NIH/National Center for Research Resources CTSA award [UL1 RR025744]
- National Center for Research Resources, National Institutes of Health
- National Center for Advancing Translational Sciences, National Institutes of Health [UL1 RR025744]
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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 >10K 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 >4K 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.
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