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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 &amp;gt;\u20094\u2009K 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.<\/jats:p><jats:p>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\u2009&amp;lt;\u200910\u221220) 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., \u201ccritical care,\u201d \u201cpneumonia,\u201d \u201cneurologic evaluation\u201d).<\/jats:p><jats:p>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.<\/jats:p><jats:p>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.<\/jats:p>","DOI":"10.1093\/jamia\/ocw136","type":"journal-article","created":{"date-parts":[[2016,9,22]],"date-time":"2016-09-22T00:19:09Z","timestamp":1474503549000},"page":"472-480","source":"Crossref","is-referenced-by-count":45,"title":["Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets"],"prefix":"10.1093","volume":"24","author":[{"given":"Jonathan H","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Medicine, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary K","family":"Goldstein","sequence":"additional","affiliation":[{"name":"Geriatrics Research Education and Clinical Center, Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA"},{"name":"Primary Care and Outcomes Research (PCOR), Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven M","family":"Asch","sequence":"additional","affiliation":[{"name":"Department of Medicine, Stanford University, Stanford, CA, USA"},{"name":"Center for Innovation to Implementation (Ci2i), Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lester","family":"Mackey","sequence":"additional","affiliation":[{"name":"Department of Statistics, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Russ B","family":"Altman","sequence":"additional","affiliation":[{"name":"Department of Medicine, Stanford University, Stanford, CA, USA"},{"name":"Department of Bioengineering, Stanford University, Stanford, CA, USA"},{"name":"Department of Genetics, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,9,20]]},"reference":[{"key":"2020110612374841100_ocw136-B1","volume-title":"Crossing the Quality Chasm: A New Health System for the 21st Century","author":"Richardson","year":"2001"},{"key":"2020110612374841100_ocw136-B2","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1001\/jama.2009.205","article-title":"Scientific evidence underlying the ACC\/AHA","volume":"301","author":"Tricoci","year":"2009","journal-title":"J Am Med Inform Assoc."},{"key":"2020110612374841100_ocw136-B3","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.ahj.2013.12.003","article-title":"Eliminating the \u2018expensive\u2019 adjective for clinical trials","volume":"167","author":"Lauer","year":"2014","journal-title":"Am Heart J."},{"key":"2020110612374841100_ocw136-B4","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1001\/jama.2009.205","article-title":"Scientific evidence underlying the ACC\/AHA clinical practice guidelines","volume":"301","author":"Tricoci","year":"2009","journal-title":"JAMA."},{"key":"2020110612374841100_ocw136-B5","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1056\/NEJM197804062981405","article-title":"The weight of medical knowledge","volume":"298","author":"Durack","year":"1978","journal-title":"N Engl J Med."},{"key":"2020110612374841100_ocw136-B6","volume-title":"Health System Leaders Working Toward High-Value Care","author":"Alper","year":"2014"},{"key":"2020110612374841100_ocw136-B7","first-page":"54163","article-title":"Health information technology: standards, implementation specifications, and certification criteria for electronic health record technology, 2014 edition; revisions to the permanent certification program for health information technology. 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