ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2013
Mining for clinical expertise in (undocumented) order sets to power an order suggestion system.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.International journal of medical informatics · 2017Article
- Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- DYNAMICALLY EVOLVING CLINICAL PRACTICES AND IMPLICATIONS FOR PREDICTING MEDICAL DECISIONS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- Data-Mining Electronic Medical Records for Clinical Order Recommendations: Wisdom of the Crowd or Tyranny of the Mob?AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
- Automated physician order recommendations and outcome predictions by data-mining electronic medical records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Physician orders, the concrete manifestation of clinical decision making, are enhanced by the distribution of clinical expertise in the form of order sets and corollary orders. Conventional order sets are top-down distributed by committees of experts, limited by the cost of manual development, maintenance, and limited end-user awareness. An alternative explored here applies statistical data-mining to physician order data (>330K order instances from >1.4K inpatient encounters) to extract clinical expertise from the bottom-up. This powers a corollary order suggestion engine using techniques analogous to commercial product recommendation systems (e.g., Amazon.com's "Customers who bought this…" feature). Compared to a simple benchmark, the item-based association method illustrated here improves order prediction precision from 13% to 18% and further to 28% by incorporating information on the temporal relationship between orders. Incorporating statistics on conditional order frequency ratios further refines recommendations beyond just "common" orders to those relevant to a specific clinical context.
Identifiers
24303232PMC3845792What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.