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AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:226-235. eCollection 2018.

Inpatient Clinical Order Patterns Machine-Learned From Teaching Versus Attending-Only Medical Services.

Author information

1
Mathematical & Computational Science Program, Stanford University, Stanford, CA, USA.
2
Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
3
Prevention Research Center, Stanford University, Stanford, CA, USA.
4
Department of Medicine, Stanford University, Stanford, CA, USA.

Abstract

Clinical order patterns derived from data-mining electronic health records can be a valuable source of decision support content. However, the quality of crowdsourcing such patterns may be suspect depending on the population learned from. For example, it is unclear whether learning inpatient practice patterns from a university teaching service, characterized by physician-trainee teams with an emphasis on medical education, will be of variable quality versus an attending-only medical service that focuses strictly on clinical care. Machine learning clinical order patterns by association rule episode mining from teaching versus attending-only inpatient medical services illustrated some practice variability, but converged towards similar top results in either case. We further validated the automatically generated content by confirming alignment with external reference standards extracted from clinical practice guidelines.

PMID:
29888077
PMCID:
PMC5961816

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