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.
Learning Objective 1: Evaluate the influence of clinical setting on the quality of clinical order patterns learned from data-mining electronic health record data.
Learning Objective 2 (Optional): Use clinical practice guidelines as a reference standard for evaluating machine-learned decision support content.
Jason Wang (Presenter)
Alejandro Schuler, Stanford University
Nigam Shah, Stanford University
Michael Baiocchi, Stanford University
Jonathan Chen, Stanford University