title: Automated physician order recommendations and outcome predictions by data-mining electronic medical records.
abstract: The meaningful use of electronic medical records (EMR) will come from effective clinical decision support (CDS) applied to physician orders, the concrete manifestation of clinical decision making. CDS development is currently limited by a top-down approach, requiring manual production and limited end-user awareness. A statistical data-mining alternative automatically extracts expertise as association statistics from structured EMR data (>5.4M data elements from >19K inpatient encounters). This powers an order recommendation system analogous to commercial systems (e.g., Amazon.com's "Customers who bought this…"). Compared to a standard benchmark, the association method improves order prediction precision from 26% to 37% (p<0.01). Introducing an inverse frequency weighted recall metric demonstrates a quantifiable improvement from 3% to 17% (p<0.01) in recommending more specifically relevant orders. The system also predicts clinical outcomes, such as 30 day mortality and 1 week ICU intervention, with ROC AUC of 0.88 and 0.78 respectively, comparable to state-of-the-art prognosis scores.
authors.affiliation: Department of Medicine, Stanford University, Stanford, CA 94305, USA.
authors.forename: Jonathan H
authors.initials: JH
authors.lastname: Chen
authors.affiliation: Department of Medicine, Stanford University, Stanford, CA 94305, USA. ; Departments of Bioengineering and Genetics, Stanford University, Stanford, CA 94305, USA.
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman