OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records

• Published in: Journal of the American Medical Informatics Association : JAMIA 2016-03, Vol.23 (2), p.339-348
• Main Authors: Chen, Jonathan H, Podchiyska, Tanya, Altman, Russ B
• Format: Article
• Language: English
• Subjects: Algorithms; Benchmarking; Data Mining; Decision Making, Computer-Assisted; Decision Support Systems, Clinical; Electronic Health Records; Humans; Patient Admission; Research and Applications
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1093/jamia/ocv091

Objective: To answer a “grand challenge” in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com’s product recommender. Materials and Methods: EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender’s ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Results: Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% (P