Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets.

Abstract

Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integrating clinical knowledge with machine learning can facilitate effective development and maintenance of order sets while promoting best practices in ordering. To this end, we simulated the revision of an "AM Lab Order Set" under 6 revision approaches. Revisions included changes in the order set content or default settings through 1) population statistics, 2) individualized prediction using machine learning, and 3) clinical knowledge. Revision criteria were determined using electronic health record (EHR) data from 2014 to 2015. Each revision's clinical appropriateness, workload from using the order set, and generalizability across time were evaluated using EHR data from 2016 and 2017. Our results suggest a potential order set revision approach that jointly leverages clinical knowledge and machine learning to improve usability while updating contents based on latest clinical knowledge and best practices.