Description
Electronic health record (EHR) systems native implementations of Clinical Decision Support rules are not suitable to be used for knowledge management, and most EHRs do not provide tools for rule extraction. We discuss an automated process for reverse engineering of native implementations into standards-based, machine and human readable knowledge artifacts. The process supports, among others, quality control, analytics, revision and change management, as well as impact and outcome measurement.
Learning Objective: Understand the challenges and a possible solution for extracting clinical decision support rules from an electronic health record system into a standards-based representation.
Authors:
Branden Hickey (Presenter)
Mayo Clinic
Adam Bartscher, Mayo Clinic
Marc Sainvil, Mayo Clinic
Robert Freimuth, Mayo Clinic
Jane Shellum, Mayo Clinic
Davide Sottara, Mayo Clinic
Presentation Materials: