084 PLUGGED-IN (Providing Likeable and Understandable Guidelines using GRADE in the EMR with Direct links to Individual patient data) Phase 2. (15th August 2013)

• Record Type: Journal Article
• Authors: Brandt, L; Elde, T; Agoritsas, T; Guyatt, G; Kristiansen, A; Alonso-Coello, P; Akl, E; Meerpohl, J; Vandvik, P
• Is Part Of: BMJ quality & safety. Volume 22(2013)Supplement 1
• Page Start: A39
• Page End: A39
• Publication Date: 2013-08-15
• Subjects: Medical care -- Quality control -- Periodicals; Health facilities -- Risk management -- Periodicals; Medical errors -- Prevention -- Periodicals; 362.106805
• DOI: 10.1136/bmjqs-2013-002293.115
• Languages: English
• ISSNs: 2044-5415

Abstract

Abstract : Background: Traditional clinical decision support systems in Electronic Medical Records (EMR) use algorithms with inclusion/exclusion criteria to provide direction to clinicians. Improved systems for developing trustworthy guidelines (e.g. GRADE, ) typically include many weak recommendations unsuited for clear inclusion/exclusion criteria, and in which the right decision varies from patient to patient. Through PLUGGED-IN phase 1 we developed a conceptual framework and a guideline authoring/publication platform to allow use of trustworthy guidelines directly as decision support in EMRs, not dependent on traditional algorithms or reproduction of the content. Our framework is based on a multilayered guideline presentation format developed in DECIDE. Objectives: To implement and test our novel approach to decision support where relevant patient specific information is shown alongside evidence based recommendations in EMRs. Methods: We used a web guideline published through the MAGIC (Making Grade the Irresistible Choice) application, which allowed our EMR partner to make use of its structured content, ontology-coded clinical questions and recommendation-specific EMR elements. Results: The EMR system was able to interact with the guideline, suggest relevant recommendations displayed along with relevant patient specific information (lab tests, measurements, medications), and offer these to facilitate direct ordering. We will show real examples and live products. Discussion: Results suggest we can offer a complementary approach to traditional algorithm-based systems that is compatible with a large number of EMRs. Implications for Guideline Developers/Users: PLUGGED-IN provides a model for direct use of guidelines as decision support in EMRs.