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084 PLUGGED-IN (Providing Likeable and Understandable Guidelines using GRADE in the EMR with Direct links to Individual patient data) Phase 2
  1. L Brandt1,
  2. T Elde2,
  3. T Agoritsas3,
  4. G Guyatt3,
  5. A Kristiansen1,
  6. P Alonso-Coello4,
  7. E Akl5,
  8. J Meerpohl6,
  9. P Vandvik1
  1. 1Department of Medicine, Innlandet Hospital Trust, Gjøvik, Norway
  2. 2Health Informatics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
  3. 3Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada
  4. 4Iberoamerican Cochrane Centre, Institute of Biomedical Research (IIB Sant Pau), Barcelona, Spain
  5. 5Department of Internal Medicine, American University of Beirut, Beirut, Lebanon
  6. 6German Cochrane Center, Institute of Medical Biometry and Medical Informatics, University of Freiburg, Germany

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.

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