Traditional Clinical Decision Support Systems (CDSS) in Electronic Medical Records (EMR) provide clinicians with recommendations based on algorithms/rules using patient-specific information as inclusion/exclusion criteria. Unfortunately these are still typically based on manual rules and algorithms for when to show recommendations based on the assumption that “all advice are equal”.
All advice is not equal and improved systems (GRADE) for developing evidence-based guidelines result in the majority of recommendations being weak and warrant balanced clinical judgments without clear inclusion/exclusion criteria. If CDSS were to use structured GRADE recommendations, they would get the strength as an inherent part of the recommendation content.
In this research program we’re introducing a new way to present guideline recommendations together with clinical details for personalised healthcare advice. Developed with GRADE methodology these recommendations can be shown within the Electronic Health Records, and through an API the Electronic Health Record will automatically know which clinical details guideline authors thought to be relevant.
Guidelines that are developed in small structured parts, can show different levels of detail to clinicians, depending on their needs at the point of care. There will always be a need for more or less information depending on the complexity of the situation and the needs of the patient. These small structured parts will automatically be linked with patient specific data from the EHR for any given patient.