An Electronic Evidence Base Supporting Derivation, Dissemination and Learning for Diagnostic Clinical Prediction Rules in Primary Care Practice
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Diagnostic error is a threat to patient safety in the context of primary care. Clinical prediction rules (CPRs) are a form of structured evidence based guideline that aim to assist clinical reasoning through the application of empirically quantified evidence to evaluate patient cases. Their acceptance in clinical practice has been hindered by literature-based dissemination and doubts regarding their wider applicability. The use of CPRs as part of electronic decision support tools has also lacked acceptance for many reasons: poor integration with electronic health records and clinician workflow, generalised guidelines lacking patient-specific recommendations at point-of-care, static rule based evidence that lacks transparency and use of proprietary technical standards hindering interoperability.
The ‘learning health system’ (LHS) describes a distributed technology based infrastructure to generate computable clinical evidence and efficiently disseminate it into clinical practice. This research describes an LHS based on computable CPRs for diagnostic decision support that makes use of aggregated sources of primary care electronic health record data to derive and disseminate computable CPRs.
Based on a literature review of clinical and technical best practice regarding use of CPRs, a theoretical model for CPRs supporting two critical aspects for a successful LHS is proposed: the model representation and translation of clinical evidence into effective practice, and the generation of curated clinical evidence that can dynamically populate those models thus closing the learning health system loop. A functional implementation of the theoretical model demonstrates an infrastructure that is model-driven, service oriented, constructed using open standards, and supports a learning evidence base derived from electronic sources of patient data.
A number of challenges exist for the LHS community to consider including medico-legal responsibility for generated diagnostic evidence, developing trust in the LHS, constraints imposed by clinical terminologies on evidence generation, and quality and bias of underlying EHR data for evidence generation.