Rheumatoid nodules: differential diagnosis and immunohistological findings.

Abstract

<h3>Abstract</h3> <h3>Background</h3> Various formalisms have been developed to represent clinical practice guideline recommendations in a computer-interpretable way. However, none of the existing formalisms leverage the structured and computable information that emerge from the evidence-based guideline development process. Thus, we here propose a FHIR-based guideline representation format that is structurally aligned to the knowledge artifacts emerging during the process of evidence-based guideline development. <h3>Methods</h3> We identified the information required to represent evidence-based clinical practice guideline recommendations and reviewed the knowledge artifacts emerging during the evidence-based guideline development process. Then we conducted a consensus-based design process with domain experts to develop an information model for guideline recommendation representation that is structurally aligned to the evidence-based guideline recommendation development process and a corresponding representation based on evidence-based medicine (EBM)-on-FHIR resources. <h3>Results</h3> The information model of clinical practice guideline recommendations and its EBMonFHIR-based representation contain the clinical contents of individual guideline recommendations, a set of metadata for the recommendations, the ratings for the recommendations (e.g., strength of recommendation, certainty of overall evidence), the ratings of certainty of evidence for individual outcomes (e.g., risk of bias) and links to the underlying evidence (systematic reviews based on primary studies). We created profiles and an implementation guide for all FHIR resources required to represent a complete clinical practice guideline and used the profiles to implement an exemplary clinical guideline recommendation. <h3>Conclusions</h3> Our EBMonFHIR-based representation of clinical practice guideline recommendations allows to directly link the evidence assessment process through systematic reviews and evidence grading, and the underlying evidence from primary studies to the resulting guideline recommendations. This not only allows to evaluate the evidence on which recommendations are based on transparently and critically, but also allows for a more direct and in future automatable way to generate computer-interpretable guideline recommendations based on computable evidence.