Lichtner Gregor, Alper Brian S, Jurth Carlo, Spies Claudia, Boeker Martin, Meerpohl Joerg J, von Dincklage Falk
Universitätsmedizin Greifswald, Department of Anesthesia, Critical Care, Emergency and Pain Medicine, Greifswald, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany.
Computable Publishing LLC, Ipswich, MA, USA; Scientific Knowledge Accelerator Foundation, Ipswich, MA, USA.
J Biomed Inform. 2023 Mar;139:104305. doi: 10.1016/j.jbi.2023.104305. Epub 2023 Feb 3.
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 format that uses computer-interpretable representations of the knowledge artifacts that emerge during the process of evidence-based guideline development to directly serve as the basis of evidence-based recommendations.
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. We then 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 FHIR resources developed for evidence-based medicine (EBMonFHIR). The resulting recommendations were modelled and represented in conformance with the FHIR Clinical Guidelines (CPG-on-FHIR) implementation guide.
The information model of evidence-based clinical guideline recommendations and its EBMonFHIR-/CPG-on-FHIR-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 the knowledge artifacts generated during evidence-based guideline development and their re-use as the basis for recommendations and used the profiles to implement an exemplary clinical guideline recommendation.
The FHIR implementation guide presented here can be used to directly link the evidence assessment process of evidence-based guideline recommendation development, i.e. systematic reviews and evidence grading, and the underlying evidence from primary studies to the resulting guideline recommendations. This not only allows the evidence on which recommendations are based on to be evaluated transparently and critically, but also enables guideline developers to leverage computable evidence in a more direct way to facilitate the generation of computer-interpretable guideline recommendations.
已经开发了各种形式主义来以计算机可解释的方式表示临床实践指南建议。然而，现有的形式主义都没有利用基于证据的指南制定过程中产生的结构化和可计算信息。因此，我们在此提出一种基于FHIR的格式，该格式使用基于证据的指南制定过程中出现的知识工件的计算机可解释表示，直接作为基于证据的建议的基础。
我们确定了表示基于证据的临床实践指南建议所需的信息，并审查了基于证据的指南制定过程中出现的知识工件。然后，我们与领域专家进行了基于共识的设计过程，以开发一个用于指南建议表示的信息模型，该模型在结构上与基于证据的指南建议制定过程保持一致，并开发了一个基于FHIR资源的相应表示，用于循证医学（EBMonFHIR）。最终的建议按照FHIR临床指南（CPG-on-FHIR）实施指南进行建模和表示。
基于证据的临床指南建议的信息模型及其基于EBMonFHIR/CPG-on-FHIR的表示包含各个指南建议的临床内容、建议的一组元数据、建议的评级（例如，建议强度、总体证据的确定性）、各个结果的证据确定性评级（例如，偏倚风险）以及与基础证据（基于原始研究的系统评价）的链接。我们为表示基于证据的指南制定过程中产生的知识工件及其作为建议基础的重用所需的所有FHIR资源创建了概要文件和实施指南，并使用这些概要文件实施了一个示例性临床指南建议。
此处介绍的FHIR实施指南可用于直接将基于证据的指南建议制定的证据评估过程，即系统评价和证据分级，以及来自原始研究的基础证据与最终的指南建议联系起来。这不仅允许对建议所基于的证据进行透明和严格的评估，还使指南制定者能够以更直接的方式利用可计算证据，以促进生成计算机可解释的指南建议。
