Development of computable guidelines: GIN McMaster guideline development checklist extension for computable guidelines

Abstract

Background: Transforming Clinical Practice Guideline (CPG) recommendations into computer readable language is a complex and ongoing process that requires significant resources, including time, expertise, and funds. The objective is to provide an extension of the widely used GIN-McMaster Guideline Development Checklist (GDC) and Tool for the development of computable guidelines (CGs). Methods: Based on an outcome from the Human Centered Design (HCD) workshop hosted by the Guidelines International Network North America (GIN-NA), a team was formed to develop the checklist extension. The team included guideline developers, researchers, implementers, and informaticists who reviewed the GDC and developed a list of additional requirements to help guideline developers author clearer, more implementable narrative guideline recommendations (referred to as knowledge level 1, or L1 recommendations) and ensure conformance-testable attributes of the different artifacts of clinical guideline recommendations. The team vetted this list with guideline development organizations and health informatics experts to validate it, for clarity, usability, and effectiveness. The team used an iterative process to determine the final extension components for CG development guidance. Results: The team identified 9 components that complement the topics included in GDC for developing, implementing, and adopting CG recommendations. Conclusion: This study demonstrates that the defined principles in the L1 Checklist, grounded in current guideline development standards, may significantly enhance the writing, development, and implementation of computable recommendations. Collaboration among guideline developers, implementers, and informaticists from the outset is crucial for achieving effective integration of these guidelines into clinical workflows. Future work should focus on assessing this extension within various ongoing learning initiatives and point-of-care digitization efforts, including the scholarly communications ecosystem and learning health systems, to further improve healthcare delivery.

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