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Data-Driven Guideline Adherence in Data Representation and Compliance Measurement: Scoping Review.

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Summary

Measuring adherence to best practice standards in healthcare is challenging. This review synthesizes methods for computable representations of guidelines and adherence measurement, highlighting the need for context-aware approaches to ensure clinical relevance.

Keywords:
best practiceclinical decision supportclinical practice guidelinescomputer-interpretable guidelineselectronic medical recordsguideline compliance

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Area of Science:

  • Healthcare Informatics
  • Clinical Decision Support Systems
  • Health Services Research

Background:

  • Best practice standards aim to standardize care and improve patient outcomes.
  • Clinical practice variation exists, and not all deviations are inappropriate.
  • Measuring adherence to standards is challenging due to representation and data fidelity limitations.

Purpose of the Study:

  • To survey and synthesize literature on computable representation of guideline recommendations.
  • To explore methods for detecting and quantifying deviations from best practice standards.

Main Methods:

  • Scoping review following Arksey and O'Malley framework and PRISMA-ScR guidelines.
  • Searched five databases (Ovid Medline, EMBASE, IEEE Xplore, Web of Science, Scopus) in November 2025.
  • Included studies describing computable representations of standards or assessing adherence using patient data.

Main Results:

  • Twenty-four studies were included, with 58% measuring adherence.
  • Cardiovascular conditions were the most common focus (54%).
  • Standards were formalized using BPMN, ontologies, FHIR, or hybrid approaches; rule-based alignment was common for adherence measurement.
  • Most models lacked contextual sensitivity and patient-specific factors, leaving clinical warrant of deviations unresolved.

Conclusions:

  • Challenges persist in computer-interpretable representation and clinically meaningful adherence measurement.
  • Current approaches focus on technical alignment over clinical relevance, limited by data quality.
  • Need for context-aware, standardized modeling integrated into clinical workflows to distinguish warranted from unwarranted deviations for safer patient care.