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

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.