Automated Monitoring of Adherence to Evidenced-Based Clinical Guideline Recommendations: Design and Implementation...

Area of Science:

• Health Informatics
• Clinical Decision Support Systems
• Medical Software Development

Background:

• Clinical practice guidelines (CPGs) are crucial for optimizing patient care.
• Effective CPG implementation requires healthcare personnel to recognize applicable recommendations in real-time.
• Gaps in CPG adherence can be addressed by automated monitoring systems.

Purpose of the Study:

• To gather requirements for a system monitoring adherence to evidence-based clinical guidelines for individual patients.
• To design and implement a software prototype integrating CPGs with patient data.
• To demonstrate the prototype's utility in providing treatment recommendations.

Main Methods:

• Work process analysis with intensive care clinicians to model guideline adherence monitoring.
• Consensus-based requirements analysis with diverse stakeholders (clinicians, developers).
• Development of a modular software architecture and a functional prototype.

Main Results:

• A system was designed to integrate CPGs with real-time clinical data for adherence evaluation.
• Four core requirements identified: applicability assessment, data integration, raw data display, and FHIR-based guideline representation.
• A functional prototype was developed and tested using COVID-19 treatment recommendations.

Conclusions:

• The developed system enhances individual patient treatment and hospital quality management.
• Further studies are needed to assess patient outcome impact and resource effectiveness.
• An open-source modular architecture facilitates collaborative development and independent expert contributions.