Diagnosis-Driven, Cross-Disciplinary QA System for Coronary Artery Disease-Study Protocol

Fakhrah Maryam Iqbal1, Max Geraedts1, Limei Ji1

  • 1Institute for Health Services Research and Clinical Epidemiology, Marburg University (UMR), Marburg, Germany.

Insights

This study proposes a new diagnosis-based quality assurance framework for coronary artery disease (CAD) care, shifting from procedure-specific assessments to a patient-centered approach for better treatment decisions and outcomes.

Area of Science:

  • Cardiovascular Medicine
  • Health Services Research
  • Quality Improvement

Background:

  • Percutaneous Coronary Intervention (PCI) and Coronary Artery Bypass Grafting (CABG) are treatments for coronary artery disease (CAD).
  • Current German quality assurance (QA) is procedure-specific, not patient-centered or diagnosis-oriented.
  • Existing QA lacks a holistic approach to individualized treatment selection for CAD.

Purpose of the Study:

  • To develop a cross-disciplinary, diagnosis-based QA framework for CAD patients undergoing elective PCI or CABG.
  • To shift from procedure-specific to patient-centered, diagnosis-oriented quality assessment.
  • To optimize individualized treatment selection and improve outcome attribution in CAD care.

Main Methods:

  • Mixed-methods study combining qualitative and quantitative approaches.
  • Formulation of preliminary evidence-based Quality Indicators (QI) using qualitative methods.
  • Secondary data analyses for QI prioritization, modeling, and evaluation, followed by a consensus process.

Main Results:

  • Development of a novel QA framework integrating guideline recommendations and enhanced data linkage.
  • Standardization and improvement of QA procedures across CAD care pathways.
  • Integration of clinical expertise and real-world data to enhance patient outcomes.

Conclusions:

  • A patient-centered, diagnosis-based QA framework is proposed for coronary artery disease care.
  • The framework aims to improve treatment decisions, transparency, and standardization in quality assessment.
  • Integration of guidelines, expert input, and real-world data enhances CAD care quality.

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