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Defining quality indicators for heart failure in general practice
Marie Smets1, Miek Smeets1, Steve Van den Bulck1
1a Department of Public Health and Primary Care, KU Leuven , Leuven , Belgium.
Insights
This study identified 19 quality indicators (QIs) from electronic health records to monitor heart failure (HF) care in Belgian general practices. These QIs will help evaluate and improve the quality of HF patient management.
Area of Science:
- Cardiology
- General Practice
- Health Services Research
Background:
- Quality indicators (QIs) are crucial for assessing healthcare quality.
- A lack of specific QIs for heart failure (HF) in Belgian general practice necessitates their development.
- Electronic health records (EHRs) offer a potential data source for these QIs.
Framework:
- The RAND/UCLA appropriateness method, a modified Delphi approach, was employed.
- A literature review generated potential QIs, refined using the SMART principle to 25 QIs.
- An expert panel of cardiologists, general practitioners, and HF nurses evaluated QI appropriateness over three rounds.
Implementation:
- Initial review identified 20 appropriate and 5 uncertain QIs.
- Subsequent rounds refined the list, resulting in 19 appropriate QIs.
- The top three QIs focused on HF etiology identification, ejection fraction differentiation, and guideline-based medication.
Implications:
- Nineteen EHR-extractable QIs for general practice HF care were successfully identified.
- These QIs provide a foundation for qualitative monitoring of heart failure management.
- The developed QIs can enhance the quality of care for HF patients in primary care settings.
Abstract:
Background: Quality indicators (QIs) are used to measure and evaluate quality of care. However, QIs to evaluate care for HF patients in general practice in Belgium are lacking. Therefore, this study aimed to determine which QIs, rooted in the electronic health record (EHR), are useful to monitor quality of care for patients with HF in general practice. Methods: The RAND/UCLA appropriateness method (a modified Delphi method) was used to define these assigned QIs. First, a literature review was performed to generate a list of possible QIs for HF. Second, by applying the SMART principle, 25 QIs were withheld. Third, an expert panel of health care providers experienced in HF (cardiologists, general practitioners and HF nurses) was convened. Finally, the panellists rated the QIs for appropriateness in three rounds. Results: The withheld QIs highlighted diverse aspects of HF care. In round 1, 20 of the 25 QIs were considered appropriate, and five were considered uncertain. In round 2, 19 QIs were rated appropriate, four inappropriate, and two uncertain. In round 3, the final 19 appropriate QIs were ranked to form a top 10. The top three began with the identification of the aetiology of HF, continued with the differentiation between HF with preserved and reduced ejection fraction, and concluded with the treatment of HF with an ACE-I and a β-blocker in third place. Conclusion: In this study, 19 QIs for HF in general practice, extractable from the EHR, were identified. These QIs should enable the qualitative monitoring of HF care.
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