Abstract
Quality measurement in pediatrics is challenged by a system that lacks a fundamental data infrastructure for children's healthcare in general and in particular in cardiomyopathy. We suggest that the thoughtful application of mixed health services research methods can serve as powerful tools for applied research that supports a priori thinking, which in turn can drive both prospective studies and the analyses of retrospectively collected data within the schemas of strong quasi-experimental designs. This can provide the means for transforming practice into evidence, and practice within uncertainty into deep knowledge. The process of learning is iterative and typically incremental, constantly being infused by every day work experience and hard-earned lessons by clinicians providing clinical care. Developing sustainable learning towards improved outcomes in pediatric cardiomyopathy can be done using the applied science of health services research to translate clinical practice into research.
| Original language | English |
|---|---|
| Pages (from-to) | 20-26 |
| Number of pages | 7 |
| Journal | Progress in Pediatric Cardiology |
| Volume | 49 |
| DOIs | |
| State | Published - Jun 2018 |
| Externally published | Yes |
Keywords
- Bayes theorem
- Cardiomyopathy
- Evidence
- Health services research
- Learning loops
- Learning systems
- Patient safety
- Quality
- Uncertainty