Key result
Large clinical and administrative databases in pediatric cardiac disease enable collaborative learning and quality improvement to minimize variability in mortality, morbidity, and costs.
Highlights the importance of large databases and collaborative learning for quality improvement in pediatric congenital heart disease care.
May support database-enabled QI in pediatric cardiac care; leaves open prospective validation of mortality and cost reductions.
Large data sets have now become ubiquitous in clinical medicine; they are particularly useful in high-acuity, low-volume conditions such as congenital heart disease where data must be collected from many centers. These data fall into 2 categories: administrative data arising from hospital admissions and charges and clinical data relating to specific diseases or procedures. In congenital cardiac diseases, there are now over a dozen of these data sets or registries focusing on various elements of patient care. Using probabilistic statistic matching, it is possible to marry administrative and clinical data post hoc using common elements to determine valuable information about care patterns, outcomes, and costs. These data sets can also be used in a collaborative fashion between institutions to drive quality improvement (QI). Because these data may include protected health information (PHI), care must be taken to adhere to federal guidelines on their use. A fundamental principle of large data management is the use of a common language and definition (nomenclature) to be effective. In addition, research derived from these information sources must be appropriately balanced to ensure that risk adjustments for preoperative and surgical factors are taken into consideration during the analysis. Care of patients with cardiac disease both in the United States and abroad consistently shows wide variability in mortality, morbidity, and costs, and there has been a tremendous amount of discussion about the benefits of regionalization of care based on center volume and outcome measurements. In the absence of regionalization, collaborative learning techniques have consistently been shown to minimize this variability and improve care at all centers, but before changes can be made it is necessary to accurately measure accurately current patient outcomes. Outcomes measurement generally falls under hospital-based QI initiatives, but more detailed analysis and research require Institutional Review Board and administrative oversight. Cardiac anesthesia providers for these patients have partnered with the Society of Thoracic Surgeons Congenital Heart surgeons to include anesthesia elements to help in this process.
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Vener et al. (2016) conducted a review in Pediatric cardiac disease. Large clinical and administrative databases in pediatric cardiac disease enable collaborative learning and quality improvement to minimize variability in mortality, morbidity, and costs.
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