Value-based reimbursement arrangements tie financial incentives to achieving quality measures to ensure savings are not from withholding care. For patients and their families, the delivery of high-quality care is simply the expectation. Defining and measuring pediatric quality, however, is not standardized which has led to a large proliferation of metrics across multiple stakeholders. The majority of these measures are process rather than outcomes metrics often chosen for the ease at which the data can be obtained. In order to drive greater value, outcomes measures should be preferentially selected. However, measuring outcomes in children presents multiple unique challenges. Compared to adults, children are generally healthier, their outcomes may take more time to manifest, and their clinical variability is greater. Another challenge is the amount of healthcare data being generated by providers, provider networks, payors, government agencies, and many others. This should help in understanding pediatric quality outcomes, but the massive volume of data requires new analytic tools. Artificial intelligence techniques such as machine learning offer faster, more precise, and larger scale evaluation of quality outcomes. Its implementation necessitates identifying expertise in the way of data scientists as well as additional infrastructure components to evaluate data governance, security, regulatory compliance, and ethics. Despite these prerequisites, much progress is being made in outcome insights that drive value benefiting children and families.
Copyright © 2021. Published by Elsevier Inc.