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Pediatric quality measures: The leap from process to outcomes
1EVP/Chief Strategy and Innovation Officer, Children's Mercy Kansas City, United States.
Current Problems in Pediatric and Adolescent Health Care
|September 14, 2021
Summary
Value-based care uses quality measures to ensure patient savings. Artificial intelligence can improve pediatric quality outcome measurement, despite unique challenges in children
Area of Science:
- Healthcare Quality Improvement
- Pediatric Health Outcomes
- Health Informatics
Background:
- Value-based reimbursement models link financial incentives to quality metrics.
- Standardizing pediatric quality measurement is challenging, leading to numerous metrics.
- Current metrics often focus on processes rather than outcomes due to data accessibility.
Purpose of the Study:
- To explore the challenges and opportunities in measuring pediatric quality outcomes.
- To highlight the potential of artificial intelligence in analyzing pediatric healthcare data.
- To advocate for a shift towards outcome-based quality measures in pediatrics.
Main Methods:
- Review of current pediatric quality measurement practices.
- Discussion of challenges in measuring pediatric outcomes (health, time, variability).
- Exploration of artificial intelligence (AI) and machine learning (ML) for analyzing large healthcare datasets.
Main Results:
- Pediatric outcome measurement faces unique hurdles compared to adults.
- Massive healthcare data volumes necessitate advanced analytical tools like AI/ML.
- AI/ML can enable faster, more precise, and large-scale evaluation of quality outcomes.
Conclusions:
- Outcome measures are crucial for driving value in pediatric care.
- AI and machine learning offer promising solutions for complex pediatric data analysis.
- Addressing data governance, security, and ethics is essential for AI implementation in pediatric quality assessment.
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