Risk-Adjusting for Pediatric Populations

Successfully managing pediatric populations requires health systems to develop risk-adjustment capabilities to inform value-based contracting strategies, program development, resource allocation, and outcomes measurement. The good news: risk-adjustment methodologies, software interfaces, and management programs have existed for decades, largely driven by shifts to managed care in adult populations. The challenge: these methodologies, interfaces, and programs are weakest in their ability to predict risk for kids.

How do we define “risk?”

Risk adjustment in healthcare is essentially a predictor of medical service utilization over a defined period. Inputs to risk-scoring models are generated either exclusively or predominantly from past healthcare utilization. This information is rolled up in a set taxonomy (e.g., Diagnostic Risk Groupers, or DRGs) and weighted by additional factors including severity or comorbidities. The resulting risk score reflects projected utilization relative to the average (1.0). The primary data sources for this calculation are demographic and claims data.

Why does it matter?

Risk-adjustment methodologies are only modestly successful in accurately predicting utilization. The most common risk-adjustment methodologies explain only 10 to 30 percent of future utilization in a given population. In other words, the model’s data (demographic and claims data) explain only 10 to 30 percent of future healthcare utilization. What factors predict the remaining 70 to 90 percent of utilization? Intuitively, we might guess the answer is some combination of social and economic determinants, health literacy, patient activation and compliance, and other factors.

Another important limitation of risk-adjustment methodologies is the tendency to significantly under-predict utilization for more complex, chronic, or acute patients. This matters because these individuals often use medical care frequently and have high annual spending. Yet, because of this, these populations often lend themselves well to population health management approaches.

While some information is better than none, proceed with caution when using risk-adjustment data to design population health programs and contracts. This caution matters even more when designing pediatric population health programs and contracts.

Why are methodologies so limited in their applicability to pediatric populations? The answer is threefold.

1. The perennial caution that kids are not small adults holds true for risk-adjustment analysis. Recent studies estimate that children account for less than 10% of annual health care spending. This disproportion carries through risk-adjustment analyses, which focus on adult conditions, health experience, and disease or injury progression that are incongruous with, or absent in, pediatric populations. It is the rare child who requires knee or hip replacements, or who receives a diagnosis of congestive heart failure. As a result, sensitivity in recognizing risk and accuracy in predicting utilization in pediatric populations are significantly limited.
2. Thankfully, most kids are well, most of the time. Most children use medical care for minor acute episodes or to manage pediatric chronic disease. Prospective risk methodologies are particularly challenging for children because of natural regression to the mean. If a child broke his arm last year, that does not increase the likelihood of breaking another bone this year. Pediatric risk adjustment is most applicable to children with special health care needs, who frequently require medical intervention and management.
3. Claims data are necessary but insufficient in predicting kids’ medical utilization. Population health research shows that social factors, physical environments, and behaviors, our own and those of those around us, have a much greater impact on health outcomes than factors like medical utilization. We find this to be particularly true for pediatric populations, whose development makes them particularly sensitive to social, environmental, and behavioral influences. Claims data is a powerful tool for understanding medical utilization, but it does not yet meaningfully extend to measuring social determinants of health. And, by extension, no risk-adjustment methodologies have found a way to meaningfully incorporate and weight social determinants of health into their calculations.

Genesis Health Consulting specializes in selecting and guiding investments in child health to generate measurable returns in the near and long term. To learn more about how Genesis Health Consulting can support your goals, visit our website or contact us.