The next BIG healthcare lie: HEDIS -- Not a Quality Measure but a Faulty Metric

Introduction: For decades, the Healthcare Effectiveness Data and Information Set (HEDIS) has been positioned as a cornerstone of quality measurement in U.S. healthcare. With over 90% of health plans using HEDIS scores to evaluate performance, it’s often assumed that high HEDIS compliance equals high-quality care. But does it?

This article argues that HEDIS is not a true quality measure but a flawed metric that encourages surface-level compliance rather than meaningful improvement. By relying on uniform thresholds that disregard socioeconomic and structural barriers, HEDIS presents an incomplete—and often misleading—picture of healthcare quality. If we want to improve outcomes for all populations, particularly the underserved, we need to stop pretending that HEDIS compliance is a mark of quality and start challenging its place as the gold standard.

Section 1: Why HEDIS is Not a Quality Measure Quality improvement in healthcare isn’t just about meeting statistical benchmarks. Quality should mean continual improvement and better patient outcomes, greater equity, and closing gaps in care for vulnerable populations. A true quality measure would account for the context of care, recognizing that underserved populations face greater barriers and need targeted support to achieve equitable outcomes.

However, HEDIS doesn’t consider these nuances. Instead, it applies a blanket threshold—usually around 75%—for all populations, regardless of the vastly different starting points between affluent and low-income groups. This “one-size-fits-all” approach is not quality; it’s a faulty metric that values compliance over genuine improvement.

When a healthcare facility achieves high HEDIS scores, it’s tempting to see that as proof of quality. But in many cases, these scores only reflect how well a provider serves healthier, wealthier patients—those who are already likely to meet preventive care targets. This illusion of quality obscures the reality for high-need populations who continue to fall short on these metrics, not due to lack of effort but due to lack of resources, accessibility, and support.

Section 2: The Fallacy of Uniform Thresholds HEDIS’s thresholds are set high and uniformly applied, with the assumption that all patients have equal access to healthcare resources. In reality, these uniform targets overlook the structural challenges that underserved populations face, including lack of insurance, limited transportation, and socioeconomic factors that hinder healthcare access. A threshold of 75% vaccination coverage, for example, might be realistic in a well-resourced, suburban population but overly optimistic in a low-income, urban or rural community.

This failure to account for baseline differences doesn’t just limit effectiveness; it actively works against the interests of underserved populations. Healthcare providers who work with high-need, high-risk populations often find it impossible to meet HEDIS’s blanket thresholds, effectively penalizing them for serving vulnerable patients. Rather than incentivizing targeted improvements where they are most needed, HEDIS’s approach encourages providers to focus on healthier, more accessible patients who help drive up compliance numbers.

Section 3: The Real Cost of HEDIS Compliance – Widening Disparities By presenting a high HEDIS score as a mark of quality, we ignore the gaps in care for underserved populations and create a healthcare system that caters to the already healthy and well-resourced. This approach not only masks disparities but may actually exacerbate them. Providers are incentivized to focus on the “easier wins”—patients who are already likely to comply with preventive measures—instead of investing resources in harder-to-reach, high-risk groups who need care the most.

This approach leads to what could be called a “two-tiered” system:

• For wealthier, healthier patients, HEDIS creates a framework that encourages consistent, preventive care, resulting in higher compliance and better outcomes.
• For underserved patients, HEDIS leaves them behind, as providers may deprioritize these groups to avoid lower scores.

The irony is that while HEDIS claims to measure quality, its approach to metrics may be actively working against it, reinforcing existing inequalities rather than reducing them. A true quality measure should improve outcomes for all patients, especially those most in need of improvement.

Section 4: A Better Way to Define Quality – Moving Beyond Faulty Metrics If HEDIS compliance doesn’t equate to quality, then what does? To redefine quality, we need to go back to the fundamentals of quality improvement, focusing on measurable progress for all populations rather than on achieving uniform targets that may be unrealistic for certain groups.

A quality measure should reflect meaningful changes in patient health outcomes, not just arbitrary compliance rates. Here’s how we might start moving in that direction:

• Set Baseline-Specific Goals: Instead of a uniform threshold, quality metrics should establish goals based on baseline performance for each population segment. If underserved populations have lower vaccination rates, the goal should be to improve those rates incrementally rather than immediately expecting parity with higher-income groups. A provider who improves vaccination rates in an underserved community from 50% to 65% is achieving real progress, even if they fall short of the 75% HEDIS target.
• Reward Incremental and Targeted Improvements: Quality incentives should be tied to the provider’s ability to improve outcomes for at-risk groups, not just maintain compliance among already well-served populations. This could mean rewarding providers who show significant improvements within specific demographics, like uninsured children or low-income seniors, even if they don’t meet the overall HEDIS threshold.
• Include Socioeconomic and Structural Context: Quality measures need to account for the realities of access barriers and social determinants of health. Metrics could reward efforts that address these barriers, such as providing mobile clinics for rural areas or offering transportation support for low-income families. These efforts may have a greater impact on quality than simply meeting arbitrary compliance numbers.

Section 5: Practical Strategies to Measure True Quality To improve quality across all populations, healthcare providers and policymakers should consider adopting a more inclusive and realistic approach to quality measurement. Here are a few strategies that could lead to a more equitable and meaningful system:

• Community-Based Metrics: Focus on metrics that reflect engagement with underserved populations. For example, tracking how many patients in low-income zip codes have regular preventive care visits or participate in health education programs.
• Patient-Centered Quality Goals: Move away from the “one-size-fits-all” HEDIS benchmarks and toward goals that focus on patient-centered improvements. Metrics could include things like improved health literacy, engagement in chronic disease management, and better patient satisfaction in underserved groups.
• Flexible, Tiered Quality Incentives: Provide tiered incentives for providers who make measurable progress in underserved communities. Rewarding incremental improvements for the most vulnerable populations will drive more targeted quality improvement efforts.

Conclusion: Ending the Illusion of Quality Through HEDIS HEDIS has long held the reputation as a comprehensive measure of healthcare quality, but it’s time to question that reputation. If quality truly means better outcomes, equity, and meaningful improvement across all populations, then HEDIS’s approach to measurement is falling short. HEDIS compliance doesn’t equal quality; it’s a faulty metric that encourages surface-level compliance and often overlooks the very populations who need quality improvement most.

A shift in perspective is needed to focus on real quality improvement—one that values progress over compliance and measures success based on the impact on vulnerable populations. By moving away from arbitrary thresholds and toward metrics that genuinely reflect patient outcomes and healthcare equity, we can begin to build a system that serves everyone, not just those easiest to reach.

Closing Thoughts: As healthcare evolves, it’s time to leave behind metrics that mislead us about quality and replace them with measures that address the complex realities of patient care. HEDIS’s uniform thresholds and compliance-based approach may be creating more illusions than solutions. By challenging the status quo, we can push for a future where quality measurement genuinely reflects quality care, not just numbers on a report.