As a physician working in value-based care at Pearl Health, I’ve witnessed firsthand how well-intentioned quality metrics have become a perverse incentive system that drives up costs and potentially erodes the doctor-patient relationship. The Healthcare Effectiveness Data and Information Set (HEDIS) measures, designed to improve care quality, now routinely incentivize overscreening, overtesting, and checkbox medicine at the expense of individualized, longitudinal care. The evidence is overwhelming: our current quality measurement system is making healthcare worse, not better.
At Pearl Health, we’ve developed a fundamentally different approach—one that identifies patients with true urgency rather than those who simply haven’t checked a box. Our Signal-Action Framework transforms reactive checkbox medicine into proactive, relationship-based care that actually works within existing programs like Medicare Advantage.
The most damaging aspect of HEDIS measures is the one-size-fits-all approach that ignores patient complexity, life expectancy, and individual circumstances. This isn’t just a theoretical concern—it’s causing measurable harm to the patients we’re trying to help. Recent research from Annals of Internal Medicine reveals that among women aged 70–74 diagnosed with breast cancer via screening mammography, 31% would be overdiagnosed—meaning they receive unnecessary treatment for cancers that would never cause symptoms. This percentage jumps to 47% for women aged 75–84 and exceeds 50% for women with life expectancy less than five years.
The overscreening epidemic
The HEDIS breast cancer screening measure exemplifies how quality metrics can drive inappropriate care. Thirty percent of women aged 75 and older received mammograms in 2010–2012 despite limited evidence of benefit (National Cancer Institute, 2023), because providers face no penalty for “overscreening”—meeting targets through excessive screening is rewarded equally with appropriate screening. These elderly women then undergo unnecessary surgeries, radiation therapy, and chemotherapy, leading to functional decline without mortality benefit.
Colonoscopy screening presents similar problems. Meta-analysis research published in the Journal of General Internal Medicine found that 17% to 25.7% of screening colonoscopies are performed more frequently than indicated or without adequate justification. HEDIS measures don’t penalize early repeat screenings, creating perverse incentives that drive costs up while potentially harming patients through unnecessary procedures and complications.
The diabetes care metrics reveal another troubling pattern. While the American Diabetes Association recommends A1C testing 2–4 times per year based on glycemic control, HEDIS measures focus on most recent test results without upper limits on testing frequency. This drives excessive A1C testing beyond clinical necessity as more testing increases the odds of a point-in-time lower measurement. This ultimately risks diverting resources from comprehensive diabetes management that would be likelier to improve patient outcomes.
The staggering cost of compliance
The financial burden of quality reporting is breathtaking. Johns Hopkins Hospital spends $5.6 million annually on quality reporting alone—162 unique metrics requiring 108,478 person-hours of work (JAMA, 2023). Extrapolated nationally across 4,100+ US hospitals, this represents billions of dollars annually diverted from direct patient care to administrative compliance.
Primary care practices fare no better. Medical practices spend 785 hours per physician per year dealing with external quality measures, costing $15.4 billion annually for primary care quality reporting nationwide. That’s equivalent to caring for an additional 9 patients per week that never happens because physicians are instead documenting compliance with metrics that may not improve patient outcomes.
The administrative burden has reached absurd levels. Chart-abstracted metrics cost $33,871 per metric per year. Electronic metrics, a more efficient option, still cost $1,902 per metric per year. These costs represent pure overhead that adds no clinical value while consuming resources that could fund direct patient care. We prefer claims-based metrics, as they can be calculated centrally, but they still must be crafted to target global care.
Physician burnout and the erosion of medical judgment
The human cost of metric-driven care extends far beyond dollars. Physician burnout increased from 45.5% in 2011 to 54.4% in 2014 during the height of quality measure deployment (deteriorating further in the post-COVID era), with quality reporting requirements being a major contributing factor (Mayo Clinic Proceedings, 2015). Physicians now spend 1.77 hours per day on documentation outside office hours (JAMA Internal Medicine, 2022), with nearly 2 hours of EHR and desk work required for every hour of direct patient care (Annals of Internal Medicine, 2016).
This administrative burden creates moral distress as physicians are forced to prioritize documentation over patient interaction. Studies show that 47% of employed physicians adjust treatment options to reduce costs based on practice policies or quality metric incentives, while 61% report moderate or no autonomy to make referrals outside their practice system. We’ve created a system where physicians spend more time documenting care than providing it.
The most insidious effect is how quality metrics fragment the doctor-patient relationship. Research published in JAMA Internal Medicine found that higher patient satisfaction was associated with 26% higher mortality risk and 8.8% higher total healthcare expenditures. This counterintuitive finding reflects how metric-driven approaches often satisfy patients’ immediate desires while undermining long-term health outcomes. This occurs because of an emphasis on short-term satisfaction, which is often at odds with longitudinal evidence-based care.
The relationship-based care alternative
The evidence demonstrates that longitudinal, relationship-based care produces better outcomes at lower costs. However, HEDIS measures are often misaligned with this approach, by rewarding episodic, metric-focused encounters over comprehensive, continuous care. Primary care practices report that quality metric requirements disrupt physicians’ habitual patient encounter patterns, replacing the patient’s agenda with one of closing quality reporting gaps (Annals of Family Medicine, 2017).
Systematic reviews show that fragmented care is associated with increased emergency department visits, higher healthcare costs, and worse chronic illness management (BMC Medicine, 2023). Yet our current quality measurement system incentivizes exactly this type of fragmentation by focusing on discrete, measurable actions rather than integrated, relationship-based care.
The most vulnerable patients suffer most. Quality metrics create “dumping behavior” where complex, high-risk patients are avoided due to their negative impact on provider quality scores. When treating homeless patients with uncontrolled diabetes, recording smoking history for quality metrics takes precedence over addressing food insecurity and housing needs—the very social determinants that drive poor health outcomes.
A proven alternative: The Pearl Signal-Action Framework
At Pearl Health, we’ve developed a fundamentally different approach that solves the quality metrics paradox. Our Signal-Action Framework uses predictive analytics and machine learning to identify patients with true urgency—those at risk for preventable admissions, ED visits, and low-yield specialty consultations—rather than those who simply haven’t checked a box.
The framework works by combining multiple data sources (claims, EMR, ADT feeds, social determinants) to surface actionable insights about patient risk. Instead of flagging every patient due for a screening, Pearl’s algorithms identify which patients genuinely need proactive intervention. For example, our preventable ED visit model performs at least 30% better than the commonly-used clinical rule of targeting frequent ED users, and our transitional care management insights have demonstrated $3,000 in savings per discharge through reduced readmissions.
Most importantly, Pearl’s approach works within existing metric-based programs like Medicare Advantage and ACO REACH. We don’t ignore quality metrics—we transcend them by focusing on what actually drives outcomes. Our partner practices achieve quality scores while simultaneously reducing total cost of care because better outcomes and lower costs always win.
The urgency revolution
Pearl’s Urgency Score algorithm represents a paradigm shift in patient prioritization. Rather than treating all “care gaps” equally, the system calculates urgency based on:
- Clinical complexity and severity scores
- Time sensitivity of interventions
- Predicted impact on outcomes and costs
- Historical utilization patterns and risk factors
This creates a dynamic, constantly updating view of which patients need attention most urgently. A frail elderly patient recently discharged with multiple chronic conditions rises to the top—not because they missed a mammogram, but because timely intervention could prevent a $15,000 readmission.
The results speak for themselves. In one case study, Pearl’s predictive insights helped a patient reduce ED visits by 67% within six months, saving over $5,000 per prevented visit through proactive primary care management. This isn’t about adding more work—it’s about focusing physician time where it matters most. And the technology is advancing rapidly, with emerging AI tools boosting predictive accuracy, netting ever better outcomes and lower costs.
Succeeding within the system while transforming it
The beauty of Pearl’s approach is that it enhances performance in existing value-based programs rather than fighting against them. Medicare Advantage plans still get their HEDIS scores, but practices achieve them through intelligent prioritization rather than mindless checkbox completion.
By identifying actionable opportunities for better outcomes—fewer admissions, reduced ED utilization, appropriate specialist referrals—Pearl-enabled practices naturally improve quality metrics as a byproduct of better care, not as the primary goal. This alignment of incentives means practices can:
- Reduce administrative burden by focusing on high-impact activities
Improve patient outcomes through proactive intervention
Maximize shared savings in value-based contracts
Restore physician autonomy and clinical judgment
The Signal-Action Framework proves that we don’t have to choose between meeting metrics and providing good care. When technology surfaces the right insights at the right time, physicians can practice medicine the way it should be practiced—with the patient, not the metric, at the center.
The path forward is here
The current system is broken, but the solution already exists. Pearl Health’s approach demonstrates that we can work within metric-based programs while fundamentally transforming how we deliver care. By replacing checkbox medicine with intelligent, urgency-based prioritization, we can achieve what everyone wants: better outcomes at lower costs.
The evidence is clear, and the technology is proven. It’s time to move beyond the quality metrics paradox and embrace a future where meeting benchmarks means actually improving patient care. At Pearl Health, we’re not waiting for the system to change—we’re changing how we work within it, one urgent patient at a time.
Learn more about how Pearl Health is transforming primary care at pearlhealth.com.
