Value-based care represents one of health care’s most important transformations, driving hospitals and health systems to focus on outcomes that truly matter. To succeed in this model, organizations must track and improve their performance metrics continuously, making data health care’s most valuable currency. It is the foundation for improving patient care, reducing complications, and optimizing resource allocation. Yet despite the growing volume of performance metrics being collected, a critical gap persists: This data rarely reaches the clinicians who need it most, at the moment they need it.
Value-based care has established the right goals. How performance data flows through health care organizations, that is where we are falling short. While a handful of stakeholders understand institutional performance metrics and quality benchmarks, this knowledge rarely reaches clinicians providing direct care in an actionable form. Take a typical quality standard document: three or four pages of dense regulatory language that ultimately require just a handful of concrete actions by a clinician. The care itself might be straightforward. But the infrastructure surrounding it obscures what matters most at the point of care. Administrative staff become fluent in the specialized language of performance metrics, while frontline clinicians often remain unsure how institutional benchmarks translate into their daily decisions. The data exists, but it is trapped in dashboards and quarterly reports rather than embedded in clinical workflows.
The disconnect between data and the bedside
This disconnect between data collection and data utilization creates real consequences. Under value-based models, accurate documentation within specific timeframes is essential, not as bureaucracy for its own sake, but because capturing the right diagnoses and conditions ensures appropriate resource allocation and enables accurate outcome tracking. A perioperative team may deliver excellent care overall, but if one team member misses a time-sensitive action, such as checking blood glucose levels, administering medication, or documenting a pre-existing condition, the entire chain breaks down and quality metrics suffer. Clinicians need better tools to capture the right information at the right time without adding cognitive burden. Yet doctors today are responsible not only for treating patients but also for mastering data entry, regulatory requirements, and administrative workflows. The toll of this shift shows up in burnout rates and the steady exit of clinicians from the workforce.
The evidence-to-practice gap: A lesson from ERAS
Enhanced Recovery After Surgery (ERAS) protocols illustrate this evidence-to-practice gap clearly. The evidence behind ERAS is strong: shorter hospital stays, fewer complications, lower readmission rates, and significant cost savings. Despite this, U.S. adherence rates sit at around 55 percent. This low adoption is not because clinicians reject better outcomes or hospitals oppose savings. The barrier is simpler: Knowing a protocol works is entirely different from having the infrastructure to implement it consistently across every surgical case. Without tools that translate evidence into action at the point of care, even the most well-established protocols remain underutilized.
Death by a thousand measures
The proliferation of quality measures, each well-intentioned, has created what clinicians describe as “death by a thousand measures.” These metrics track outcomes that genuinely matter for patient safety and recovery, so the pressure makes sense. But when quality data arrives as monthly reports or quarterly dashboards rather than real-time clinical decision support, it adds work without adding value. We need proactive intervention at the point of care, not retroactive reporting.
Bridging the gap with AI
Health care technology companies have pitched AI and automation as part of the answer, and they are partly right. The most promising AI applications do not just collect more data; they democratize access to existing performance insights and translate institutional priorities into individualized clinical guidance. Ambient documentation tools can reduce clerical burden, but the real opportunity lies in AI that connects quality data to frontline decision-making, shortening the lag between measurement and intervention.
The quality metrics being tracked today largely align with outcomes patients care deeply about: avoiding infections, reducing complications, shortening recovery times, and preventing readmissions. Instead, making that measurement meaningful at the point of care is where we need to focus. Real improvement means designing systems that make adherence to evidence-to-practice protocols easier, not harder, giving clinicians the right information at the right time rather than burying them in retroactive reports.
Operationalizing the promise
Value-based care has established the right goals; now we need the infrastructure to operationalize them. When performance data is democratized and accessible where care happens, when AI can identify specific clinical scenarios requiring intervention, and when real-time feedback replaces quarterly dashboards, we can finally deliver on the promise of continuous quality improvement. The gap between executive dashboards and bedside decision-making is not inevitable. Solving this problem is how we will make value-based care work as intended: transforming data from a reporting burden into a clinical asset, and shortening the evidence-to-practice gap so that what we know works actually gets implemented consistently.
Ido Zamberg is founder and chief medical officer of C8 Health and a research fellow at McGill University Health Centre. He is a trained medical professional specializing in anesthesia and internal medicine. In addition to his clinical training, Dr. Zamberg is a computer scientist with a decade-long career as a developer at companies including HP and Autodesk. His research and academic publications are available on ResearchGate.
