Apr 09, 2026
By Chris Mansi, MD, CEO, Viz.ai and Andrew M. Ibrahim, Chief Clinical Officer, Viz.ai
At a recent closed-door session at the World Economic Forum in Davos, Dr. Mehmet Oz joined leaders from Kaiser Permanente, Mayo Clinic, Viz.ai and global life sciences companies to discuss a shared challenge: how to put patients first while reducing waste, variation, and cost. The conversation quickly converged on a familiar execution gap in U.S. healthcare—one that policy alone has not been able to close: variability.
Variability is the enemy of good outcomes. When a patient arrives at a rural hospital with early signs of stroke, the guidelines are clear – two million neurons are dying each minute, so get that patient to treatment as quickly as possible. But variability in medical knowledge, workflow and care coordination burns up time, and therefore the brain. Imaging must be reviewed, specialists engaged, and transfer decisions made—often across long distances—before the patient deteriorates. When variability increases, outcomes worsen and costs climb.
This gap between what medicine knows and what health systems can reliably deliver is exactly what the Centers for Medicare and Medicaid Services (CMS) is confronting as it advances initiatives such as the Rural Health Transformation program. Expanding access and stabilizing rural hospitals are essential goals. But access alone does not ensure timely, appropriate care for time-sensitive and high-cost conditions. Without better coordination and escalation, patients can still fall through the cracks.
That is where AI healthcare technology offers a simple and effective solution.
AI Care Pathways address the most persistent challenges in healthcare delivery: variability. These systems read real-time clinical, EHR, and imaging data to identify disease, alert the right clinicians, and activate an evidence-based sequence of next best actions. The goal is not to replace clinical judgment but to ensure that clinicians have the right information, the right team, and the right next steps—without delay, and reduce the operational friction inherent in healthcare.
What was once possible only in large academic medical centers is now achievable at scale across community and rural settings. AI Care Pathways support time-sensitive emergencies such as stroke, heart attack, and pulmonary embolism, as well as more complex care journeys like cancer diagnosis and chronic respiratory disease. Clinical guidelines for these conditions have existed for years. What has been missing is the infrastructure to apply them consistently for every patient, across geographically dispersed health systems.
That kind of operational reliability aligns directly with CMS’s priorities: improving outcomes for high-cost conditions, expanding access without worsening workforce shortages, supporting value-based care, and reducing waste without adding administrative burden.
Today, too much Medicare and Medicaid spending fails to translate into better outcomes. Some excess costs stem from care that does not adhere to established guidelines. Others reflect fragmented accountability or uncertainty that drives unnecessary admissions, transfers, and testing. Traditional oversight mechanisms—audits, reporting requirements, and retrospective reviews—are increasingly strained by the scale and complexity of federal programs.
What is often missing from policy discussions is a delivery-level solution that improves care quality as decisions are being made, not months later. AI Care Pathways fill that gap by embedding clinical standards, coordination logic, and documentation directly into the flow of care.
This approach is already being deployed across the country. Platforms like Viz.ai analyze EHR, imaging and clinical data across hospitals to identify patients with serious conditions and activate coordinated care pathways within minutes. More important than detection alone is what follows: standardized escalation criteria, clear accountability, and defined next steps. That structure reduces delayed treatment, avoids unnecessary downstream utilization, and improves consistency—particularly in settings where specialist access is limited. For conditions like stroke, even a few minutes saved upfront in the patient’s care can lead to significantly better outcomes with shorter hospital stays, lower rehabilitation needs after discharge and ultimately lower costs.
Overutilization is often discussed cautiously in Washington, but it remains a central CMS concern for good reason. Many unnecessary admissions, transfers, and procedures are not driven by bad actors. They are driven by uncertainty and uneven access to expertise. When clinicians cannot rely on rapid coordination or specialist input, the safest choice often appears to be doing more.
AI Care Pathways reduce that uncertainty. When detection, triage, and escalation are automated and consistent, clinicians can act earlier and with greater precision. Patients who need higher-acuity care receive it faster. Patients who do not are less likely to enter costly cascades of low-value testing, treatment and transfers. This is utilization management through better medicine—not blunt controls or rationing.
There are also implications for program integrity. CMS processes more than a billion claims annually and reported roughly $93 billion in improper payments across Medicare and Medicaid programs in fiscal year 2025. Many of these payments stem from insufficient documentation or fragmented processes rather than intentional wrongdoing. This reflects a misalignment between care delivery, documentation, and payment.
AI Care Pathways operate upstream. By aligning clinical triggers, standardized workflows, and documentation at the moment care decisions are made, they reduce ambiguity around what constitutes appropriate care and generate traceable records as a byproduct of care delivery—strengthening program integrity without increasing administrative burden.
CMS has been clear that the future of Medicare depends on smarter oversight, not more bureaucracy. AI Care Pathways offer a rare opportunity to align quality, cost control, access, and integrity within the same infrastructure.
These technologies are not a cure-all. But they directly address the execution gap that has limited healthcare reform for decades. If the goal is better care delivered with greater consistency and integrity, this is an area where policy and practice can finally move together.
The implications extend beyond acute emergencies. In oncology, delays in testing or referrals can postpone effective treatment or prevent patients from receiving appropriate therapy altogether. In many cases, such as a lung cancer patient who has not had genetic testing to guide their care, they default to higher-cost, less effective options. AI Care Pathways help operationalize established guidelines by ensuring that recommended tests, consults, and treatments occur on time. Similarly, in chronic disease management, patients discharged after exacerbations often lack timely follow-up, leading to avoidable readmissions. Care pathways that coordinate outpatient care improve outcomes while reducing repeat emergency visits—benefiting both patients and Medicare.