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Could better managing our very sickest patients be the path to lower costs of care? Los Angeles Times via Getty Images For two decades, value-based care has been built upon a simple premise: if we better manage the sickest patients, total costs of care will fall. This idea catalyzed countless disease-management programs, care-coordination models, and technology innovations. And yet, as the field has matured, an inconvenient reality has emerged. Much of value-based care isn’t truly aimed at the sickest patients. Instead, the biggest advances have focused on high-volume, moderately expensive chronic diseases: congestive heart failure, diabetes, and chronic obstructive pulmonary disease to name a few. The formula has been straightforward — layer relatively uniform interventions (“peanut-butter care management”) across thousands of patients to achieve incremental improvement. This is not wrong. It’s not bad. It is simply incomplete. Because in every population exists a much smaller subset of patients with ultra-high-acuity, ultra-high-cost conditions who defy actuarial expectations. These conditions—such as ALS (amyotrophic lateral sclerosis), advanced muscular dystrophy, or complex transplantation scenarios—constitute only tiny fractions of total patients served, yet drive disproportionate medical expense and suffering.This group has remained largely outside the scope of practical innovation. That may finally be changing. Why We Ignored the Highest-Cost Patients Value-based care has gravitated toward large-scale, predictable conditions for sensible reasons: 1. Volume justifies investment: Managing tens of thousands of diabetics means a programmatic improvement of 5-10 % yields meaningful returns. 2. Their disease course is predictable: Clinical guidelines offer clear pathways. Care is scalable. MORE FOR YOU 3. The math works cleanly in payment models: Risk adjustment methodologies were built to predict and normalize costs among large populations—not outliers. This has left patients with catastrophic, highly idiosyncratic disease effectively invisible to most innovation. Their costs are volatile, their clinical progressions variable, and their needs deeply individual. These patients often bounce between hospitals, specialists, and fragmented care teams. Their outcomes are poor. Their experiences are worse. And because they are few, most plans and provider systems have chosen to focus elsewhere. The Case for a New Frontier Think of an ALS patient with progressive respiratory failure who is hospitalized 5–6 times a year. Each admission may cost $50,000–$150,000 depending on ICU utilization. (Consider that an ICU stay routinely adds $10,000–$20,000+ per day over a general ward.) If that patient is admitted 5-6 times, total hospital cost can easily exceed $300,000–$600,000 annually. Multiply that by, say, 10,000 similar patients with multiple different diseases across a national service area (conservatively), and total hospital costs could reach $3 billion–$6 billion per year for that cohort alone. What if intensive, protocolised outpatient care driven by true clinical experience could avoid even a fraction of those hospitalizations? Suppose the targeted care model prevents just 1 admission per patient per year (a 20% reduction in admissions if baseline is 5). For 10,000 patients, that alone might save $500 M (assuming $50k per admission) to $1 billion (if each prevented admission was $100k). Suppose additional interventions shorten average length of stay by 1 day per admission across all admissions. If the average cost per day is $15,000, and there are 50,000 total admissions across the cohort, then reducing by one day equals ~$750 M in savings. Together, these modest-to-medium sweeps of improvement can generate hundreds of millions to over a billion dollars in annual savings for just one narrow cohort. Why This Matters Now With healthcare affordability under pressure, health plans and health systems are searching for the next meaningful lever. Provider organizations have already squeezed out much of what can be gained from traditional population-level chronic-care management. The next chapter requires: 1. New clinical operating systems: Teams with deep clinical expertise that can build bespoke protocols around rare conditions—not just blanket care plans. 2. Specialised multidisciplinary models: In the example of ALS, Neurology, pulmonology, physical medicine & rehab, palliative care—integrated, rather than fragmented. 3. Re-designed payment: Payment models that adjust for extreme acuity and reward avoided admissions and reduced ICU days. 4. Data differentiation: Advanced analytics allowing early signals when decline is underway—not after hospital admission. These are solvable problems. What’s been lacking is strategic priority. Building for the Few to Help the Many When I caught up last week with Dr. Philip Choi, a college classmate who now leads a clinical respiratory failure program at New York University Medical Center, I was struck by the clarity of his personal mission: “These patients need a different care model than what is traditionally enabled.” His clarity inspired me to write this piece. We need to build real models that support the tiny fraction of individuals who absorb a massive share of resources, navigating catastrophic illness with courage, and—too often—alone. These patients deserve more than we have historically offered. The system needs the innovation they can catalyse. Value-based care needs a new direction. Maybe this is it. Closing Thought For twenty years, value-based care optimised the middle of the cost curve. The next decade must belong to the invisible tail. The field will know it has matured when we no longer just celebrate a few-percent reduction in diabetes-related admissions, but instead meaningfully improve life for the ALS patient struggling to breathe at home, alone, between emergency hospitalizations. If value-based care is to live up to its founding promise, it must follow the money — and the humanity — to where the suffering is greatest. It is time to build for the truly sickest of the sick. Editorial StandardsReprints & Permissions
