Part 6 ended on a practical warning. If you are going to take on the outcomes of a whole population, you had better know where the cost actually sits. It does not sit evenly. It sits in a startlingly small group.
The number is consistent across almost every health system that has looked. Roughly 5% of patients account for around half of all spending. Push further and it gets sharper still: the top 1% can drive more than a quarter of the total.
So the arithmetic of reform is blunt. Direct your effort anywhere outside that group and the ceiling on what you can achieve is low. This is the single most important fact in health economics, and most systems still budget as if every patient cost about the same.
Who these people are
They are not a random unlucky few. They share a profile:
- Multiple chronic conditions at once. In one large study, about two-thirds of the highest-cost patients had conditions spanning three or more body systems.
- Frequent hospitalisations, which is where their cost concentrates. Roughly half the high-cost group had at least one admission in a year, against under 5% in the general population.
- Mental health woven through the physical. Anxiety, depression and chronic pain show up again and again in the costliest clusters.
In Belgium, people with multimorbidity were nearly half the population studied and drove about three quarters of the cost. Same pattern, different country.
And here is the thing that makes them the right target. High-cost multimorbid patients also tend to have the worst-coordinated care, the most duplication, the most unmet need, and the worst outcomes. They are simultaneously the most expensive and the most poorly served. The waste and the suffering are the same people.
The trap in the data
Now the honest part, because this series does not sell easy wins.
Targeting the high-cost few sounds like free money. It is not, for two reasons.
First, regression to the mean. Someone is expensive this year partly because they hit a bad patch. Some will get cheaper next year no matter what you do. Any observational programme that claims credit for that drop is fooling itself. This is why the naive "find the top spenders and manage them" pitch so often disappoints once it meets a control group.
Second, and more sobering, is Camden. The Camden Coalition ran the most famous hotspotting programme in the world: intensive teams of nurses and social workers wrapped around the highest-need patients after discharge. It was celebrated for years. Then someone ran it as a proper randomised trial of 800 complex patients. Result: no reduction in readmissions versus usual care. None.
Care coordination alone, even done well, was not enough to move outcomes for the most complex patients.
A later analysis showed the programme did increase primary-care visits. It connected people. It just did not change the endpoint. Connecting a patient to a broken system faster is not the same as fixing what the patient needs.
What the failure actually teaches
Camden is not an argument against targeting the few. It is an argument against thinking coordination is a light-touch add-on.
The patients who drive the cost have medical and social complexity tangled together: housing, isolation, addiction, mental illness sitting on top of the diabetes and the heart failure. A weekly check-in call does not touch that. Coordination that works has to own the case with real depth, and often has to reach outside the clinic into the social drivers entirely.
So the lesson pulls straight back to the rest of this series:
- Segment first. Stratify the population by need and cost, and concentrate the expensive, high-touch model on the few who justify it. Give everyone else something lighter.
- Go deep, not wide. For the complex few, shallow coordination fails. Ownership has to be real, continuous, and connected to social support.
- Measure against a counterfactual. Because of regression to the mean, only a controlled comparison tells you if anything worked. Everything else is a story.
Where this leaves the model
Put Parts 5, 6 and 7 together and the shape is clear.
The goal is value. You only get it if you pay for it. And you concentrate the effort where value actually lives, in the small, complex, badly served group that spends the money and suffers the most. That is not a slogan; it is a segmentation exercise with a budget attached.
But none of it functions if you cannot see it. You cannot segment a population you have not measured, pay for outcomes you do not track, or prove a counterfactual without data. Which is the last piece.
Next week: the measurement problem, and how a system actually gets built.
Sources
- Zulman D.M. et al. and related work on high-cost patients in the US Veterans Affairs system. On the 5% who drive roughly half of spending, and two-thirds having conditions across three or more body systems.
- Wammes J. et al. and US MEPS analyses. On cost concentration, the top 1% driving over a quarter of spending, and hospitalisation as the main cost driver in high-need patients.
- COMORB study (2024), multimorbidity healthcare expenditure in Belgium. On multimorbidity representing about half the population and three quarters of cost.
- BJGP (2024), Disease patterns in high-cost individuals with multimorbidity. On heterogeneity and the prominence of mental health in high-cost clusters.
- Finkelstein A. et al. (2020), "Health Care Hotspotting: A Randomized, Controlled Trial," New England Journal of Medicine 382:152-162; and the follow-up in Health Affairs (2023). On the null result for readmissions and the increase in ambulatory care.
Part 7 of the series. Part 6 covered how to pay. Part 8 covers measurement, data, and building the system.