Unfortunately, one of the problems with risk adjusted, case-mix compensating mechanisms is that there is no incentive whatsoever for health care providers who should get negative case mix adjustments to request such modifications. In essence, all the providers in Lake Wobegon think they receive capitation payments that are at, or below, adequate levels. No provider in Lake Wobegon ever thinks their capitation payment is excessive.
The authors have some keen insights but leapfrogged a bit. They saw an aberrant consequence of capitation payment schemes but have not fully accounted for the true causes of the “revenue to cost” gaps that necessarily arise under capitation. If I were solely a health care provider I would, at best, be in their position as well.
My advantage in all this is that I am a mathematician and statistician and I spent close to a decade in the actuarial field (Insurance Services Office, NY, NY; Liberty Mutual Insurance Company, Boston, MA; General Accident Insurance Company, Philadelphia, PA; and Reliance Insurance Company, Philadelphia, PA).
Even having done insurance and reinsurance rate making and reserving and financial reporting for most of a decade, it took more than a decade away from insurance and reinsurance to see that most of what I thought was true about insurance operations was hopelessly muddled by the incorrect assumptions in insurance rate making and reserving theory.
Actuaries as it turns out, are far more concerned about getting their company’s rate filings approved than they are concerned about educating regulators and the public about how insurance really works.
Insurance makes less and less sense the more you view it the way rate making, reserving, and financial reporting actuaries view it. On the other hand, it makes perfect sense when viewed as a statistician or financial analyst might view it, focusing on: Profits, Losses, Insolvency risk, and Maximum sustainable benefit levels.
If actuaries were not enmeshed in roles as their employer’s advocates, they might concentrate more on educating regulators and the public about how insurance really works. But their roles as company/client advocates are far easier to fulfill when the public, politicians, and regulators are misinformed.
Explaining the flaws in capitation, as it turns out, is both more and less difficult than I imagined 14 years ago. Central to this problem is that the explanation is a bit more sophisticated than most potential beneficiaries can comfortably digest, and and the human tendency, is to err conservatively – rejecting things we do not understand.
It is far safer, and easier to question the proponent of a new theory, especially a counter-intuitive theory, than to risk ridicule by accepting a theory that may prove unworthy. The best alternative is recognizing our inability to follow such arguments and commit to acquiring the additional skills we need to fairly appraise the theory. I won’t hold my breath.
Perhaps this teaser will encourage some people to look at my papers on “Professional Caregiver Insurance Risk”. (See my CV at
for a list of publications and presentations.) The mathematics behind Professional Caregiver Insurance Risk is undeniably correct. Still, many who read this material and are unable to evaluate the mathematics ask for empirical data that will “show” what I already know is a mathematical tautology: Capitation, no matter what it is called, is a deficient and inefficient mechanism for paying for health care services. Looking at empirical data is befuddling and boring for someone who understands the mathematics. Isolated sets of data support both extremes in the debate over capitation: Proponents and Critics, as detailed below.
The Wales Problem
The problem with isolated data is that in any accounting cycle there are predictable, though random, outcomes. During any financial cycles, Wales might indeed exhibit an excess of costs over revenues for three reasons: 1) A “Cost bias” that might be compensated for by a case mix adjustment scheme, and 2) An increase in cost variability unrelated to the case mix adjustable “Cost bias” that is solely dependent on small portfolio size compared to the NHS, 3) A combination of both of these effects, a clearly bias in costs and a very poor year as an insurer.
The next cycle, the “Cost bias” will continue in Wales if not case mix corrected. But the increased cost variability is likely to manifest in some other locality becoming the next “worst” outlier, not Wales. Wales may still receive inadequate “Cost bias” adjustments but another locality may have a greater discrepancy between costs and revenues than Wales because its patients are sicker than average that year.
Worse still, if Wales has an aberrantly low cost year next year, it may appear that the capitation payment for Wales should actually be lower, not higher, the following year, even though the population remains older and sicker than for the rest of the UK.
An Influenza Epidemic
One might imagine an influenza epidemic effecting office workers rather than factory workers because, we might assume, factories may have more fresh air than offices. The providers serving predominantly office workers will have unusually high costs, treating much higher numbers of office workers for influenza related conditions. Providers serving primarily factory workers will have much lower than average costs that year.
When viewed retrospectively the providers serving office workers have excessive costs, compared to revenues, which may appear to be a new case-mix problem. It is not a new case mix problem. It is a cost variability problem. These providers do not require a case mix adjustment – their misfortunes are solely the result of a bad year as insurers. The amount they were paid, per capita, is adequate for thir expected costs, but it does not adequately compensate for their roles as insurers for their patients, at least not this past year.
During each year, as the authors correctly note, there are over-paid providers and under-paid providers. Case mix adjustments certainly make a difference and actuarially adequate, but not redundant payments, ought to adjust for discernible case mix effects, such as the older and sicker population in Wales.
The problem is that while efficient case-mix adjustments can be made for the Wales’ demographics, it is mathematically impossible to efficiently compensate providers for their insurance risk management activities that are really responsible for the shortfalls in revenues for most under-paid providers during each accounting cycle.
At cycle end, one can always go back and look at the extremes and correctly note that some providers were paid inefficiently excessive amounts for the services provided. Others, of course, were inadequately compensated. The key is that we know this will occur (though we cannot specify which providers will be inadequately or excessively compensated each year) based on the mathematical theory.
This must happen in every accounting cycle, whenever the NHS, or any American health care finance entities, transfer insurance risks to smaller entities, through capitation payments schemes. The increased “variability” in costs in small portfolios of insurance risks – unlike the “Cost biases” that case-mix adjustments correct, cannot be efficiently compensated for by any level of sustainable and efficient capitation payments.
As disadvantaged providers cite instances of inadequate revenues as just cause for requesting increased revenues, budget cutters at the national level look at data for excessive payments as just cause for further reducing provider payments. What is needed, and what Professional Caregiver Insurance Risk provides, is a theory that encompasses both outcome extremes, and which explains why capitation mechanisms are always inefficient and unsustainable methods of financing health care services. at any level below that of a large, very efficient, risk retaining insurer.
When insurance risks are managed at the level of the NHS, what would have been excessive and inadequate payments for different providers/trusts, cancel each other out. This is why the NHS, functioning as risk manager, is a more efficient insurer than health providers/trust that manage sub-portfolios of the NHS’s insurance risks.
Insurance risk transfers are only efficient if the entity accepting the risks is larger, after the transfer, than the entity ceding the insurance risks. While this may not conform to the average person’s intuition – it is absolutely the case when you understand that risk management through insurance (and the mathematical theory it is based on) is all about coming close to average costs each year. Efficient insurers’ costs do not deviate far from average population costs, while inefficient insurers’ costs deviate dramatically, from average costs, each year.
Whenever insurance risks are transferred to smaller entities, none of them know, at the beginning of their accounting cycle, whether they will have excessive, or inadequate, resources at the end of the accounting cycle. To deal with this essential uncertainty, ALL risk assuming providers should reduce the level of benefits they plan to provide, below the average level represented by their fixed capitation payments, lest THEY be one of the providers whose revenues will prove inadequate because they had exceptionally high demand for services during the accounting cycle.
Maximum Sustainable Benefits
It is not appropriate to close one’s doors on 1 December 20X1 because you have used up all your resources during the first 11 months of the 20X1 funding cycle. The Maximum Sustainable Benefit (MSB) tells us how much providers/trusts managing sub-portfolios of insurance risks ought to reduce planned service capacity, compared to the levels they could sustain when the NHS (Or in the US, when an insurer, managed care organization, or the Medicare/Medicaid program) manages the same collection of risks, so that they are as able to provide identical care for identical symptoms at the beginning and the end of each risk assumption accounting cycle, as the NHS could if it simply retained these insurance risks.
While one might think that the Maximum Sustainable Benefit is a fixed amount – it is actually contingent on other factors: Profitability goals; Loss avoidance preferences, and Insolvency aversive-ness. Each such contingency results in different levels of maximum sustainable benefits because the variations in costs are non-linear stochastic processes, not a fixed determinate processes.
The most damaging inefficiency is not measured by the few providers who have excessive revenues at the end of the accounting cycle, nor by the few providers that have inadequate revenues at the end of the accounting cycle, it is measured by the reduced levels of care available from ALL providers, that affect ALL patients, throughout each financial cycle.
So, for example, we might at first glance conclude that the most inefficiently compensated providers are of two sorts:
5% of all providers receive the most excessive and inefficient payments for the accounting cycle.
5% of all providers receive the most inadequate and inefficient payments for the accounting cycle.
We might think that the funding inefficiencies are isolated and only one type of inefficiency actually harms both patients and providers: Underpayments to providers. These authors correctly noted this under-payment problem for a specific locality – Wales.
So the inefficiencies appear to affect, at worst, 10% of providers and their patients and only 5% of providers and patients appear likely to provide/receive inadequate care. This might even be considered to be a tolerable level of inefficiency compared with the presumed inefficiencies in what was essentially the old fee for service provider reimbursement system.
But the inefficiencies induced when insurance risks are transferred from large capable entities to much smaller entities actually affect 100% of providers and 100% of patients and at levels far greater than the levels suggested by the case-mix adjustments appropriate for the older and sicker Welsh demographics.
My papers in The Journal of Healthcare Risk Management and JONA’S Healthcare Law, Ethics, and Regulation (See my CV at
for a list of publications and presentations.) spell out how one might approach calculating these far greater inefficiencies by calculating Maximum Sustainable Benefits any risk bearing entity can provide, throughout the financial period.
While most researchers see isolated effects, such as the case mix adjustable inadequacy for Wales so that Welsh providers will receive adequate service revenues and maintain adequate service capacities for the Welsh population, the flaws in transferring insurance risks from large, capable, and efficient insurers such as the NHS, or American insurers and governmental programs such as Medicare and Medicaid, to smaller, less efficient entities such as individual providers or trusts, lead to situations in which ALL providers should reduce benefits by as much as 50-95%, depending on their size, relative to the NHS.
From a practical standpoint, the authors should go back, as they did and assess case-mix adequacy retrospectively after every accounting cycle. They should also attempt to advocate for case mix adjustments to reflect disparate Welsh epidemiology and demography. But there will always be outliers with the greatest under-payments and the greatest over-payments in every cycle in which capitation mechanisms are used because capitation is an extraordinarily inefficient health care service mechanism.
To adequately compensate ALL providers for their insurance risk management services, the NHS ought to be paying each provider substantially more than any case-mix adjusted system would suggest, and well beyond the levels that a sustainable and efficient national health insurance program would cost if the risks were managed at the national level. The only savings that come from capitation payment mechanisms are because providers, squeezed by their exposure to higher costs than revenues, systematically reduce the level of care they provide to their patients. Capitation mechanisms are logically and mathematically inconsistent with efficient health care (finance) systems.
The problem with adjusting capitation payments with case mix adjustments to correct “cost bias” in actual service costs for well defined differences in population health demographics is that these adjustments do not correct for the increased variability in costs that small portfolio size and inefficient insurance operations cause.
The authors leapfrogged a bit, skirting the issue of inefficient insurance risk management, which is fine. It is often impossible to convince seemingly excessively compensated providers that even their over-payments are actually inadequate for their dual services as clinicians and insurers.
Disadvantaged providers are almost always more likely to see a problem than those who receive over-payment. But neither set of providers tends to see that the real problem is a bit different and far more insidious than anything amenable to correction using case mix adjustments.
The reason it is harder to explain this to over-paid providers is that they like being overpaid. If their case mix correction should be negative they are thrilled because this suggests continuing over-payments. The problem is that the over-payments are likely still a random effect due to random, rather than systematic lower than expected costs. These providers are still likely to be inadequately compensated for their clinical AND risk management services.
Bureaucrats, on the other hand, are a real problem. As underpaid providers, not overpaid providers tend to look for positive case mix adjustments, bureaucrats look for data on over-paid providers, with the opposite intent: Cutting health care payments to providers because they see providers who are being over-paid providers and they believe this is sufficient to make further reductions in provider reimbursement.
The problem with case-mix adjustments is that they can be made after each accounting cycle: Providers with excessive revenues might have negative case mix adjustments for next year, reflecting the reduced costs, and/or excessive revenues, for patient care last year. There are two problems with this.
First, when the national entity wants to reduce costs, or at least restrain the growth in costs, it wants to spend less, not more, on provider payments. This pits provider against provider for a non-increasing pool of funds. This increases competition between providers for the wrong reasons.
Rather than working collaboratively to produce healthier populations, providers are competing for the same funds. A win for one provider becomes a loss for another. Surgeons want more money for operating rooms and surgical interventions while family practitioners want more money for prevention services and out-patient care.
The second, and more serious problem, is that transferring insurance risks to providers results in knee jerk reactions to the most recent years costs, leading to wildly erratic financial outcomes for providers. A provider that was accurately compensated last year, but which had lower than expected costs, should get a cut in its capitation payments. The reduced payments would be even more inadequate for an average year, so the provider is likely to suffer a big loss the following year.
On the other hand, a provider with unusually high costs last year, gets an increase in its capitation payments that far exceed its needs. It is paid excessively more than necessary in the second year.
It is one of the great 21st century paradoxes that these wild fluctuations in year to year payments and costs are precisely the effects true insurance mechanisms are intended to eliminate.
The piece these authors leapfrogged is the most critical for understanding health provider’s inefficient insurance operations.
Until I read the NHS White Paper (See “Equity and excellence: Liberating the NHS” at
I thought the NHS was somewhat more immune to the US’s health care finance flaws. After reading the White Paper, I realized that both health care finance systems are nearly indistinguishable in their mismanagement of insurance risks.
Professional Caregiver Insurance Risk 