This section uses stylised examples based on actual events to illustrate the effects of the allocation of random risk under the NZPHCS. The first example is a strike by hospital junior doctors; the second is a decision by District Health Boards to cull hospital waiting lists by removing all patients who would not be seen by hospital staff within six months. In both cases, unmet hospital demand was referred back to primary care practitioners for management, causing primary dare demand to increase, as each patient referred back would require at least one, and possibly more, additional consultations not anticipated in the capitation formula design. Whilst the shocks are exogenous, and ultimately the additional demand could be factored into revised capitation payments, the examples also highlight how different allocation of individuals of different health states amongst practices in the presence of variable practitioner-set patient payments leads to higher payments for the patients of practices with sicker patients facing higher demands.
The examples highlight the fallacy of equating the ‘notional averaged treatment subsidy’ under capitation to the risk-free treatment subsidy under the previous fee-for-service system. Under fee-for-service, practitioners would be indifferent to any demand shock, as each extra consultation delivered would be remunerated at its full cost c. Under capitation, however, a practice incurs the full costs of an additional consultation, but receives no additional government payments. As the capitated patient is charged a fee less than cost (v < c), the additional income from the additional consultations will be insufficient to cover their additional, and unanticipated cost. The practice now incurs a deficit in respect of the additional demand. As practitioners can charge patients, it is presumed that the deficit is passed on in higher patient fees (patient payment = v+y).
A simple numerical example illustrates. Suppose each consultation costs the practice $50, and the patient payment (v) is initially set at $10. Each additional consultation arising from a DHB referral imposes a $40 deficit on the practice. The more patients referred back by the DHB and the greater the number of consultations required for each patient, the greater the deficit incurred. If the practice is unable to charge the referred patients the full cost of the additional consultations, to break even the additional costs must be levied to all patients via an increased patient payment y. All patients, including those not receiving the additional consultations, now pay the risk premium created by an unpredicted and unpredictable (that is, ‘random’) change in the demand of a small number of patients registered at the practice. As the patient list is the risk pool, prices faced by those patients in the pool whose demand does not change are determined by those whose demand has changed.
Assume now that two practices have otherwise identical costs and patient lists with identical ex-ante characteristics, but one has 10 patients referred back and the other has 20 (that is, its patients are ‘sicker’ but this is not recognised in the capitation formula). The deficit incurred by the second practice is twice that of the first practice. The patients of the second practice, who are sicker on average, will face payment increases twice the size of the first. Patients with identical capitation status now pay different prices depending upon the practice at which they are registered, even though both practices have the same service delivery costs.
Now consider the effects of the mandatory requirement that individuals for whom higher capitation payments are received when well must also pay commensurately lower patient payments when they seek a primary care consultation. Assume the base-case practice charges nothing to high-capitated patients under five years old and $30 per consultation to low-capitated 25–44-year-old patients. The deficit for each additional DHB-referred under-five consultation is $50 and for each additional 25–44 consultation $20. The higher the proportion of higher-capitated (ex-ante assessed as ‘high needs’ or ‘high political priority’) patients referred back by the DHB, the greater the deficit incurred and the higher the commensurate price increases to all patients of the practice must be. The patients of practices with large numbers of higher-capitated patients referred back will pay proportionately more, because the practice is obligated to charge the higher-capitated patients lower fees.
The costs of the DHB waiting list cull are unlikely to be trivial. Typically, higher-capitated (and politically favoured) elderly and young people are disproportionately represented in hospital waiting lists. Thus, the probability of a waiting-list patient referred being a high-capitation individual will be substantially higher than the probability of the patient being a low-capitation individual. These individuals are also likely to require multiple additional primary-care consultations in order to continually treat the waiting-list condition and to reassess the eligibility for re-entry into the waiting-list system. These non-trivial additional costs, imposed by DHBs shifting demand and financial risk into the primary sector, will have a non-trivial effect upon the costs paid by all sick individuals seeking primary care.
This example illustrates the fallacy of treating the capitation subsidy as if it is a ‘notional averaged treatment subsidy’ when setting v for each subsidy group. Patients from two different subsidy categories may each have identical needs for treatment, but requiring each to pay different amounts as each receives a different capitation subsidy confuses the payment of an ex-ante risk-rated insurance premium subsidy with a politically motivated wealth transfer. The former approach compensates the risk manager for the extra costs anticipated in respect of individuals with higher demands, and typically means that no distinction needs to be made between patients of any premium class when treatment is sought ex post. The latter approach echoes the pre-NZPHCS arrangements, when different subsidies were paid for different classes of individual for politically motivated wealth-transfer reasons.
By confusing differences in anticipated demand for care with politically motivated wealth transfers under the NZPHCS, sick patients of practices with greater exposure to unanticipated demand shocks associated with individuals of the more favoured group end up contributing not just to the costs of demand variation in their own payments for health care, but also a portion of politically motivated wealth transfer because the two functions are bundled together in the one patient payment. Such transfers are avoided in typical social insurance systems when the wealth transfer is achieved using a premium subsidy. Less politically favoured groups pay more of the costs of the system by paying a higher premium top-up ex ante (for example, in New Zealand’s workers Accident Compensation (ACC) system, employers pay a premium for employees based upon industry risk characteristics, and the employee’s contribution is paid as a proportion of taxable income). This eliminates any need to adjust payments to achieve wealth transfers when treatment is sought (under the ACC system, there is no difference in patient payments based upon income or subsidy class). Individuals with equal need of treatment pay equal amounts at the point of service delivery.
To avoid distortions of the kind illustrated in the example, either premium top-ups should be charged ex ante in respect of each individual in accordance with political wealth-transfer motivations and identical payments levied ex post; or identical payments should be levied to all patients ex post and an additional fee-for-service government subsidy paid in respect of each treatment delivered to the member of a politically favoured group, as occurred pre-NZPHCS. Under such arrangements, there is separation and transparency between the consequences of risk-management practices and wealth transfers, and the sick are not required to pay an additional consumption tax on primary health care in order to further political wealth-distribution goals.
Assume now that one practice has a high proportion of higher-capitated individuals ‘on the books’ (Practice A), and another practice (Practice I) has a low proportion. Practice A thus receives a higher proportion of its revenue from fixed payments, and it can only charge its patients on average low values of v. Practice I receives a lower proportion of its income from fixed payments, and more from patient payments (that is, v is higher). The capitation contract incentive is thus higher for Practice A.
Both practices provide the same number of consultations (K) in a given period. The average patient payment v at Practice A is $20 per consultation, and at Practice I $35. Assume also that, as a consequence of the DHB patient referral, each is required to provide an additional 200 consultations in a given period. Practice A incurs a deficit of 200 x $30 = $6000, and Practice I a deficit of 200 x $15 = $3000. If each practice spreads the additional costs across all patients, Practice A patients will pay an additional $6000/(K+200), twice the increase faced by Practice I patients, $3000/(K+200). Thus, DHB referral results in the ex-ante assessed ‘higher need’ Practice A patients, who are deemed less able to meet the costs of higher patient payments, and more likely to be dissuaded from seeking primary care by the size of the payment, and ostensibly more likely to need care in the first place, facing higher patient payment increases than the ‘lower priority’ Practice I patients.
This example illustrates numerically the increasing costs of uncontrollable events under sharper incentives. It also illustrates the inequitable consequences of confusing a premium subsidy with a treatment subsidy in the absence of consideration of the locus of residual financial risk-bearing.
Inevitably, patient payments have become more variable between practices as the proportion of the population eligible for capitation subsidy has increased. Consequently, the likelihood of DHBs invoking their NZPHCS price-regulation powers has increased. However, if prices are regulated as if the patient payment is a risk-free ‘notional averaged treatment subsidy’, practice financial viability may be severely compromised. DHB materials and politicians’ expressed intentions suggest that neither group fully appreciates the extent to which the NCPHCS has shifted substantial amounts of additional random-risk costs onto practices relative to the pre-2002 system. Consequently, fears expressed by practitioners about their fees becoming subject to price regulation by DHBs under the currently voiced understandings[5] are substantiated.
Assume that a naïve regulator, knowing that Practice A receives higher capitation payments, and therefore, in line with treatment benefit pass-through expectations, must charge lower patient out-of-pocket payments than Practice I, is faced with the price rises in the example above. If the regulator presumes that the capitation payment is simply a ‘notional averaged treatment subsidy’, then a price increase of $6000/(K + 200) imposed by Practice A is ‘unreasonable’ given that Practice I with the same demand increase imposes a price increase of only $3000/(K + 200). If the regulator restricts the price increase by Practice A to that imposed by Practice I, then Practice A becomes financially unviable.
Effective regulation of capitated practices requires specialist insurance and risk-management knowledge. Such regulation is problematic even in countries where there is considerable experience in this form of regulation (Hagen, 1999). Given the added complications from the bundling of wealth transfers with patient payments, effective regulation within the NZPHCS would appear to be an extremely complicated, costly and risky endeavour.