Paying a flat fee for a particular HM program, or one that is based on the number of people in the population at risk, is the simplest of all charge methods. It enables HM providers to predict their revenue, and HM prospects/clients to know what their costs will be – before any health risk assessment (HRA) that is typically part of HM programs is purchased or implemented. HRAs may be purchased separately, of course, and used to predict costs or set charges, which may then be based on fixed or per member cost, based on the overall risk scores of the population at risk. HM purchasers can compare the costs of competing options, as long as they use the same flat or per member fee approach. It makes budgeting by sponsors and clients simple as far as costs are concerned.

The predictability of costs is often what makes this approach attractive to clients, whether insurance plans or employers, and the predictability of revenue is attractive to providers, as well. But the predictability of costs comes with almost total unpredictability of results. Since these are definitely important in budget planning for clients, it can complicate the budget process overall. In order to predict results, clients must be able to predict: 1) what the effects of participation in specific HM interventions will be; 2) how many individuals in the population might benefit from each intervention; and 3) how many of these will participate in each. While past experience may offer a basis for guessing results based on past averages, there are likely to be wide variations across populations.

HM providers should be able to describe what the average effects of each of their interventions has been in the past, perhaps even promise or guarantee some minimum level of effects per participant. But clients will not usually know up front how many of their plan members or employees will be logical targets, eligible for any particular intervention, unless they have already performed a claims analysis, screening, or health risk assessment (HRA) required to do so.

Moreover, predicting the number of eligible members that will participate in particular HM interventions is impossible up front, since what the client or provider offers in the way of incentives and rewards for doing so has a powerful impact on that. Such a prediction is largely dependent on decisions regarding enrollment effort and incentives, rather than merely characteristics of the population at risk.

And since incentives and enrollment efforts add to costs, decisions about these must be made before prospects can estimate their ROI, assuming they will pay incentives. If HM providers are to pay incentives, they will not be likely to offer a fixed fee or per member price, since only knowing how many are eligible and will choose to participate will enable them to predict their costs.

On the other hand, charges that are based on flat fees or per employee rates are usually used by the lowest-priced HM providers. This is because the only kinds of HM interventions that can be charged for in this way are those whose costs to the provider can be predicted up front as well, without knowing how many members of the population will be eligible for or participate in particular interventions. This means the HM interventions must vary only modestly in costs to providers based on the numbers of population members that participate.

When HM providers use expensive face visits or phone contacts with professionals as their basic HM intervention method, the costs for programs vary almost totally based on the numbers of people to be managed. The number and duration of the personal contacts involved per participant may vary based on the risk/reward potential of individuals or different segments in the population at risk, but this potential can only be estimated based on past provider experience up front. To improve predictability of the numbers of people who should participate in an HM intervention, the HM program must have already been implemented, at least as far as conducting a health risk assessment (HRA) of the population.

By contrast, even before HRAs are conducted, HM providers that employ computer-automated HRA analysis and HM coaching that consists of automatically computer-customized recommendations, coaching and feedback to participants, can project their costs well enough to charge on a flat fee or per employee basis for each HM program they offer. Once HRAs are completed, prospects can choose how many and which HM interventions to purchase, based on predictable costs and results.

For example, HealthMedia, Inc. of Ann Arbor, Michigan, is such an HM provider, offering HM programs to insurers, including Kaiser Permanente, and to employers. It charges on a flat fee basis, with the flat fee adjusted for different population sizes. Charges for individual HM programs may also be discounted when clients choose to purchase multiple programs. Its HRA includes questions on employee health-related productivity impairment whose answers can be used to identify eligibles for particular HM interventions.

When HM providers charge fixed or per member fees, clients’ costs can be translated into predictable costs per eligible or per participant, once the numbers of people to be targeted/eligible and the predicted participation rates are estimated. Based on just the numbers eligible, the number of participants needed to make an individual HM program attractive for a given insurer or employer can be calculated, even if charges are set per population. The method for doing so will be described in the final posting in this series.

Given that flat fee/per population member charges and payments can be translated into comparable per eligible and per participant charges and payments, it is as good a method to use as any. And like the others, it can add risk/reward elements, where both HM provider and client find this mutually satisfactory and beneficial. This translation involves simple mathematics, though computer programs can easily be created to perform the math automatically, once the inputs needed are known or predicted.