The three earlier challenges for traditional healthcare providers in health management (HM) pale beside the greatest challenge facing both providers and customers thereof, namely counting the benefits, as well as the costs of HM investments.  While sickness care has devised thousands of metrics for what is accepted as good practice and outcomes for its efforts, the same is nowhere near the case for HM.  Its history has included a lot of over-counting and under-counting of benefits, in particular, though costs are occasionally overlooked as well, particularly costs to HM participants.

The challenge is greatly complicated by the sheer number of consequences that HM can have, the complex “causal chain between particular HM interventions and such effects, and the difficulties in measuring many of these effects, particularly the beneficial ones.  It is no wonder that early HM efforts focused almost entirely on reducing sickness care costs, since these were readily measured, and it seemed thoroughly logical and credible to attribute reductions therein to HM interventions.

But while sickness care, along with workers compensation and disability costs of “unhealth” for insurers and employers have long been counted, even the simple out-of-pocket financial cost savings that HM participants gain have more often been overlooked.  Smokers who quit can save as much as a thousand dollars a year by not purchasing tobacco products, for example, to say nothing of the out-of-pocket savings for those able to overcome substance abuse problems.

Since the first objective of all HM initiatives is to change the current behavior of individuals and families, the first counting problem arises because there are few reliable, simple, and inexpensive ways of monitoring such changes.  When participants have financial incentives to make such changes, there is usually the risk that they will report them as made, rather than make them, or al least exaggerate the extent of the changes they make.  Aside from some chemical checks on behaviors such as alcohol, drug and tobacco use, all of which cost money for testing, there are few objective measures of behavior changes.

Monitoring objective health status changes are also likely to require testing, though this may involve no more than simple monitoring devices, from scales to check weight to simple blood pressure monitors, blood sugar meters, etc.  Many other metrics are routinely part of annual lab tests that measure twenty or more indicators at relatively low costs, but still add to overall intervention costs.  At the other extreme, some continuous monitoring devices can add hundreds of dollars a year to HM efforts dealing with chronic diseases, though such frequent monitoring is also good for managing and evaluating results.

It is when counting attempts to include workplace productivity and performance effects that it becomes really challenging.  Except for a minority of industries and jobs where individual employee output quantity and quality are routinely measured as part of management and compensation, most counting of these effects involves estimation.  Estimates may be based on team objective measures, or individual self-reports, but both are suspect.  Team measures may accurately reflect total output, while missing the contributions of individuals outrageously.  Self-reports, of either current health-related impairment or improvement may be “honestly” biased by low levels of self-awareness, or by the desire to look good, get rewards, etc.

When workers in jobs where their output could be objectively measured, in one example, they reported themselves as having been impaired by an average of 20% due to migraine headaches, far greater than the objectively measured effect, which was only 8%. [G. Pransky, et al. “Performance Decrements Resulting from Illness in the Workplace” JOEM 47:1 Jan 2005 34-40]  In any case, it would be a strange coincidence if self-reported impairment levels were identical with actual objective measures.

This means that self-reported declines in individual productivity or performance have to be converted from their estimates to whatever objective checking shows corresponded to self-reports. For the call center representatives in the preceding example, the “conversion ratio”, from self-reported to actual impairment is 0.40 to 1.00.  Unfortunately, while the average conversion ration works well, once it is determined, for each employer or team where such a conversion is used, for individuals in pay-for-performance (P4P) situations, an individual conversion rate would be needed to apply in individual-based P4P compensation.

Fortunately, healthcare providers already have a strong motivation to develop the best gauges for individual, or at least team productivity and performance, thanks to the growing number of P4P systems that apply to them, and the growing amount of bonus or other performance-based revenue to which they are subject.  Since performance has to be measured in order to be managed, the measurement system that is used for management should be applicable to monitoring and evaluating HM interventions, as well as other efforts intended to improve workforce performance.

In fact, once healthcare providers master performance measurement for P4P reasons, they will be in an excellent position to integrate all employee-focused investments, including all benefits, aimed at improving their performance.  This should make HM efforts more efficient and effective, when they are combined with employee training and development, EAP programs, and other efforts aimed at improving the performance and retention of employees.

Moreover, healthcare providers may be able to become leaders in performance measurement, management, and integrated benefits strategies, which could be added to their HM expertise as a competitive distinction relative to specialized HM suppliers.   In most cases, such suppliers have to rely on either HM participant self-reporting, or on their employer clients’ own systems for measuring performance, while healthcare providers can afford to develop their own affordable and effective measurement systems.

We are only at the beginning of addressing the counting challenge, particularly when it comes to positive market and revenue impacts, such as improved product and service quality, customer satisfaction and loyalty, new business and similar effects that have been traced to HM efforts by at least some employers.  And if healthcare providers master the art and science of counting the full range of HM effects, they will not only be able to rise to the top in terms of their own internal HM applications, but in their marketing of HM programs to other employers, as well.