The same error in logic keeps turning up in evaluations of proactive health management ((PHM) efforts, with recent examples relative to both disease management and prevention.  It is the attempt to reach an overall conclusion about PHM in general, as well as its various components, as if each is a uniform “solution” to a single “problem”.  Since this is simply not the case, all such efforts are doomed to failure from the start, but manage to capture headlines when they are reached, nevertheless.

It is difficult, and probably arrogant even to attempt, to conclude whether this consistent pattern is the result of ignorance or deliberate choice, but both the media that report results in such cases, and the organizations that sponsor or conduct the analyses, are prone to the same mistake.  Medicare, for example, consistently attempts and publishes conclusions about its disease management (DM) demonstration projects as if DM were one consistent, universal “treatment” to one consistent medical condition.

In a recent example, the published article on the subject reminded us of DM’s failure to save money. [R. Abelson “Medicare Finds How Hard It Is to Save Money” New York Times, Apr 7, 2008]  This conclusion is based on results from eight DM suppliers who remain in a demonstration project begun in 2005, addressing different populations with different conditions by means of different interventions.  Yet both Medicare and the report strive to arrive at a general conclusion that DM does not save money.

A similar conclusion was reached by another newspaper with respect to the value of prevention, in general. [D. Brown “In the Balance” Washington Post, Apr 10, 2008 (www.washingtonpost.com) ] It cited a series of studies conducted since 1986 that have come to the conclusion that prevention costs more than it saves, citing examples such as costs as high as $160,000 per life saved for men, and $240,000 for women with high levels of risk for heart attack, through conventional medical treatment therefor.

The problem with prevention, in general, is that people vary widely in their risk of actually contracting a disease, even if they have similar risk indicators.  It is necessary to treat the risks in many more people than will would have turned out to be sick as a result, so it may be necessary to treat ten or a hundred more than actually deliver the desired savings of not having a disease that they would otherwise have had.

It is an unfortunate reality of the model for evaluation used in such cases that its very rigor in stipulating what is “scientific” works to limit the probability of success in what is evaluated.  The best way to succeed in either DM specifically, or prevention in general, is to treat both as a “marketing” challenge – identify which people are the best prospective “customers” for using the proposed “product”, specifically in terms of their potential for success.  Then, customize the intervention to match both what such prospects are most likely to “buy” and what is most likely to deliver a positive return on the investment involved.

Such a matching process, i.e. making the intervention fit the individual prospect — in terms of both likelihood of their “buying” or engaging in it, and probability of doing so realizing as much as possible of their individual potential for delivering savings – would optimize the return on investment. Unfortunately, it would also violate the “rules” of scientific evaluation from beginning to end.  It would not provide a single form of the intervention, but one that varies by individual, and not to a randomly assigned group, but one composed of people who were “self-selected” to different interventions by design.

Marketers have long understood that this is the best approach to maximizing response among prospects. By at least segmenting populations by purchase potential and propensity, plus tailoring messages accordingly, the success of advertising efforts can be significantly improved.  Identifying the best targets for DM or prevention makes equally good sense, in terms of promoting success.  Unfortunately, it would require a far more complex application of science to demonstrate this, and too often, only a one-size-fits-all intervention, applied to a heterogeneous population, with no differences permitted is the rule for science.

Even the frequent reports of disappointing or equivocal results from DM and prevention include examples of particular interventions that do work, along with those that do not.  It is only the overall picture that is reported, whereas it would make more sense to be delighted by even a few examples that work, in order to determine how to improve interventions, rather than condemn all with the same brush.  If the same logic were extended to identifying which are the best prospects, and customizing interventions, the overall picture would probably be considerably more positive.

Moreover, we would learn more about what does work, instead of denying ourselves potential gains because a majority of science-restricted interventions do not.  Presumably the idea is to gain success, in reducing sickness care use and expense, improving the quality of life of consumers, saving money and improving performance for employers.  The aim for success should be the dominant concern when planning, implementing, and evaluating DM and preventive interventions, not scientific restrictions that reduce the chances of such success.