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The Name of the Game in PHM is Variability: Part 3 - Assessment

by Scott MacStravic

The sheer number of dimensions of the PHM challenge that are to be measured partly determines the relative difficulty, uncertainty, and costs involved, but the types of dimensions makes an even greater difference. One fairly common practice, however, is to count only the numbers of health risks, chronic conditions, or impairment causes, and link the number of such “factors” to the amounts of sickness care, disability and workers compensation (‘direct”) costs, and productivity or performance impairment (“indirect”) costs, for each individual in the population. This greatly simplifies the measurement challenge, but it may also mislead it.

The difficulty with assessing the specific factors of concern in PHM is that it is normally only possible to identify the overall direct and indirect costs that apply to each individual, together with the presence/absence or degree of the separate factors for each. This means that there is no basis for determining how much each individual factor contributes to the total costs for each, nor predicting how much might be saved through a particular PHM intervention addressing each. In practice, PHM interventions are customized to individuals, at least by what is usually one particular factor affecting each, though potentially for a number of such factors simultaneously, depending on the types of interventions available.

There are four common approaches to gauging individuals’ and populations’ risk/reward levels in terms of costs and economic gains: 1) past claims analysis, 2) health risk assessments); 3) biometric screenings; and 4) “psychographic” surveys. Champions for each tend to think theirs is the best approach, though there is likely to be added value in each of them that is not available in the others. The trouble is, however, that adding more methods adds to costs for insurers and employers, and to participating employees as well, which can directly threaten payers’ return on investment (ROI) by increasing the cost denominator. It can also indirectly threaten ROI by making participation either lower in numbers of people involved, reducing the economic gains achieved, or more expensive to achieve by requiring incentives to be paid, which will affect both numerator and denominator.

Claims analysis is often easiest, because the data already exist, in insurance plans or the employers’ own operational data. This will only apply to direct costs, unless the employer has a P4P system that involves already measuring employee productivity or performance in a way that can be applied to gauging their health-related impairment. Otherwise, only direct costs will be gauged by using existing data, and this will greatly understate the size of the problem and extent of economic benefit for employers, regardless of how much they pay their employees. The degree of understatement will be directly related to the amount of average compensation, with or without team/peer effect or value-based multipliers.

Claims analysis also carries with it the greatest risk of overestimating potential and actual economic gains. This is particularly true when individuals with high/outlier levels of sickness care expense in the baseline year will greatly influence the estimate of potential. Since such individuals are more likely to have lower costs in the following year, thanks to what is called “regression to the mean” than to have the same or higher costs in the next year, a major portion of the potential savings in the assessment, as well as the actual savings in the evaluation, will not be linked to the PHM intervention, but to unrelated “natural” causes.

There are half a dozen validated measures for employee productivity, which should also be fairly good estimates for performance, though I know of none validated for this added dimension of employee value. The book — R. Kessler & P. Stang (Eds), “Health & Work Productivity “, U. Chicago Press 2006 — offers the best compilation of these methods that I know of, and any one might be chosen for PHM use with some confidence. The trouble is that different methods tend to produce different results. Moreover, where comparisons have been made between employees’ self-reported impairment and objectively measured output, it has been found that employees often overstate their degree of impairment. So whichever method is used, there should be sound data available for converting employee reports into most probable reality.

In practice, many PHM suppliers use psychographic measures, along with, or even instead of the others. A few rely totally on individual’s self-reported perceptions and attitudes to predict their future costs, and the same may be used to predict their impairment. This can be inexpensive, where the survey is administered online, for example, and responses are automatically analyzed by computers. But the proof of all such assessments will be in the degree to which they lead to positive ROI based upon them. No merely “scientific” superiority should overcome pragmatic advantages.

Most suppliers who use employee surveys, whether health risk assessments or productivity/impairment questionnaires, can and usually do combine these with psychographic questions to gauge things such as employee motivation, commitment to change, confidence and self-efficacy in self-governance, for example. Answers to such questions, combined with the costs linked to each, can be used to identify and target the most promising individuals for inclusion in PHM interventions. Or they may be used to determine risk/reward segments, whose members will be assigned different levels of intensity and cost for the interventions they get.

There are equally wide variations on how identified risks are defined. “Depression”, for example, may be defined as a diagnosis, with only a small portion of the population identified as having a diagnosed “disease”, or as a “disorder” where a far larger portion is affected, or as an “emotional state” where an even larger portion would be identified as having it. It may even be defined as a general catchall covering all emotional problems and “down” feelings, including anxiety and stress, making it likely to affect the majority of the population.

The variety across internal PHM programs, as well as outsource suppliers, in the methods they employ to assess the baseline situation, which are typically repeated in the evaluation process, add greatly to the overall variability across the large numbers of options available to employers and insurers. We can hope that in the future, there will be improved identification of which are truly the “best practices” in PHM, though there is likely to be constant experimentation with new methods that will compete with even those proven best in the past.

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