Archive for Population health management
by Scott MacStravic
May 12, 2008 at 11:49 am · Filed under Disease Management, Population health management
Conventional wisdom in PHM has long adhered to the notion made famous by Willy Sutton, the famous robber, who explained his predilection for robbing banks by noting that they are where the money is. Translated into PHM terms, it leads to identifying those people – members of health plans and employees – who cost the most money. This logic became the major reason for the growth of the disease management (DM) industry, once it was realized that people with chronic disease account for roughly 75% of all health care costs.
This was the original logic that prompted investments in DM, and it persists today, despite the fact that federal government studies persist in finding, at best, equivocal or uncertain evidence as to the return on investment delivered by DM providers in practice. An article just last month, for example, noted that “Health plans are not effectively reaching the sickest Americans…” according to the latest Silverlink Healthcomm Behavior Index. It concluded that one recommendation for improving health and reducing costs is to “…focus on those who are most unhealthy. [L. Masterson “Getting Personal Engages Members” HealthPlans.HCPro.com, Apr 30, 2008]
While the idea may seem self-evidently true, or at least logically justified, it is more often blessed with the appearance than the reality of how disease and health management can be best applied. Even in the narrow context of PHM applied to health insurance plan members, selecting the sickest members has not turned out that well for DM providers, at least not in the Medicare demonstration projects that persist in yielding equivocal or disappointing results.
The basis for selecting targets for PHM interventions should be the economic and other value that can be obtained thereby, not simply the severity of the problem individuals may have. In the Medicare examples, often the problems addressed were simply too severe for the solution to fit well with, given the cost of the solution compared to the benefits it delivered. It is the benefit to cost relationship that should determine who will make the best prospects and participants for PHM.
Moreover, the relationship should be benefits minus costs, more than benefits divided by costs. The ROI ratio available from any single prospect or participant, or from a given PHM intervention relative to a population, should only be an indicator of value, not its calculation. The highest ROI ratios, almost automatically, will tend to arise from the lowest cost interventions, merely because their denominator is low. But if we rely on the ratio, alone, it is too easy to miss far greater opportunities for ROI amounts that require a bit more investment to achieve, and deliver lower ratios as a result.
The best prospects for PHM success will be the individuals and health problems that contain the greatest potential for delivering value, not merely reflect the worst problems. Smoking, for example, is often cited as a catastrophic risk factor, considering the great many diseases to which it can be connected. But smokers are often the toughest people to “reform”, and deliver only low economic benefit to payers per participant in smoking cessation initiatives, because so few quit and remain abstinent long enough to deliver significant benefit.
By contrast, some “minor” health problems, such as sleep deprivation or lack of physical fitness may have far higher success rates in PHM efforts to reform them, and deliver far greater benefit faster, with slightly higher costs per participant. Self-service methods such as visiting web sites and support communities may be so inexpensive as to enable practically all members of a population to be directed to use them. This may yield a high ratio based on modest improvement for even more modest cost, but fall far short of a more intensive approach aimed at another problem, or the same problem.
Since employers are not permitted to know much about the health of their employees, only medical care and PHM providers can normally select who are the best prospects among individuals. But populations and problems should be selected based on the overall ROI amounts available thereby, as long as the ROI ratios for the interventions available represent an attractive rate of interest on the money involved. Targeting based on severity of the healthcare costs or productivity/performance impairment levels alone will only yield the best choices by chance, not because they are the worst problems around
by Scott MacStravic
May 5, 2008 at 2:33 pm · Filed under Population health management, Variability in PHM Series
Once members of the population have been targeted for participation in particular PHM interventions, the next step is to recruit as many as possible, or at least as many as will contributable value per participant, to each intervention. Such recruitment may actually be part of the assessment process, when members of the population have to be persuaded to engage in a health risk assessment or biometric screening process, for example. In any case, this element is yet another in which there is wide variability across the methods used by different DIY payers or the suppliers they hire.
One of the simplest is the “opt-out” strategy, in which every member of the population targeted is automatically enrolled without their involvement, once the targeting process is complete. They are usually individually notified of their enrollment, and invited to take whatever the first step in actual participation is. In some cases, they are automatically assigned a coach who will call them, or are automatically sent online or mailed information that is part of the intervention process.
This method often results in 90-95% enrollment, since only those who explicitly opt out are deemed non-participants. But the significance of their being enrolled lies in their active participation, cooperation, and changes in behavior, and the rates of each may be quite a bit lower than when other enrollment strategies are followed. The major two options with “opt-in” interventions are: 1) targeting each for a particular intervention based on the identified most promising PHM intervention for each; or less common 2) asking each target to choose among a number of options based on recommendations customized to each.
The enrollment process has to balance two considerations: 1) which intervention promises the greatest return to the investor; and 2) which has the greatest likelihood of delivering on that promise. The opt-out approach probably is strongest on the first criterion, and weakest on the second. Inviting targets to enroll in an intervention pre-selected for each is probably medium on the first and on the second criteria. The “self-determination” approach would probably be weakest on the first and strongest on the second. So it is a balancing act to select the best approach, and one or two may be tried before the best is identified in each case.
The “style” of the intervention and enrollment effort may also make a major difference to the numbers who enroll, participate effectively, and succeed. Early PHM interventions were mostly “one-size-fits-all designed and implemented by the sponsor or supplier. Increasingly, however, interventions are either partly or totally individualized, by either the person who contacts the target, or the automated online/mail communications based on analysis of assessment information. And the customized approaches tend to work far better.
Enrollment is also influenced by whether or not financial or other incentives are offered – for enrollment, participation, or success. Incentives paid for enrollment should achieve the highest enrollment, but not necessarily the highest levels of enthusiastic participation. Incentives paid for participation in or completion of an intervention may achieve high levels of participation, but not necessarily of success. Incentives paid for success may promote both enrollment and participation, particularly among those with the greatest confidence in their probability of succeeding.
Of course, people often delude themselves. Repeated studies have found, for example, that those with the highest levels of capabilities tend to underrate themselves, while those with the lowest levels overrate themselves. If the assessment process identifies targets based on predicted probability of success, it should be safer to pay incentives for participation, since those with low probability, despite confidence therein, would not be targeted. Those with high probability, but perhaps less confidence, would be encouraged to participate by the incentive, where they might not be as encouraged by a reward only for success.
Since incentives add to the costs incurred by insurers or employers, they have to be used carefully. PHM suppliers or peers who have used incentives, if they share their experience, may provide a basis for choosing which type and amount of incentive may work best, though each population is likely to be unique enough to make actual experience with it the best guide in the long run. The combination of customization and the right incentives is likely to work best, judging by experience in PHM so far.
by Scott MacStravic
May 5, 2008 at 2:33 pm · Filed under Employer CEOs, Population health management, Variability in PHM Series
The first purpose of the assessment process is to determine the size of the PHM challenge and of potential gains from implementing a PHM strategy. The assessment process is then also used in the evaluation process, to identify changes in measured “success” dimensions against the baseline data. But another key use of the data is to identify and select which members of the population make the most promising targets for participation in which PHM intervention, together with how many available interventions will likely justify their investment.
This step begins with identifying the baseline costs, both direct and indirect when applied to employee populations, linked to individuals and the health-related factors each has been found to have. Usually, without extensive multivariate analysis and predictive modeling, the “potential” is estimated based solely on the baseline costs. But the real predictor of potential economic gains lies in the particular PHM interventions that will be applied on a DIY or outsourced basis. And this information is peculiar to each PHM strategy, intervention, and supplier, rather than any generalizable average for PHM as a whole.
Moreover, when measurement problems such as side-by-side comparison “self-selection bias” and before/after “regression to the mean” effects have been overcome, there remains the inherent difficulty of predicting realistic potential cost impacts or other benefits based on past data. While past data may accurately describe the past of the very population to be addressed by PHM, it does not translate easily into predictions of the future. And while a given PHM supplier’s own past performance may be “statistically valid and reliable”, it may not be a sound basis for accurate prediction of results in a new population.
Within these limits, PHM investors and suppliers will want to determine which and how many population members represent the best risk/reward potential for which particular PHM interventions. Some suppliers and employers have employed a completely customized approach to PHM, where each individual either chooses each’s own goals or is assigned a coach who will customize the intervention to the individual’s mix of challenges, and address as many of each’s individual problems as possible. Experience suggests that individuals can handle more than one, but rarely more than two or three, so the number of problems to be addressed is normally small.
In many cases, there is one specific problem that is the basis for the PHM intervention to which members of the population will be invited. This does not mean that the intervention will have a very narrow focus, however, though it may. A smoking cessation program normally focuses exclusively on enabling participants to quit, but may have to address a wide range of perceived barriers and related issues, including stress and weight management along with the smoking behavior, per se. And diabetes disease management interventions routinely include attention to blood pressure and cholesterol levels, in addition to blood glucose.
While the baseline costs linked to individuals may guide targeting to some extent, the psychographic data indicating the probability that each will make a needed change and achieve success thereby, together with PHM suppliers’ demonstrated performance will add significantly to how “informed” the choice of targets can become. In many cases, the supplier may guarantee results for particular population segments, interventions, or success dimensions, making the targeting that much better informed.
Targeting necessarily includes consideration of how PHM suppliers charge, and what additional costs may be incurred by targeting more and particular segments or individuals for interventions. When the supplier charges on a “per population” of flat fee basis, it will not add fee costs to include as many members of the population in any given intervention, though most charge a fee for each separate intervention, which will multiply costs by the number of interventions selected, rather than the number of members participating.
When the supplier charges per participant, perhaps with graduated fees based on the risk/reward potential it determines for each and the different intensity/expense for the graduated interventions, it is the number of members targeted for each intervention that will represent the upper limit on fees. Moreover, payers will likely incur additional costs, per member, per participant, or even per successful participant (e.g. when incentives are offered for success rather than participation alone), which have to be considered as well. And while payers may recommend steps their clients should take that incur costs, the client will have the last word on which kinds of support it provides and what it will pay for what.
There will always be the unknown costs of participation or success incentives that will have to be paid, but this will typically be substantially less than the economic gains associated therewith. The past performance of the supplier applied to the baseline costs and economic value of members of the actual population to be addressed should help in ensuring that the client’s total costs are kept lower than the client’s benefits. Controlling the number of members targeted and number of interventions invested in based on risk/reward vs. predicted or controllable costs, is the best overall approach to take.
by Scott MacStravic
May 2, 2008 at 12:33 pm · Filed under Business of Health, Population health management, Variability in PHM Series
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.
by Scott MacStravic
May 2, 2008 at 12:31 pm · Filed under Business of Health, Population health management, Variability in PHM Series
The first basic split among PHM clients in terms of which dimensions of economic impact will be used in assessing problems and evaluating gains is that between commercial and government insurers on the one hand, and employers on the other. Insurers focus mainly, though not exclusively, on reductions in sickness care costs as the economic gains they are after, with many true “health” care costs part of the PHM intervention. They may also look at member/beneficiary health status and satisfaction with their PHM experience, as well as the duration of their “membership” in specific insurance plans.
Employers tend to look at a far greater number of dimensions, though these include the same ones that insurers look at, since they pay for such insurance, or if self-insured, pay the costs directly. But in addition to sickness care costs, employers worry about workers compensation and short- plus long-term disability (STD and LTD) insurance or direct costs as well. These may be as great as are insured sickness care costs, with some employees, though they are normally far less overall.
The really large economic benefits sought in PHM by employers tend to be that derived from reductions in absences and both productivity and performance impairment while at work (“presenteeism”). Economic losses from lost productivity/performance has been measured at levels two to five times as great as the sickness care costs associated with the many health-related problems that cause impairment. Moreover, employers may achieve economic gains through “positive presenteeism”, the potential that healthy and happy employees will contribute significantly more economic value than is “normal”, not merely “impaired” levels.
While productivity, per se, is the more frequently included dimension, compared to performance, there have been a few cases where specific benefits of improved performance have been addressed by employers. These include employee impact on customer satisfaction and loyalty, word-of-mouth impact on new business, and similar revenue-related benefits, in addition to labor cost reductions. And many employers have examined improvements in employee retention as adding value in the balanced scorecard dimension of “organizational knowledge” as well as reducing costs of replacing employees who leave.
The determination of which dimensions to include affects the complexity and costs of the PHM effort, whenever measuring these dimensions requires going beyond information already being collected as part of normal operations. This is less often the case for insurers, since claims data is frequently all they look at, though if they also consider member health status, satisfaction, and retention, for example, special efforts and added expense may arise in collecting such information.
With employers, complexity and costs of measuring dimensions of the PHM problem and progress in its solution are likely to be significantly greater than for insurers (though insurers may have to address these when they provide PHM services for their employer clients). Measuring workers compensation and disability expenditures is normally routine, already being tracked overall, though there may be added effort used in analyzing collected data so that it serves better to guide and evaluate the PHM effort, itself.
The biggest challenges will be in gauging productivity and especially performance impairment levels and their reduction, i.e. the economic benefits of improving either or both. While productivity is susceptible to both objective measurement in some cases, and validated estimates via surveys that collect employees’ and sometimes team supervisors’ perceptions of output, performance includes multiple dimensions of its own.
Of course, some dimensions of performance, such as customer satisfaction, retention, market share, new business revenue, etc. are likely to be routinely measured. But they are normally measured on an overall or market/segment-specific basis, rather than linked to individual or segments of employees, except for sales results linked to specific sales staff members. Customer satisfaction may be linked to the specific employees known to have served them, where this is likely to be the same individual or team over time, but market share and new business increases may have to be credited more widely, perhaps to the PHM effort overall.
There are probably a half dozen commonly used methods for estimating productivity based on employee self-reported data. These may directly ask employees to recall how many hours of lost productivity they have had in a recent period, perhaps a week, two weeks or a month, where their memory may be reliable. Or it may ask for an estimate of the percentage of their full potential they have been able to deliver, considering their health and any problems identified, that may affect their output.
Similar surveys may be used to gauge the levels of performance employees have been able to deliver, since both absence and presenteeism will likely affect performance as much as they do productivity. And the impairment found in individuals may be “multiplied” by their estimated “team/peer impact”, which has been measured at between 1.25 and 1.35 times the impact of the individual’s absence on average. [“Multiplier Effect: The Financial Consequences of Worker Absences” Knowledge@Wharton Dec 14, 2005 (knowledge.wharton.upenn.edu)]
The economic impact of absent or impaired employees may also be based on the economic value of employees in terms of their overall contribution to the employers’ performance. When employees are paid on a “pay-for-performance” basis, their performance will naturally be measured, though it may prove difficult to determine the extent to which any individual is impaired by their health problems, as opposed to other non-health-related limitations. Fortunately, once performance improvements have been linked to PHM interventions, the measured performance may be more easily translated into economic benefit.
By far the most common approach currently used in evaluating the economic impact of employees’ health-related impairment is to multiply their degree of impairment times their annual compensation levels. This means the different employees will have different costs of lost productivity, whenever they have different levels of compensation. It also fails to take into account any team/peer impact or the likelihood that employees are worth more than they are paid. One study, for example, found employees worth an average of 3.28 times their annual compensation, though the median was only 1.92 times. [P. Strassman “How Much Is an Employee Worth?”, Jan 14, 2006]
While insurers have relatively few and commonly used dimensions when measuring their PHM problem/challenge and the results of their PHM investments, employers have far more complex and potentially costly choices. Fortunately, employers can use the same relatively simple and inexpensive-to-measure dimensions as do insurers, and when they go beyond those, they should also be gaining many times the cost of doing so in the added value they discover across the full range of benefits in having a healthier/happier workforce.
by Scott MacStravic
May 2, 2008 at 12:30 pm · Filed under Business of Health, Population health management, Variability in PHM Series
Population Health Management (PHM) has become a major market for insurers, governments, and employers to invest in – and for a wide range of organizations, from traditional healthcare providers to specialized PHM suppliers to insurers to deliver to that market. The numbers of suppliers keeps growing as the number of payer clients does also. The market is beginning to look like the early stages of many new markets, from automobiles to radios to TV sets, cell phones, and electronic gadgets in general – it offers a vast number of options to purchasers, including the “do-it-yourself” (DIY) alternative to purchasing PHM.
Many traditional healthcare organizations (HCOs), from physician practices to hospitals to integrated health systems (IHSs) have entered this market, as DIY providers to their own workforces, or as competitors in marketing PHM services to payers, mainly to employers. They may do so in order to support their sickness care services marketing strategy by creating and sustaining stronger and more lasting relationships with employers as influencers of both insurance plan decisions on provider networks, and employees’ choices of providers. Or they may see the PHM market as one they would rather join than merely suffer its sought-after effects in reducing sickness care use and expenditures and thereby their sickness care revenue.
Employers have been involved in a “toe-in-the-water” approach to PHM since the 1970s at least. Many began with some kind of DIY “worksite wellness” program for their employees, and a growing number are offering onsite medical clinics that engage in PHM along with traditional sickness care. But most appear to be outsourcing the PHM function to specialized suppliers or HCOs, since there are so many complications in their knowing about their individual employees’ health problems — and most have no great competence in PHM.
Insurers have often been influenced by their own interests or their employer clients’ demands to become PHM DIY suppliers, as well as insurers, to employers, at least. Both Aetna and CIGNA, for example, offer PHM services to employers other than those to which they provide health insurance plans. Since insurers can gain substantial experience, and with large populations, by delivering PHM services to their own insured populations, they can gain both the competence and track record needed to become viable suppliers in the overall market, as well as capable DIY providers for their own health plan members.
Specialized PHM suppliers have been in the PHM market, as operators of worksite medical care clinics for employers, or suppliers of PHM services for commercial and government insurers, for a few decades. There has been a significant movement toward merger and acquisition among such suppliers, resulting in consolidation of their numbers, but many new ones keep entering the market, so the overall numbers of suppliers is still huge. And while some have captured large market shares, there appears to be no single supplier who could be called dominant.
One of the reasons for this is the vast degree of variability among PHM suppliers, due to both their large numbers and the fact that there is nothing close to agreement on what are the “best practices”, the most cost-effective approaches to PHM in any one of its essential elements. These elements include:
- Determination of what dimensions of economic impact will be included in the PHM planning, management, and evaluation
- Assessment of the PHM current state of these dimensions in whatever population (insured plan members, employees, dependents, retirees) is to be involved
- Targeting of which individuals shall be sought as participants in either standardized or differentiated PHM approaches to particular problems, risks, or potential gains are to be addressed
- Recruiting targeted individuals and segments to become active, engaged, and enthusiastic participants
- Sustaining participants throughout the PHM intervention program, at least long enough to achieve some desired effects, if not to the end of a limited intervention, or on a continuous basis if there is no intended end.
- Carrying out the intervention process on those participating, using a wide range of interaction and communications technologies and methods
- Evaluating the effects of interventions over whichever time frame(s) clients wish
Not only are there vastly different approaches to each of these elements being used by different PHM suppliers, but clients may engage different suppliers for two or more of such elements, including doing one or more themselves, or at least sharing the responsibility with outsource suppliers. As a consequence of the numbers of and variations among suppliers, the overall PHM market contains far more variation than is true for almost any other example in the business-to-business (B2B) or business to consumer (B2C) markets.
In subsequent postings, I will describe and discuss the seven separable elements in PHM that are subject to such variations. The combination of this number of basic elements, and the variability in how they are carried out by different suppliers, even within the same supplier for different clients, is what makes the PHM market so replete with variability.
by Scott MacStravic
May 1, 2008 at 9:14 am · Filed under Business of Health, Population health management, Marketing
Honesty is required by law in advertising of medical treatments and pharmaceuticals, though both are subject to some “overenthusiastic” promotion by physicians and drug companies, alike. On the other hand, there have been even more cases of overenthusiastic promotion by manufacturers of vitamins and food supplements, as well as providers of “alternative medicine” services whose methods have not been subject to scientific proof of safety and effectiveness before being advertised.
The regulation of treatments and products used in sickness care has long been a major effort in the interest of protecting patients from unscrupulous manufacturers, retailers, and providers who can easily take advantage of patients desperate for something that works, or those who rely too much on emotional vs. rational bases for making decisions about the care and providers they seek. There are certainly a large number of complementary and alternative medicine treatments that have solid evidence behind them. But in some ways, this makes it easier for unscrupulous sellers to make the case that their offering will work, by citing other examples where medicine has been wrong in concluding that previous CAM therapies were worthless.
The growing popularity of health management, of persons and populations (both deserving to be labeled “PHM”) has opened up a large new market for CAM therapies. Where CAM providers have achieved greater credibility among their patients for their approaches, and even greater success in terms of bang for the buck, thanks to their holistic approach to patient care, or their ability to enlist more enthusiastic collaboration among patients, they may be significantly more successful than are traditional physicians, at least in terms of benefits vs. costs.
Achieving a greater benefit/cost ratio is sure to make CAM providers more popular among payers, whether governments, commercial insurers, or employers. A growing number of insurers, for example, are offering, and employers as well as consumers selecting, lower-priced coverage plans that involve more use of CAM providers for health management of sickness care services. The generally lower prices they charge for their services, and lower overhead/operating costs for their practices, make CAM providers more likely to be able to compete on costs, at least.
The challenge in PHM is to promote honesty in advertising by its providers, whoever they are – specialty organizations that focus on PHM, traditional providers, or CAM alternatives – about what kind of results they are getting for what kinds of costs. If honesty in advertising were enforced in PHM, then unscrupulous or simply ineffective providers would be severely limited in their ability to attract payer clients, or even consumers, whether they pay out of their own pocket, or have a third party doing so.
There would be a significant number of current PHM providers who would probably be forced out of business if there were forced honesty in advertising, or even if there were the kind of comparative testing and reporting of outcomes and providers as is increasingly true with sickness care. Commercial insurance plans are already talking about developing and rating the performance of physician practices in terms of managing the health and costs of patients with chronic diseases. It would be relatively simple to do the same for practices engaged in protecting and improving their patients’ health, such as the MDVIP retainer medicine practices, now numbering over 200 in the US.
If honesty in advertising were required across the board in PHM as well as in sickness care, there would naturally be the same two effects as already noted with publication of comparative quality in sickness care. The lower-performing providers would strive and many succeed in improving their performance to make themselves more competitive with their higher-performing rivals. Or they would be forced out of business, as more consumers and payers would be able to “Buy Right” in PHM, as well as sickness care.
It will take a major improvement in the numbers of payer clients forcing and financing rigorous evaluation of the actual performance that PHM providers achieve. This will have to be done on a set of comparable outcome dimensions, rather than only those that individual PHM providers choose to measure or report. And there would have to be the kinds of rigorous analysis of the different results that different PHM providers get as has already been done in sickness care, and even in disease management D(M), though for the wrong reason.
Instead of rigorous scientific analysis of a number of different PHM providers and methods, there should be equally rigorous analysis of individual PHM providers’ results across their entire book of business. And instead of pursuing a ludicrous and futile answer to the general question of whether PHM works, as has characterized reviews in DM, these analyses should aim to develop comparable performance data on competing PHM providers to identify which do the job best.
This will speed up the ability of PHM sponsors and buyers to identify and selectively prefer those PHM providers who have been shown, in objective, accurate, and rigorous ways, to deliver the best outcomes. Ideally, these “best outcomes” should include both economic effects on payers, and personal health/life quality for those persons and populations that participate and invest their own time and effort, as well as their money in many cases, to achieve these outcomes.
The same amount of money already wasted on answering the unanswerable general question of whether it works could go a long way toward identifying which PHM methods work best. The general question is unanswerable because PHM, as is true for DM, is simply not one “treatment” that can be examined across different populations and problems to find out if it works. PHM and DM are a wide range of significantly different approaches, with highly varying costs and intensity, being applied to highly variable sets of problems and populations. The individual programs that do work should be the focus of analysis, not the collection of diverse programs, where some do and some don’t, virtually guaranteeing the almost always equivocal and uncertain results of studies addressing the general question.
Armed with comparative, rigorous, reliable and valid data on the performance of competing PHM methods and providers, the entire discipline and market of PHM could become dramatically more effective and efficient, and in a far faster time than is possible without such an effort. When the results of publishing such data are combined with regulated, honest advertising, PHM would have its best chance of succeeding, for its providers, its payers, and the populations that should be benefiting from such success.
by Scott MacStravic
April 30, 2008 at 1:16 am · Filed under Business of Health, Population health management
Predictive analytics (PA), the science of collecting, analyzing, and applying information about customers to promote the success of firms that use the information successfully, is a rapidly growing element in marketing and sales, as well as customer service efforts. It includes two main aims:
- to identify the potential worth of different customers in order to tailor the level and costs of effort to the relative value of each, and thereby improve the ROI from investments in customer acquisition and retention; and
- to tailor the kinds of communications and experiences delivered to customers to optimize the ROI from acquisition, “development” (usually means up- and cross-selling/marketing), and retention investments
[J. Tsai “Predictive Analytics Foresees Change in the Future” DestinationCRM.com, Oct 30, 2007]
While these applications have had a long history in marketing applications, they are only recently being used in population health management (PHM), of commercially or governmentally insured, and particularly employee populations. The application of the concept of predictive analysis in PHM has been slow to develop, and its applications still fall far short of those in customer marketing, sales, and service, though it is moving in the direction of more sophisticated and comprehensive efforts.
Initially, PA focused primarily, if not solely, on identifying those members of a given population who had the highest level of current or past expenditures, and perhaps the greatest risk of future expenditures. This usually meant identifying people with expensive chronic diseases, whose conditions had recently been diagnosed, involved a significant crisis or complication. Because they had already generated high expenditures, this often meant closing the barn door well after the horse had departed. It also added to the widespread early tendency for PHM results to be exaggerated by ignoring the tendency for high-cost individuals to “regress to the mean” in the following year, whether or not any PHM intervention had been applied to them.
Gradually, PA has grown to identify those at risk for high expenditures in future, including those at risk of contracting new diseases, both acute and chronic. By adding those at risk, a far larger population of potential PHM participants can be included in PHM efforts, and a far broader range of expenditures can be avoided or reduced. Where chronic disease management may target 5-30% of the population, increasing with the average age thereof, it usually engages only a minority of those targeted, meaning that only tiny minority of the total population is involved, and many long after they have generated their highest levels of costs.
By adding in not only disease risks, in employee populations, but productivity/performance impairment factors, PHM can also greatly expand the measured costs and benefits of PHM investments. Typically, the costs and savings related to improving health and thereby reducing absenteeism, presenteeism, and performance impairment, as well as creating positive impacts on both, is from two to five times as great as are “direct” healthcare, workers compensation and disability expenses alone. And these are usually improved far more through addressing impairment factors such as general emotional problems, lack of sleep, stress, poor nutrition and hydration, etc. than through disease risk factors, alone.
PA really adds value when it is also applied to identifying which members of a given population are not only most expensive, at risk or impaired, but also most likely to respond to PHM interventions. Analysis of individuals’ “stages of change”, “internal vs. external locus of control”, self-efficacy, motivation, perceived personal benefit, etc. can differentiate those who are likely to make the necessary behavior changes to succeed vs. not can make PHM interventions more effective and efficient.
For example, HealthMedia, Inc. Ann Arbor, Michigan employs an online health risk assessment (HRA) that includes not only questions used to estimate productivity impairment, but to identify which individuals are the best prospects and what kinds of support will have the best impact on them. American Healthways, Nashville, Tennessee rely on their personal nurse coaches to learn about and incorporate individual characteristics, attitudes and barriers — not merely in general, but at each coaching session – to customize their coaching accordingly, in addition to using HRA analysis upfront.
Healthcare organizations are already using simple tools to identify individual characteristics and facts that enable them to customize sickness care experiences. St. Jude Medical Center in Fullerton, California, for example, uses a set of 10-12 questions that patients are asked to answer, so that physicians, nurses, and other staff serving them can identify interests or experiences they have in common with patients, adding to the personalization of interactions, particularly about reasons that will motivate patients strive to get better. [N. Vssell “Simple Tool Helps Personalize Health Care” Strategic Health Care Marketing, 24:11 Nov 2007 5-7]
Duke University Health System can customize its support to eligible participants in its Duke Prospective Health Program based on the individual health vision and mission statements each participant is asked and helped to complete at the beginning of PHM efforts. By understanding the personal motivations different employees and dependents bring to PHM participation, personal coaches can more explicitly tie their efforts to the reasons that drive participants, not merely the cost-saving reasons that drive the system. (www.dukeprpospectivehealth.org)
By combining a broader focus for interventions and measurement of their impacts with more predictive understanding of which prospective participants have the best prospects, and what kinds of interventions will work best with each, PHM can multiply its probability and value of success many times. As predictive modeling technology improves the accuracy and precision of its analytics, PHM figures to deliver increasing probability and value of results, along with greater efficiency, effectiveness and ROI for its investors.
by Vijay Goel
April 21, 2008 at 10:48 am · Filed under Consumer Engagement, Pay-for-Performance, Prevention and Health Promotion, Business of Health, Value-based health care, Population health management
George Halverson, Kaiser Permanente’s CEO gave a keynote earlier today at the World Health Care Congress in Washington DC. The statistics he gave were compelling. The opportunities, also, really interesting. From a consumer perspective, the prescription he wrote was not– heavy on centralized best practice reminiscent of the socialistic command & control approach rather than incentives for innovative practice.
The issues today are pretty clear– we are focusing our resources heavily on the sickest individuals.
- 1% of the sickest consume 35% of the health spend
- 10% of the population consumes 80% of the health spend
Even more compelling are the stories of conflicting interests, where an institution such as Virginia Mason is able to significantly reform health costs through better treatment up front (in this case imaging)– only to find a 30% revenue cut putting the institution at a disadvantage in being able to meet payroll and overhead expense.
But these innovations, although they lowered costs and seemingly were good for patients, hurt Virginia Mason’s bottom line. For example, “the big employers saved $100,000 in the first year. But Virginia Mason fell into the red on the average migraine case, instead of breaking even as before.”
The diagnosis was clear– hospitals and hospital systems make such a large sum off of “excess” care, that they can’t afford to get off the gravy train by doing the right thing.
In my mind, this is where the solution laid out was exceedingly non-consumer friendly.
Halverson suggested that universal mandates are required to make health affordable– taking spend for the sickest 1% from $12K/month down to a more manageable $300/month. This makes sense if one looks at the purpose of insurance as a mechanism for wealth redistribution/ wealth transfer. Thus, universal healthcare has an individual mandate– the healthiest are subsidizing the sick at a level that they can’t reasonably expect to recoup.
From the viewpoint of the healthy consumer, spending $300/month for no benefit is a poor economic choice. The business model for insurance in fact rests on a different trade-off, the payment of an underwritten premium that matches actuarial risk against level of insured protection (e.g., amount of potential claim payment one could attain if the risk in fact occurs). The healthy consumer then faces one of two choices– pay premiums to insure against future risk or opt out of insurance altogether. As insurance costs go up, and are focused on highly technical solutions with marginal benefit, we would expect to see the largely healthy opt out, as we are starting to see in the employer health insurance market, via CDHP plans or dropping the benefit completely, as the job. Employers are increasingly showing that they believe health insurance’s cost is not a good value for the job(s) it was hired to perform.
The solution is unlikely to come from the hospital system that is disincented to cut its own throat by reducing the cost/ delivery of high tech care. Instead, how can we create incentives that increase reimbursement/ wealth for those that reduce the shift of the “healthy” 80% into the “sick” 20%? How can we also create better value for those currently seeing minimal value for their contributions, and limit the spigot being poured, without accountability, into the sickest 1%?
Note: this post is cross-posted on Consumer focused care
by Scott MacStravic
March 26, 2008 at 1:24 am · Filed under Health Plan/Payer CEOs, Business of Health, Population health management
For many years, the news on “retail clinics” consisted almost entirely of stories of new ones opening everywhere. But recently, we have seen a series of stories of existing clinics closing, in a wide variety of places for a variety of reasons. It may be that, like so many innovations, retail clinics will follow the same kind of boom and bust history that has affected others, due to the fallacy of composition.
While there are many versions of this logical fallacy, its application in this case is the expectation that since the first examples of retail clinics are successful, all subsequent examples will be, also. Such optimism has affected investors in automobiles, where in the early part of the last century, literally hundreds of different companies emerged making cars, with only the “big three” having survived till the present, and their future not guaranteed. While retail clinics started slowly, they have burst into the hundreds with predictions of thousands in recent years.
There had been an earlier “boomlet” in retail clinics starting in the 1970s and 80s, when “urgent care” clinics, staffed by physicians, and operating primarily on evenings and weekends, emerged in many large cities. These served much the same market as current retail clinics, and there has been a resurgence in this kind of retail clinic, along with the ones staffed by nurse practitioners or physicians assistants. Indeed, the most recent example of the Medical Mart’s demise, closing over a dozen clinics in retail outlets if Illinois. Missouri, Utah and Virginia, was a physician-staffed model. [B. Japson “Medical Mart Clinics Close in Suburbs” Chicago Tribune, Mar 12, 2008]
Physician-staffed models have avoided the criticism by physician associations that has affected the NP/PA-staffed versions, since they have physicians present at all times, who can therefore treat all the illnesses they are likely to see, while NP/PA versions have been criticized for their limited capabilities. The trouble is, of course, that physician-staffed models tend to have far more staff on hand at each, often a couple of physicians along with support staff, so they are far more expensive to operate, and therefore far more at risk if they do not attract enough patient volume.
The Medical Mart clinics, for example, had four staff on hand, two physicians supported by two medical assistants or licensed practical nurses. This meant they automatically had over four times as much staff costs, along with larger space they were paying for. Moreover, they typically have the kinds of equipment that physicians want in diagnosing and treating patients, adding still further to their operating costs. Without perhaps five times more patients being seen than needed for NP/PA-staffed clinics, they could not survive.
There has always been a complementary function that retail clinics could serve, one that could offer many more reasons for patients to visit many more times each per year. That is the proactive health management (PHM) function, which consumers are increasingly being expected as well as “incentivized” to adopt. Both the cost shifting by employers and the move toward “consumer-directed health plans”, with their high deductibles and consumer-owned health savings accounts, provide significant motivation for consumers to do more about protecting their health and preventing disease and injury where possible.
While all retail clinics may offer minimal preventive care, including annual check-ups, flu shots and other immunizations, for example, there is already a model for significantly more comprehensive continuous PHM services that has grown almost as fast as retail clinics. So-called “concierge medical practices” most of which include a major PHM component as justification for charging a thousand dollars or more in annual “retainers”, have grown to include over 600 physicians, by my count.
In addition, there is at least one retail clinic chain, the RediClinic examples, that combines what it deems “Get Well” reactive sickness care with “Stay Well” PHM services. Having convenient locations in popular retail superstores and pharmacies, with onsite free parking, as well as something else to do while waiting, when necessary, to see the practitioner, are at least as valuable features for PHM as for routine sickness care. And for patients who really need the coaching of a professional, in person, whom they know and trust, the retail clinic can generate perhaps a dozen or so PHM visits each year per patient, at modest fees, to supplement sickness care visits.
Moreover, in my experience, and as strongly suggested by research, NPs and Pas may be better at delivering PHM services than are physicians, trained as the latter are in the challenging diagnosis and treatment of illness. And they certainly will not need to charge as high fees as are needed to support physicians.
Retail PHM-including clinics can easily combine their PHM services with sickness visits, using these as added opportunities to ask patients how they are doing with their PHM goals, even checking their weight, blood pressure, cholesterol, blood sugar, and similar common biometrics as commonly involved in PHM efforts. Until physicians work out their own competing versions of a “medical home” that can combine reactive and proactive services while generating enough income to support such a model, retail clinics may be in the best position to do so for patients unwilling or unable to pay for the concierge medicine model.
In any case, the success of onsite medical clinics, also staffed by either physicians or NP/PAs, or a combination of the two, in meeting the PHM as well as sickness care needs of employee populations, is a clear indication of the potential. The first “customers” for this combination in currently sickness-focused retail clinics may well be the employees of the superstores where the clinics are located. After all, they are already onsite medical clinics for such employees, and if their employer wishes to contract for free or subsidized PHM services along with sickness care, that will generate a substantial patient volume, by itself.
While the fallacy of composition will still threaten or at least limit the extent to which retail clinics can expand before closures become even more frequent, the option of combining PHM with sickness care should offer another avenue to support as many and even more such clinics as sickness care alone would be able to. The combination may not suit all or even a majority of consumers or employers, but it represents a proven model in its existing forms, and should be at least worth considering where sickness care alone doesn’t provide sufficient success and survival.
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