New HIT ROI lit review is a methodologic tour de force, but misses the point

JAMIA logoRecently, Jesdeep Bassi and Francis Lau of the University of Victoria (British Columbia) published in the Journal of the American Medical Informatics Association (JAMIA) another in a series of review articles that have been written in recent years to summarize the literature regarding the economic outcomes of investments in health information technology (HIT).  Such articles answer the questions

  • “How much do various HIT technologies cost?”
  • “How much do they save?”
  • “Are they worth the investment?”

They reviewed 5,348 citations found through a mix of automated and manual search methods, and selected a set of 42 “high quality” studies to be summarized.  The studies were quite diverse, including a mix of types of systems evaluated, methods of evaluation, and measures included.  The studies included retrospective data analyses and some analyses based on simulation models.  The studies included 7 papers on primary care electronic health record (EHR) systems, 6 on computer-based physician order entry (CPOE) systems, 5 on medication management systems, 5 on immunization information systems, 4 on institutional information systems, 3 on disease management systems, 2 on clinical documentation systems, and 1 on health information exchange (HIE) networks.

Lau HIT ROI results

Key results:

  • Overall, 70% of the studies showed positive economic results, 24% were inconclusive, and 6% were negative.
  • Of 15 papers on primary care EHR, medication management, and disease management systems, 87% showed net savings.
  • CPOE, immunization, and documentation systems showed mixed results.
  • The single paper on HIE suggested net savings, but the authors expressed doubts about the optimistic assumptions made in that analysis about a national roll-out in only ten years.

My take:

Bassi and Lau have made an important contribution to the field by establishing and documenting a very good literature review methodology – including a useful list of economic measures, a nice taxonomy of types of HIT, and many other tools which they graciously shared online for free in a series of appendices that accompany the article.  They also made a contribution by doing some tedious work to sort through lots of papers and sorting and classifying the HIT economics literature.

But, I think they missed the point.

Like many others, Bassi and Lau have implicitly accepted the mental model that health information technology is, itself, a thing that produces outcomes.  They evaluate it the way one would evaluate a drug or a cancer treatment protocol or a disease management protocol.  Such a conceptualization of HIT as an “intervention” is, unfortunately, aligned with the way many healthcare leaders conceptualize their investment decision as “should I buy this software?”  I admit to contributing to this conceptualization over the years, having published the results of retrospective studies and prospective analytic models of the outcomes resulting from investments in various types of health information technologies.

Process PuckBut, I think it would be far better for health care leaders to first focus on improvement to care processes — little things like how they can consistently track orders to completion to assure none fall through the cracks, bigger things like care transitions protocols to coordinate inpatient and ambulatory care team members to reduce the likelihood that the patient will end up being re-hospitalized shortly after a hospital discharge, and really big things like an overall “care model” that covers processes, organizational structures, incentives and other design features of a clinically integrated network.  Once health care leaders have a particular care process innovation clearly in sight, then they can turn their attention to the health information technology capabilities required to enable and support the target state care process.  If the technology is conceptualized as an enabler of a care process, then the evaluation studies are more naturally conceptualized as evaluations of the outcomes of that process.  The technology investment is just a one of a number of types of investments needed to support the new care process.  The evaluation “camera” zooms out to include the bigger picture, not just the computers.

I know this is stating the obvious.  But, if it is so obvious, why does it seem so rare?

This inappropriate conceptualization of HIT as an intervention is not limited to our field’s approach to economic evaluation studies.  It is also baked into our approach to HIT funding and incentives, such as the “Meaningful Use” incentives for investments in EHR technology, and the incentives created by HIT-related “points” in accreditation evaluations and designations for patient-centered medical home (PCMH), accountable care organizations (ACOs), organized systems of care (OSC), etc.  The designers of such point systems seem conscious of this issue.  The term “meaningful use” was intended to emphasize the process being supported, rather than the technology itself.  But, that intention runs only about one millimeter deep.  As soon as the point system designers put any level of detail on the specifications, as demanded by folks being evaluated, the emphasis on technology becomes instantly clear to all involved.  As a result, the intended focus on enabling care process improvement with technology slides back down to a  requirement to buy and install software.  The people being evaluated and incentivized lament that they are being micromanaged and subject to big burdens.  But they nevertheless expend their energies to score the points by installing the software.

So, my plea to Bassi and Lau, and to future publishers of HIT evaluation studies, is to stop evaluating HIT.  Rather, evaluate care processes, and require that care process evaluations include details on the HIT capabilities (and associated one time and ongoing costs) that were required to support the care processes.

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Conceptualizing “over-treatment” waste: Don’t deny health economics

A Health Policy Brief published in Health Affairs on December 13, 2012 referenced an analysis published last April in JAMA regarding waste in health care.  In this analysis, Don Berwick (one of my health care heroes) and Andrew Hackbarth (from RAND) estimated that waste in health care consumed between $476 billion and $992 billion of the $2.6 trillion annual health care expenditures in the US.  That’s 18-37% waste.  They divided this estimate into 5 categories of waste.  Their mid-point estimates are as follows:

Berwick and Hackbarth estimates of waste in health care - JAMA 2011

They consider “failures in care delivery” to include failures to execute preventive services or safety best practices, resulting in avoidable adverse events that require expensive remediation.  By “failures of care coordination,” they mean care that is fragmented, such as poorly planned transitions of care, resulting in avoidable hospital readmissions.  They categorize as “overtreatment” care ordered by providers that ignored scientific evidence, were motivated to increase income or to avoid medical malpractice liability, or out of convenience or habit.  They considered “administrative complexity” to be spending resulting from “inefficient or flawed rules” of insurance companies, government agencies or accreditation organizations.  They estimated the magnitude of administrative complexity by comparing administrative expense in the US to that in Canada’s single payer system.  They considered “pricing failures” to be prices that are greater than those which are justified by cost of production plus a “reasonable profit,” presumably due to the absence of price transparency or market competition.  Finally, they considered “fraud and abuse” to be the cost of fake medical bills and the additional inspections and regulations to catch such wrongdoing.

Underestimating Over-treatment

These estimates are generally in alignment with other attempts to categorize and assess the magnitude of waste in health care.  But, I think Berwick and Hackbarth’s estimates of “overtreatment” are probably far too low.  That’s because they, like so many other health care leaders, are so reluctant to address the issue of cost-effectiveness.  Obviously, the definition of over-treatment depends on one’s philosophy for determining what treatments are necessary in the first place.  Everyone would agree that a service that does more harm than good for the patient is not necessary.  Most would agree that a service that a competent, informed patient does not want is not necessary.  Some argue that, if there is no evidence that a treatment is effective, it should not be considered necessary, while others argue that even unproven treatments should be considered necessary if the patients wants it.   Berwick and Hackbarth are ambiguous about their application of this last category.

But, the big disagreement occurs when evaluating treatments for which there is evidence that the treatment offers some benefit, but the magnitude of the benefit is small in relation to the cost of the treatment.  This is a question about cost-effectiveness.  It is at the heart of medical economics.  In my experience, most health care leaders and an even higher proportion of political leaders choose to deny the principles of medical economics and the concept of cost-effectiveness.  They describe attempts to apply those principles as “rationing” — a term which has taken on a sinister, greedy meaning, rather than connoting the sincere application of rational thought to the allocation of limited resources.   Berwick and Hackbarth implicitly take that view.  They are unwilling to define over-treatment based on cost-ineffectiveness.

The analysis I want to see

For years, I’ve been looking for an analysis that attempted to estimate the magnitude of waste from over-treatment based on the principles of health economics.  The diagram below illustrates the hypothetical results of the type of analysis I’d like to see.

Diagram re Conceptualizing Overtreatment

 In this diagram, the horizontal axis represents the total cost of health care to a population.  I don’t want to see the entire US health care system.  What is more relevant is the population served by an Accountable Care Organization or an HMO.  To create such a diagram, we would first need to break down health care cost into a large number of specific treatment scenarios.  Each of these scenarios would specify a particular treatment (or diagnostic test) with enough clinical context to permit an assessment of the likely health and economic outcomes.  For each scenario, each of the possible health outcomes would be assigned a probability, a duration, and a quality of life factor.  My multiplying the duration by the quality of life factor, we could calculate the “quality-adjusted life years” (or “QALY”) for the outcome.  Then, by taking the probability-weighted average of all the possible health states for the scenario, and then dividing the result by the cost, we could calculate the “cost-effectiveness ratio” for the scenario, measured in “$/QALY.”   Then, we would sort all the treatment scenarios by the cost-effectiveness ratios, with the treatment scenarios with the most favorable health economic characteristics on the left.

Some of the scenarios will generate net savings, such as for certain preventive services where the cost of the avoided disease is greater than the initial cost of the preventive service.  These are clearly cost-effective.  On the other end of the spectrum are scenarios that offer net harm to the patient, such as when adverse side-effects are worse than the benefits of the treatment.  These are clearly cost-ineffective.  In the middle of these extremes are scenarios where there is a positive net benefit to the patient and a positive net cost borne by the population.

If a person rejects the principles of health economics, they would consider all of these middle scenarios to be “necessary” or “appropriate” regardless of how small the benefits or how large the costs.  But, among those who accept the principles of health economics, some of these scenarios could be judged to be cost-effective and others to be cost-ineffective.  Such judgments would presumably reveal some threshold cost-effectiveness ratio that generally separated the scenarios into cost-effective and cost-ineffective.  Since different people have different values, their judgments could reveal different cost-effectiveness thresholds.  If we had many people making these judgments, we could find a range of cost-effectiveness ratios that were considered to be reasonable by 90% of the people.    Applying this range to all the treatment scenarios, one could find a group of scenarios that were considered wasteful by most, and another group of scenarios that were considered wasteful only by some.

Variations on this theme have been used throughout the world for decades by various practice guidelines developers, healthcare policy analysts, health services researchers and health economists.  It is complex and time-consuming.  As I’ve discussed before, it is also controversial in the United States.

Right now, in the US, we all recognize that health care costs are too high.  We’re all focusing on merging providers into larger organizations, installing computer software, and exploring new reimbursement arrangements to address the problem.  But, I’m convinced that over-treatment with cost-ineffective services is a major source of waste.  We will inevitably get back to the core issue of having to figure out which treatment scenarios are wasteful.  We will inevitably have to overcome our denial of health economics and our irrational fear of rational allocation.

 

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To resolve conflicts, re-frame polar positions as optimization between undesirable extremes. But, sometimes there is no way to win.

In politics and professional life, achieving success requires the ability to resolve conflicts.  I’ve noticed that conflicts often become entrenched because the opposing parties both simplify the conflict in black and white terms. They conceptualize their own interest as moving in one direction. And, they conceptualize their opponent as wanting to move in the opposite, wrong direction. As a result, the argument between the parties generates no light, only heat.  Each side only acknowledges the advantages of their direction and the disadvantages of the opposing direction. Neither side seeks to really understand and learn from the arguments offered by the other side.

When I’ve had the opportunity to mediate such conflicts, I almost always used the same strategies.

  • Step One.  I try to move both parties to some common ground, moving back to some basic statement that seemingly nobody could disagree with.  This generates a tiny bit of agreement momentum.
  • Step Two. Apply the momentum generated in step one to getting the parties to agree that, if you took each party’s position to an extreme, the result would be undesirable. The parties are inherently agreeing to re-conceptualize the disagreement from being a choice between polar opposite positions to an optimization problem. The idea is to choose an agreeable value on some spectrum between undesirable extremes.  If the parties make this leap, we are half way to sustainable agreement.
  • Step Three.  Get the parties to agree to avoid talking about the answer, and focus on reaching consensus on the factors and assumptions that influence the selection of the optimal answer.  Sometimes, this can be done subjectively, simply listing the factors.  Other times, it is worthwhile to get quantitative, working together on assumptions and calculations to estimate the magnitude and uncertainty of the outcomes at various points along the spectrum of alternative answers.  This quantitative approach has been described as the “explicit method,” and an example of applying it to resolve fierce disagreements about mammography guidelines is described in an earlier post.
  • Step Four.  Finally, ask the parties to apply their values to propose and explain an optimum answer, from their point of view.  In this step, the important point is to insist that the parties acknowledge that they are no longer arguing about facts or assumptions, since consensus has already been achieved on those.  If not, then go back to step three. The objective is to untangle and separate factual, logical, scientific debates from the discussion of differences in values.  If those remain tangled up, the parties inevitably resort to talking at each other, rather than engaging in productive dialog.
  • Step Five.  Try to achieve a compromise answer.  In my experience, if you’ve really completed steps 1-3, this ends up being fairly easy.
  • Step Six.  Work to sustain the compromise.  Celebrating the agreement, praising the participants for the difficult work of compromise, documenting the process and assumptions, and appealing to people to not disown their participation in the process are all part of this ongoing work.   Passive aggressiveness is the standard operating model in many settings, part of the culture of many organizations.  And, it is a very difficult habit to break.

Of course, in the routine work of mediating conflicts, I don’t really explicitly go through these six steps. This conflict resolution approach is in the back of my mind. They are really more like habits than steps.

Sometimes this approach works. Sometimes, it does not.  It can break at any step.

Notice that break downs in most of the steps are basically people issues. People won’t change their conceptualization. They are unwilling to make their assumptions explicit. They are unwilling to acknowledge differences in values. They are unwilling to compromise.

But, sometimes, the process breaks because of the nature of the issue being debated. Sometimes, conceptualizing the debate as an optimization problem between two undesirable extremes fails because there are really not good choices along the spectrum.

For example, when debating the design of a program or policy, I have often encountered a no-win trade-off between keeping it simple vs. addressing each party’s unique circumstances.  If I keep it too simple, people complain that it as a “hammer,” failing to deal with their circumstances.  If I add complexity to deal with all the circumstances, people complain that it is a maze or a contraption.  If I select some middle level of complexity, the complaints are even worse because the pain of complexity kicks in before the value of complexity is achieved.

I’ve seen this no-way-to-win scenario in my own work, in the design of information systems, wellness and care management protocols, practice guidelines and protocols, analytic models, organizational structures, governance processes, contractual terms, and provider incentive programs.  And, I’ve seen this scenario in many public policy debates, such as debates about tax policy, tariffs, banking regulations, immigration, education, and health care reform.  In cases when the extremes are more desirable than the middle ground, the only approach I can think of is to bundle multiple issues together so that one party wins some and the other party wins others, to facilitate compromise.

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Slides for Dr. Ward’s presentation at 3rd Annual Predictive Modeling Congress for Health Plans, Orlando, Florida, January 31, 2012

 

Click here for PDF copy of slides.

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The debate about what to maximize when selecting candidates for care management programs: Accuracy? ROI? Net Savings? Or Cost-Effectiveness?

When doing analysis, it is really important to clarify up front what it is you are actually trying to figure out.  This sounds so obvious.  But, I am always amazed at how often sophisticated, eager analysts can zip past this important first step.

Health plans, vendors and health care providers are all involved in the design and execution of wellness and care management programs.  Each program can be conceptualized as some intervention process applied to some target population.  A program is going to add more value if the target population is comprised of the people for whom the intervention process can have the greatest beneficial impact.

I have found it useful to conceptualize the process of selecting the final target population as having two parts.  The first part is the process of identification of the population for whom the intervention process is deemed relevant.  For example, a diabetes disease management program is only relevant to patients with diabetes.  An acute-care-to-ambulatory-care transitions program is only relevant to people in an acute care hospital. A smoking cessation program is only relevant to smokers.  The second part is to determine which members of the relevant population are to actually be targeted.  To do this, a program designer must figure out what characteristics of the relevant candidates are associated with having a higher opportunity to benefit from the intervention.  For example, in disease management programs, program designers often use scores from predictive models designed to predict expected cost or probability of disease-related hospitalization.  They are using cost or likelihood of hospitalization as a proxy for the opportunity of the disease management program to be beneficial.  Program designers figure that higher utilization or cost means that there is more to be saved.  This is illustrated in the graph below, where a predictive model is used to sort patients into percentile buckets.  The highest-opportunity 1% have have an expected annual admit rate of almost 4000 admits per 1000 members, while the lowest 1% have less than 200 admits per 1000 members.  A predictive model is doing a better job when more of the area under this curve is shifted to the left.

Although it is common to use expected cost or use as a proxy for opportunity, what a program designer would really like to know is how much good the intervention process is likely to do.  Other variables besides expected cost or use can contribute to a higher opportunity.  For example, in a disease management program, the program might be more worthwhile for a patient that is already motivated to change their self-management behaviors or one that had known gaps in care or treatment non-compliance issues that the intervention process is designed to address.

Applying micro-economics to care management targeting

Once the definition of “opportunity” is determined and operationally defined to create an “opportunity score” for each member of the relevant population, we can figure out which members of the relevant population to actually target for outreach for the program.  Conceptually, we would sort all the people in the relevant population by their opportunity score.  Then, we would start by doing outreach to the person at the top of the list and work our way down the list.  But, the question then becomes how far down the list do we go?  As we go down the list, we are investing program resources to outreach and intervention effort directed at patients for which the program is accomplishing less and less.   Economists call this “diminishing returns.”

As illustrated by the red line in the graph above, there is some fixed cost to operating the program, regardless of the target rate.  For example, there are data processing costs.  Then, if the program does outreach to a greater and greater portion of the relevant population, more and more people say “yes” and the costs for the intervention go up in a more or less linear manner.  As shown by the green line, the savings increase rapidly at first, when the program is targeting the candidates with the greatest opportunity.  But, as the threshold for targeting is shifted to the right, the additional candidates being targeted have lower opportunity, and the green line begins to flatten. The blue line shows the result of subtracting the costs from the savings to get net savings.  It shows that net savings increases for a while and then begins to decrease, as the cost of intervening with additional patients begins to outweigh the savings expected to accrue from those patients.  In this analysis, net savings is highest when 41% of the relevant population of diabetic patients is targeted for the diabetes disease management program.  The black dotted line shows the result of dividing savings by cost to get the return of investment, or ROI.   With very low target rates, too few patients are accumulating savings to overcome the fixed cost.  So the ROI is less than 1.  Then, the ROI hits a peak at a target rate of 18%, and declines thereafter.  This decline is expected, since we are starting with the highest opportunity patients and working down to lower opportunity patients.   Note that in this analysis, increasing the target penetration rate from 18% to 41% leads to a lower ROI, but the net savings increases by 24%.  So, if the goal is to reduce overall cost, that is achieved by maximizing net savings, not by maximizing ROI.

Should we try to maximize accuracy?

In a recent paper published in the journal Population Health Management by Shannon Murphy, Heather Castro and Martha Sylvia from Johns Hopkins HealthCare, the authors describe their sophisticated methodology for targeting for disease management programs using “condition-specific cut-points.”  A close examination of the method reveals that it is fundamentally designed to maximize the “accuracy” of the targeting process in terms of correctly identifying in advance the members of the relevant disease-specific population that will end up being among the 5% of members with the highest actual cost.   In this context, the word accuracy is a technical term used by epidemiologists.  It means the percentage of time that the predictive model, at the selected cut-point, correctly categorized patients.  In this application, the categorization is attempting to correctly sort patients into a group that would end up among the 5% with highest cost vs. a group that would not.  By selecting the cut point based accuracy, the Hopkins methodology is implicitly equating the value of the two types of inaccuracy: false positives, where the patient would be targeted but would not have been in the high cost group, and false negatives, where the patient would not be targeted but would have been in the high cost group. But, there is no reason to think that, in the selection of targets for care management interventions, false negatives and false positive would have the same value. The value of avoiding a false negative includes the health benefits and health care cost savings that would be expected by offering the intervention. The value of avoiding a false positive includes the program cost of the intervention.  There is no reason to think that these values are equivalent.  If it is more important to avoid a false positive, then a lower cut-point is optimal.  If it is more valuable to avoid a false negative, then a higher cut-point is optimal.  Furthermore, the 5% cost threshold used in the Hopkins methodology is completely arbitrary, selected without regard to the marginal costs or benefits of the intervention process at that threshold.  Therefore, I don’t advise adopting the methodology proposed by the Hopkins team.

What about cost-effectiveness?

The concept of maximizing ROI or net savings is based on the idea that the reason a health plan invests in these programs is to save money.  But, the whole purpose of a health plan is to cover expenses for necessary health care services for the beneficiaries.  A health plan does not determine whether to cover hip replacement surgery based on whether it will save money.  They cover hip replacements surgery based on whether it is considered a “standard practice,”  or whether there is adequate evidence proving that the surgery is efficacious.  Ideally, health care services are determined based on whether they are worthwhile — whether the entire collection of health and economic outcomes is deemed to be favorable to available alternatives.  In the case of hip replacement surgery, the health outcomes include pain reduction, physical function improvement, and various possible complications such as surgical mortality, stroke during recovery, etc.  Economic outcomes include the cost of the surgery, and the cost of dealing with complications, rehabilitation and follow-up, and the savings from avoiding whatever health care would have been required to deal with ongoing pain and disability.  When attempting to compare alternatives with diverse outcomes, it is helpful to reduce all health outcomes into a single summary measure, such as the Quality-Adjusted Life Year (QALY).  Then, the incremental net cost is divided by the incremental QALYs to calculate the cost-effectiveness ratio, which is analogous to the business concept of return on investment.  If the cost-effectiveness ratio is sufficiently high, the health service is deemed worth doing.  There is no reason why wellness and care management interventions should not be considered along with other health care services based on cost effectiveness criteria.

The idea that wellness and care management interventions should only be done if they save money is really just a consequence of the approach being primarily initiated by health plans in the last decade.  I suspect that as care management services shift from health plans to health care providers over the next few years, there will be increased pressure to use the same decision criteria as is used for other provider-delivered health care services.

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Reports of the death of Cost-Effectiveness Analysis in the U.S. may have been exaggerated: The ongoing case of Mammography

Guidelines for the use of mammograms to screen for breast cancer have been the topic of one of the fiercest and longest-running debates in medicine.  Back in the early 1990s, I participated in that debate as the leader of a guideline development team at the Henry Ford Health System.  We developed one of the earliest cost-effectiveness analytic models for breast cancer screening to be used as an integral part of the guideline development process.  I described that process and model in an earlier blog post.  Over the intervening 20 years, however, our nation has fallen behind the rest of the world in the use of cost-effectiveness analysis to drive clinical policy-making.  As described in another recent blog post, other advanced nations use sophisticated analysis to determine which treatments to use, while Americans’ sense of entitlement and duty have turned us against such analysis — describing it as “rationing by death panels.”  Cost-effectiveness analysis and health economics is dead.

But, maybe reports of its death have been exaggerated.

recent paper published on July 5, 2011 in the Annals of Internal Medicine described the results of an analysis of the cost-effectiveness of mammography in various types of women.  The study was conducted by John T. Schousboe, MD, PhD, Karla Kerlikowske, MD, MS, Andrew Loh, BA, and Steven R. Cummings, MD.  It was described in a recent article in the Los Angeles Times.  The authors used a computer model to estimate the lifetime costs and health outcomes associated with mammography.  They used a modeling technique called Markov Microsimulation, basically tracking a hypothetical population of women through time as they transition among various health states such as being well and cancer free, having undetected or detected cancer of various stages and, ultimately, death.

They ran the models for women with different sets of characteristics, including 4 age categories, 4 categories based on the density of the breast tissue (based on the so-called BI-RADS score), whether or not the women had a family history of breast cancer, and whether or not the women had a previous breast biopsy.  So, that’s 4 x 4 x 2 x 2 = 64 different types of women.  They ran the model for no-screening, annual screening, and screening at 2, 3 or 4 year intervals.  For each screening interval, they estimated each of a number of health outcomes, and summarized all the health outcomes in to single summary measure called the Quality-Adjusted Life Year (QALY).  They also calculated the lifetime health care costs from the perspective of a health plan.  Then, they compared the QALYs and costs for each screening interval, to the QALYs and costs associated with no screening to calculate the cost per QALY.  Finally, they compare the cost per QALY to arbitrary thresholds of $50K and $100K to determine whether screening at a particular interval for a particular type of women would be considered by most policy-makers to be clearly costs effective, reasonably cost-effective, or cost ineffective.

The authors took all those cost effectiveness numbers and tried to convert it to a simple guideline:

“Biennial mammography cost less than $100 000 per QALY gained for women aged 40 to 79 years with BI-RADS category 3 or 4 breast density or aged 50 to 69 years with category 2 density; women aged 60 to 79 years with category 1 density and either a family history of breast cancer or a previous breast biopsy; and all women aged 40 to 79 years with both a family history of breast cancer and a previous breast biopsy, regardless of breast density. Biennial mammography cost less than $50 000 per QALY gained for women aged 40 to 49 years with category 3 or 4 breast density and either a previous breast biopsy or a family history of breast cancer. Annual mammography was not cost-effective for any group, regardless of age or breast density.”

Not exactly something that rolls off the tongue.  But, with electronic patient registries and medical records systems that have rule-based decision-support, it should be feasible to implement such logic.  Doing so would represent a step forward in terms of tailoring mammography recommendations to specific characteristics that drive a woman’s breast cancer risk.  And, it would be a great example of how clinical trials and computer-based models work together, and a great example of how to balance the health outcomes experienced by individuals with the economic outcomes borne by the insured population.  It’s not evil.  It’s progress.

It will be interesting to see if breast cancer patient advocacy groups, mammographers and breast surgeons respond as negatively to the author’s proposal as they did to the last set of guidelines approved by the U.S. Preventive Services Task Force which called for a reduction in recommended breast cancer screening in some categories of women.

 

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Dr. Ward to speak at 5th Annual Predictive Modeling Summit, November 8-9, 2011, Washington, DC

The 5th Annual Predictive Modeling Summit is described by conference organizers as the “leading forum on predictive analytics applied to key health care functions, settings and populations.”

Dr. Ward will be giving a presentation entitled “Using Intervention Models and Predictive Models to Optimize Patient Selection for Care Management in ACOs” at 1pm on November 9, 2011.

Conference details:

Hope to see you there!

 

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Dr. Ward’s Slides from AMGA ACO Learning Collaborative, Chicago, July 14, 2011

Presentation entitled “Developing the Economic Model for Success” from day 2 of the American Medical Group Association’s Accountable Care Organization Learning Collaborative, at the Swissotel in Chicago.  Attended by approximately 60 leaders of AMGA member physician organizations and other interested parties.

Full page PDF format.

Hand-out PDF format.

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Why do other countries have different attitudes about Health Economics?

Wednesday morning, I attended a thought-provoking panel discussion entitled “Is Health Economics an Un-American Activity?” — a reference to the McCarthy-era Congressional committees that judged Hollywood movie directors and others considered to be communist sympathizers.  The panel presentation was part of the annual meeting of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) in Baltimore.  It featured John Bridges, PhD from Johns Hopkins, Peter Neumann, ScD from Tufts, and Jeff White, PharmD, from WellPoint.

The panel started by noting that the field of health economics has, as its fundamental premise, the rational allocation of scarce health care resources. This allocation is informed by “cost-effectiveness analysis” — broadly defined as the process of preparing estimates of both health and economic outcomes for different health care services to support decisions about which services are worth doing.  Health care services often produce a mixture of different health outcomes — sometimes extending life and sometimes affecting different aspects of the quality of life.  So, to deal with the mixed basket of different outcomes, cost-effectiveness analysts commonly combine all the health outcomes into a single summary measure called the “quality-adjusted life year,” or “QALY.”  Then, QALYs are compared to the costs of the health care service.  Based on this comparison, decision-makers determine if the service is worth doing, or “cost-effective.”

The panel noted that the United States is far less supportive of these basic concepts of the field of health economics, compared to almost all other developed nations.  In the U.S. stimulus bill, Congress provided substantial new funding to establish a Patient-Centered Outcomes Research Institute (PCORI).  But, Congress specifically forbade that Institute from using QALYs or establishing cost-effectiveness thresholds.  In the debates leading up to passage of the health care reform legislation, U.S. political leaders went out of their way to emphasize that they did not condone any type of “rationing” or “death panels.”  In contrast, the ISPOR meeting was full of presentations by health economists from Europe, Australia, Asia and elsewhere describing their government-sponsored programs to formally assess the cost-effectiveness of heath services and to use those assessments to determine whether to grant market access to drugs, biomedical devices and other health care products and services.

Although the panel discussion was enlightening and interesting, I felt they generally focused too much on QALYs and too little on the deeper cultural issues.  They made only vague comments on any evidence or theories about why there would be a such an obvious difference in attitudes between the US and other developed countries.  One presenter noted that America was founded by individuals fleeing tyranny, which led Americans to be distrustful of government hundreds of years later.  Another jokingly hypothesized that support for health economics had something to do with having a monarchy.

So why does the US see things differently?

It seems to me that there are two competing explanations for why Americans are so troubled by health economics and cost-effectiveness analysis: Entitlement and Duty.

According to the entitlement hypothesis, after a few generations of economic largess, Americans have come to feel entitled to a worry-free life.  As a result, Americans are supposedly unwilling to accept limits or burdens.  This is described as the decline of our culture.  It supposedly applies not only to health care, but also to our unwillingness to make tough decisions and sacrifices to solve the federal budget deficit, global warming, urban sprawl and even childhood obesity.  Both political parties implicitly support this view when they assert that their party will revive American exceptionalism and put the country back on the right track.  This sense of entitlement applies to both rich and poor.  Rich people hate rationing because they associate it with big government, which they equate with high taxes to pay for generous social welfare programs that transfer their wealth to the poor.  Poor people hate rationing because they fear that it will provide the pretext for the “establishment” to avoid providing them with the high quality health care to which they feel entitled.  According to the entitlement hypothesis, both rich and poor are like spoiled children, stomping their feet at the prospect of any limits to the health care they expect.

In contrast, the duty hypothesis makes seemingly opposite assumptions about the state of American culture.  It emphasizes that Americans have a strong sense of duty, and a romantic sense of chivalry, loyalty and patriotism. They note that Americans, compared to their European counterparts, tend to have more fundamentalist religious beliefs.  Americans tend to have a strong sense of right and wrong, seeing moral issues as black and white, rather than the more relativistic shades of grey prevalent in attitudes of those from other developed countries. Advocates of this hypothesis point out that Americans feel strongly about not leaving a soldier behind in battle, no matter what the risk. This sense of duty translates to an insistence that we spare no expense to rescue the sick from illness.

I can’t say I know which point of view is right.  Perhaps both forces are at work to animate Americans’ opposition to health economics.

What are the implications for ACOs?

ACOs involve providers taking responsibility for the quality and cost of care for a population.  Controlling cost requires reducing waste.  Many health care leaders would like to believe that we can control costs just by eliminating medical procedures that offer zero benefit or that actually harm patients, and by creating leaner care delivery processes so each necessary service is delivered at lower cost.  But, the elephant in the room is the far larger waste in the form of delivery of procedures that do offer some benefit — just not enough to be worth the high cost. Reducing the use of such procedures will face opposition and resistance. To be successful in the face of such resistance, ACOs must overcome the sense of entitlement.  ACOs must channel the strong sense of American duty, honor and righteousness to the act of triaging to help the people who need high value services.  The courts and churches use rituals and solemn settings to convey solemnity, seriousness and integrity.  Perhaps ACOs should use some form of ritual and a solemn setting to build a sense of rigor, transparency and integrity to the process of determining practice guidelines that direct resources to the “right” clinical needs. In this manner, the US culture of duty could potentially overcome any sense of entitlement, enabling the ACO to carry out its stewardship duties and responsibilities regarding quality and cost of care for the population.

I suspect that for-profit health care provider organizations will have a far more difficult time overcoming this resistance to health economics.  For people to internalize a sense of duty to triage, they must have confidence that when practice guidelines cause providers to say no to one patient regarding a low value service, the preserved resources go instead to provide a high value service to another patient.  If they suspect the savings is going into the pockets of stockholders, the cultural opposition to health economics will be strengthened.

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In the battle between Randomized Clinical Trialists and Observational Studies Advocates, don’t forget the Modelers

This week, I’m attending the annual meeting of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) in Baltimore.  Yesterday evening, I attended an interesting “smack-down” presentation between advocates for Randomized Clinical Trials (RCTs) vs. advocates for Observational Research.  The session was packed with hundreds of people.  In one corner was Michael Lauer, MD, the director of Cardiovascular Sciences at the National Institutes of Health (NIH).  In the other corner was Marc Berger, MD, the Senior Scientist of Ingenix’s Life Sciences Division.  Both gave examples when their research framework was right and the other got it wrong.  Both acknowledged that RCTs are really essential, and that observational studies will play some role.   It was an interesting session, but I was generally disappointed that neither side recognized the importance of the uninvited model builders.  I’ve been involved in trials, observational research and modeling over the years.  But, in the tongue-in-cheek debate about which methodologists should be kings of the hill, I’m with the modelers.  Let me explain.

How Trialists See the World

The religion of the medical profession includes some strongly-held core beliefs.  One of those beliefs is that the profession should be “evidence based.”  On one level, the premise of Evidence-Based Medicine (EBM) is indisputable.  We should be a profession that makes decisions based on facts, rather than emotions, myths, traditions, self-interest, or whatever else would take the place of facts.  But, the EBM belief system, particularly as preached by the clinical trialist clergy, goes beyond that to imply a decision-making framework that should define the way we determine which treatments to offer or not offer to patients.  The implied EBM decision-making process can be summarized in the following diagram.

When one reads the abstracts of scientific manuscripts describing RCTs published in all the best medical journals, this implied decision-making framework is clearly visible in the conclusions that are drawn and the logical basis supporting those conclusions.

It is important to note the implicit assumptions that underlie this traditional EBM decision-making framework:

  1. There are no important differences between the ideal conditions in a tightly controlled clinical trial vs. real-world use
  2. Health outcomes other than the one selected as the primary endpoint are not important
  3. 95% certainty correctly reflects decision-maker’s values regarding the trade-off between benefit and certainty
  4. Resources are unlimited, so costs need not be considered

Advocates of observational studies often start by questioning one or more of these assumptions.  They point out that the way surgical procedures are carried out or a patient’s adherence to prescribed medication can be different in community settings.  They point out that side effects or complications need to be considered when deciding whether a treatment should be offered.  They question the evidence threshold, particularly for potentially life-saving treatments for patients not expected to survive long enough for the definitive RCT to get published.  And, they point out that our health care system costs far more than other countries without a corresponding improvement in life expectancy or other measured health outcomes, and question whether high tech, new versions of drugs and biotechnology are worth the high cost.

But, how do modelers fit in?

Modelers are those that build computer-based models that use explicitly-stated, quantitative assumptions as the basis for mathematical calculations to estimate outcomes thought to be relevant to decision-making.  Models come in many forms, including decision-analytic models, Markov models, discrete event simulation (DES), and agent-based models (ABM).  The assumptions used as inputs to such models can be supported with data, based on expert opinion, or a mixture of both.

In my experience, both clinical trial and observational study enthusiasts sometimes put down computer-based models as being unrigorous.  They point out that models are based on assumptions, as if that was incriminating.  But, when you take a closer look at the relationships between models, RCTs and observational studies, you notice that modeling logically comes first.  And modeling also logically comes last.  These relationships are illustrated in the following diagram.

 

Clearly, an RCT is a waste of resources if it can be shown that it is implausible that the proposed treatment would add value.  It is also wrong to do an RCT if it can be shown that it is implausible that the treatment would not add value.  Dr. Lauer explained this point last night in an entertaining way with a reference to a sarcastic paper pointing out that parachutes have never been proven in an RCT to be effective in treating “gravitational challenge.”   But, how does one rigorously assess plausibility other than by offering a model with explicit assumptions that represent the boundaries of what knowledgeable people consider to be plausible?

When the clinical trialist is designing the RCT, they must decide on the sample size.  To do this, they do power calculations.  The basis of power calculations is an assumption of the effect size that would be considered “clinically significant.”  But, if you acknowledge that resources are not unlimited, “clinically significant” is a synonym for “worthwhile,” which is a synonym for “cost-effective.”  But how can one assess “worthwhile-ness” or “cost-effectiveness” in a rigorous way, in advance of an RCT, without constructing a model based on explicitly-stated assumptions?

Once the trial is done, the results must be incorporated into practice guidelines.  Again, if you accept that health outcomes other than the primary endpoint of the trial might be important or that resources are not unlimited, one needs to use a model to interpret the trail results to determine for which subsets of patients the treatment is worth doing.

Then, if observational studies are done to assess the outcomes of the treatment in real-world use, described as “effectiveness studies,” one needs to again use a model to interpret and determine the implications of the data obtained from such studies.

So, if we really want to be logical and rigorous, models must precede RCTs.  And models must also be done after RCTs and observational studies to properly and rigorously determine the implications of the data for practice guidelines.

For next year’s ISPOR annual meeting, I propose to include modelers in the methodology smack-down session.

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