Physician Outliers and Their Role in Health Outcomes Variation

The shortness of breath was returning. She suspected her long standing asthma was responsible, but the inhalers were to no avail. See finally saw the doctor, expecting another short course of glucocorticosteroids. The doctor examined her and arranged for an echocardiogram. It showed a dilated cardiomyopathy, i.e., chronic heart failure.

In addition to chronic obstructive pulmonary disease, high blood pressure and having had a stroke, he had had a cardiac pacemaker placed. He felt best when his blood pressure remained high at 170/90. His regular doctor didn’t change a thing.

Even family wondered why the doctors could never really find anything to help her myriad complaints that included dizziness, chest pains and intestinal complaints. As the years went by, she eventually developed diabetes mellitus. Her doctor, a fan of disease management, told her to not answer the nurses when they phoned her.

As regular readers may have discerned, the Disease Management Care Blog is delighting in Malcom Gladewell’s bestselling book Outliers. In it, he shows that human accomplishment is not just a function of innate ability but timing, social support and practice. When it comes to developing world class expertise in intellectually difficult tasks, that level of necessary practice is prodigious: 10,000 hours. That translates to three to fours a day for approximately ten years.

What does this have to do with the care of populations with chronic illness? In yesterday’s post, the DMCB pointed out that supporting ‘patient centered medical care’ (where the patient is ultimately responsible for selecting a course of treatment) can result in variations in clinical outcomes that are arguably ‘good.’ In today’s post, it argues that high physician expertise can also lead to variation.

Patients and healthcare professionals are well aware of the physician who can glance at the x-ray and spot the tumor that no one else will see, the other physician who can spend minutes with a patient and immediately discern the diagnosis or the surgeon who can perform an extraordinarily difficult procedure and cure the problem. The DMCB thinks medical school, post-graduate training and the additional years in practice easily exceed 10,000 hours. For the right individuals under the right circumstances, that level of practice results in far more than ‘art’ or ‘science,’ but Gladwellesque expertise. That’s the kind of ‘outlier’ ability that innately knows a patient’s shortness of breath is not run-of-the-mill asthma, that persons with pacemaker syndrome may ‘need’ high blood pressure and that a somatoform disorder is one area where the less that is said about disease, the better. Because they are outliers, they find outliers. Outliers are statistical anomalies. It's not art, it's not science, it's exceptionalism. As physicians, they find exceptions. They lead to variation.

True, the national guidelines that tout treatment of asthma, heart failure, hypertension and diabetes always caution they are no substitute for a physician’s judgment. It is also true that most of the problem with docs' inability to adequately recognize or treat asthma, heart failure, hypertension and diabetes can result from less than 10,000 hours of training or failure to use their 10,000 hours to maximum effect. As a result, too many patients fail to receive a sufficient level of quality care.

However, for those world-class level experts who understand the guidelines, the DMCB doubts they’ll achieve 100% compliance with nationally recognized guideline-based standards. There’s no science or peer review literature on this, but more likely they’ll be achieving 80-90% compliance because somewhere between 1 to 20% [?] of patients have an outlier reason to not receive standard treatment. The DMCB asks: which would YOU rather have: a doc with 100% compliance or a doc with 90% compliance?

Given the importance of 10,000 hours of 'practice,' perhaps being called a ‘practitioner’ is not such a bad thing after all.

Disease Management: Are We Asking the Right Questions?

Suppose it was reported that a widely used condition-management program that relied on nurses didn’t work? That, according to an exhaustive review of the available literature, there is ‘scant evidence to support [its] effectiveness in terms of outcomes or economic cost?’ What’s more, that the studies that do exist ‘have been hindered by methodological challenges,’ including inappropriate sample size calculations and contamination of the referent control groups? Would you wonder why commercial insurers are willing to pay for it? Would you recommend against including it in the Medicare benefit?

Well, say goodbye to hospice, you cruel, malicious heel. Zimmerman and colleagues, reporting in the April 9 JAMA (subscription required to get to the full article) found that there were 22 randomized control clinical trials evaluating ‘palliative care,’ which was defined as a service that provides or coordinates comprehensive care for patients with terminal illness. Only 4 of 13 studies found that there was an improvement in quality of life, 1 of 14 found that there was symptom improvement and 1 of 7 found that there were cost savings. Yet, palliative care is a growth industry with approximately 4,500 standing programs, of which 46% are for profit, involving 1.3 million patients. It’s been covered by Medicare since 1982, along with the vast majority of other commercial and government health insurance programs.

The Disease Management Care Blog has already fussed over the “evidence-based” double standard being applied by many of the health care illuminati to ‘population-based care approaches’ to care versus yearly check-ups, the medical home and integrated delivery systems. While that cognitive dissonance can be a source of great fun, it’s time to be serious.

The DMCB thinks it’s difficult for the vast majority of hospice programs to accommodate randomized clinical trials (RCTs) let alone concoct one. It’s simply “out of reach” in terms of their business model or available resources. What’s more, the vast majority of hospice programs leaders, their workers, their patients, the families and the physicians wouldn’t want to bother with RCTs because the questions they answer wouldn’t add to their knowledge. Sound familiar?

How can we reconcile what we know and accept about hospice versus the lack of evidence? The DMCB commends an excellent essay authored by Don Berwick in a preceding issue of JAMA (subscription again). In it, he points out RCTs are better suited for the circumscribed evaluation of tests, drugs and procedures. They fall short, however, in evaluating complex multi-component interventions that depend on ‘context’ and the ‘mechanism.’ It’s not a matter of getting to the right answers, it’s a matter of getting to the right questions.

He suggests new evaluation methodologies are necessary when the question is not if something works, but why and under what circumstances. He also recommends that a) the high threshold used to decide whether change is warranted needs to shift if the status quo is unacceptable, b) sources of bias are better accommodated than eliminated and c) academician-front-line caregiver gap needs to be bridged.

Can you think of a big health care policy debate that would benefit from this approach? Hint here.

ACCORD & the Implications for Diabetes Disease Management

The Disease Management Blog doesn’t presume that its readers rely on this corner of the blogsphere for news and information, so you’ve probably already heard that the ACCORD (the catchy acronym stands for the Action to Control Cardiovascular Risk in Diabetes) was suspended. This my blog though, so that doesn’t mean I can’t weigh in with what I’ve learned and offer up some additional speculation through the lens of disease management. Read on if you are so inclined…….

Interesting stuff. Over 10,000 persons were randomly allocated to either tight blood glucose control (target A1c less than 6%: that is VERY aggressive) versus moderate control (an A1c between 7% and 8%, which isn’t bad but doesn’t meet guidelines of the American Diabetes Association or HEDIS). As an aside, there was an additional “2x2 factorial design” that also tests the benefits of 1) aggressive blood pressure control and 2) treatment to increase the “good” or HDL cholesterol. Research subjects began to be recruited in January 2001 in 77 outpatient clinics across the United States and Canada.

What was found? Over an average of 4 years, the researchers noted an increase in the death rate among the approximately 5000 subjects assigned to the A1c less than 6% group versus the 5000 in the other group. This was quite counterintuitive: 257 died in the tight control group, versus 203 in the group assigned to an A1c between 7% and 8%. This was statistically significant and apparently not a function of the types of diabetes drugs used. The portion of the trial on tight blood sugar control has been halted; the other research on blood pressure and HDL is continuing.

To put the calculated excess death rate of 3 per 1000 into perspective, the numbers suggest that a doctor (ensconced in a population-based program of course) would need to aggressively target 333 persons with diabetes down to an A1c of 6% or less to provoke one extra death (for more on number needed to treat go here). Deaths were evenly split between cardiovascular categories and “other” (for example, cancer). Persons assigned to the low A1c group had a lower rate of heart attacks, but the irony is that they were more likely to die if that happened. Participants are being notified of the trial result and the persons in the low A1c group are being reassigned to an A1c between 7% and 8%.

Note this is an “intention to treat” analysis. In other words, the data hasn’t been sorted by the actual A1c. A technically correct interpretation is that trying to get a person with diabetes to an A1c less than 6% is associated with excess mortality. That is slightly different than the conclusion getting a person to an A1c less than 6% is associated with excess mortality. Not everyone in the aggressive control group actually got to an A1c of less than 6%.

Why is this interesting? Older readers with a background in patient care may recall the debate about the “J curve” in essential hypertension back in the 1990s. The moniker “J curve” was used because the plot of BP control on the horizontal axis versus complications on the vertical axis looked like a “J.” Some studies had suggested that lowering the blood pressure “too much” (for example, less than 70 diastolic) among persons with hypertension seemed to be associated with increased mortality. Folks speculated that a lower “head of pressure” in arteries partially blocked with atherosclerosis led to premature clotting/thrombosis and death. The HOT (another catchy acronym – Hypertension Optimal Treatment) Trial found out that was not true and that aggressive lowering of blood pressure does no harm. What’s more, for persons with diabetes, HOT showed aggressive lowering of blood pressure is beneficial.

The preliminary review of the data from ACCORD makes me wonder if the previously obsolete concept of a J curve can be resurrected for diabetes mellitus. If we forego “intention to treat” for a moment and speculate persons in the ACCORD Trial who maintained an A1c between 6% and 7% did “better” than those with an A1c greater than 7%, the question is how did the persons in the less than 6% group do compared to those with an A1c between 6% and 7%? If they did worse, J curve! If they did the same, hockey stick.

Stay tuned. It will take some time for the health services researchers to pull all that apart and present all this in a peer review, transparent forum.

So what do we know?

This is another great example of why we need to uncouple short term process (the incidence of A1c testing) or clinical measures (the A1c results themselves) from the outcome measures that people really care about. And people care about death.

Patients who are engaged in their diabetes care now have even more information to better gauge how they should be treated. They can be counseled that targeting an A1c lower than 6% isn't necessarily in their best interest. However, they should factor in the intention to treat dimensions.

The mortality was observed in an “intention to treat” context. Just because a patient has an A1c less than 6% isn’t necessarily bad, it’s having that be the target that’s apparently bad. For example, a patient with an A1c between 7% and 8% who is being aggressively treated to achieve an A1c less than 6% is also in potential trouble.

The difference in mortality rates could have happened as a result of random variation and may have nothing to do with the A1c target. While possible, it’s unlikely because the researchers probably used the "chance of variation being 5% or less" (otherwise known as p<.05) threshold. In other words, the likelihood of random variation being responsible for this is less than 5% or 1 in 20.

It took four years for the difference in mortality to become apparent. Even if we forgo the intention to treat context, a brief dip of an A1c to less than 6% in an individual patient is not cause for alarm.

Note that the participants in this study are being re-targeted to an A1c between 7% and 8%, not the ADA recommended level of less than 7%. In looking over the protocol (see page 12) the researchers argue the 7%-8% treatment range what was obtained in previous studies on the benefits of diabetes control, particularly with the drug metformin. There are some interesting data out there that says a level between 7% and 8% may be good enough. This is despite what the ADA says and what the NCQA’s HEDIS optimum measure is. Once again, enrollees in disease management program have a basis to assess the ADA/HEDIS recommendations in the context of their own preferences and values. And deciding to let the A1c creep up over 7% may not be an egregious foolhardy sin.

Keep an eye out for the J curve.

Lipids, Vytorin, HEDIS and Disease Management

The Vytorin kerfuffle has receded to a slow burn, but that hasn’t stopped the disease management blog from mulling over what can be learned about the population-based dimensions of lipid management.

First some lipid orthodoxy, then the stuff of head cramps and then some discussion of a potentially under recognized ingredient in the Vytorin saga. If you’re already familiar with statins and lipids move 4 paragraphs down.

There is a direct link between blood cholesterol levels and the later development of atherosclerosis. Statins block the body’s ability to manufacture cholesterol. Multi-year studies involving statins have shown they lower cholesterol, reduce the progression of astherosclerosis, decrease the incidence of heart attack and save lives.

In general, there is a correlation between the amount of statin taken, the degree of blood cholesterol lowering and the subsequent risk of atherosclerosis-related diseases like heart attack. The more you take, the better the risk reduction for heart attack. But here is the rub: not everyone responds to statins the same way and some individuals taking statins still get heart attacks. According to one reference, a maximum dose of one statin will result in about only 70% of adults having the kind of low blood cholesterol level that in turn correlates the greatest reduction in heart attacks. The remaining 30% have a lower risk, but it’s not optimum.

Enter ezetimibe. This is a new agent that works differently than statins. It blocks the absorption of dietary cholesterol. While taking it leads to decreases in blood cholesterol, there are no studies (unlike statins) proving that the lower blood cholesterol from ezetimibe in turn leads to a lower frequency of heart attacks. However, based on what we know about blood cholesterol, it’s not unreasonable to believe that it could but there is no proof. Hence its potential role for the 30% of persons for whom statins are not enough.

The tension between populations and individuals is what gives the disease management blog a head cramp: if a population has a high frequency of cholesterol testing and statin usage, or (even better) if blood cholesterol levels are dropping thanks to statin use, you can confidently predict that the frequency of heart attacks will go down. Some individuals on statins, however, will still get heart attacks, just like some individuals on blood pressure medicines will still have high blood pressure. It's not unusual for physicians to struggle with reconciling that lower individual risk when making treatment decisions.

Why the head cramp you ask? There isn’t a managed care organization (MCO) seeking accreditation with the National Committee for Quality Assurance (NCQA) that isn’t doing its best to reduce the number of persons at risk for a heart attack using HEDIS measurements of cholesterol levels. While there are interventions that increase measurement rates and lower cholesterol levels at a “population” level, MCOs typically apply their maximum leverage at the individual patient-physician level, using what can euphemistically be called feedback and incentives. The result? Since physicians can easily recall that last statin-taking heart attack victim and 30% of their patients don’t get to target cholesterol levels anyway, the additional MCO leverage lowers the threshold for them to prescribe ezetimibe, even though the lower cholesterol has only a potential role in the reduction of heart attacks.

Enter the ENHANCE trial. Even though ezetimibe lowers blood cholesterol, this study used something completely different and more meaningful: thickening in the inside of the carotid arteries. This is a surrogate marker for the development of atherosclerosis, which is a arguably a stronger predictor for heart attack risk than a cholesterol level. Compared to statin alone, the addition of ezetimibe (combining ezetimibe with a statin in one pill is called “Vytorin”) - even though cholesterol levels were lower – had no impact on the progression of atherosclerosis. That means it’s less likely have any impact on heart attacks or saving lives.

The disease management blog speculates that at least some of Vytorin's growth was powered by physicians being leveraged by MCOs that were less interested in meaningful population-based improvements like heart attack reduction and more interested in their shorter term HEDIS rates. There is no proof of course, but if you ask a typical PCP, you might get a yes. Ask me and I will say yes. Ask NCQA and they will take credit for post-MI beta blocker prescribing: why not let their participants share some of the credit for Vytorin with Merck and Schering Plough?? Pending proof (which may still come) that Vytorin prevents heart attacks, a rigorous population-based approach would be to not give any credit for cholesterol lowering thanks to this drug. There are also implications for the physician feedback and incentives described above.

Lessons learned:

Even though statins reduce heart attack risk, it doesn’t have a 100% success rate. Compared to other interventions, however, it has a very good track record and lots of statin prescribing is good. Measurement of this at a population level is meaningful.

Considering the potential role of ezetimibe from a population perspective makes it a heluva lot easier to discern what it is known to do (lower cholesterol) and what it isn’t known to do (prevent heart attacks).

MCOs' running amok by leveraging NCQA HEDIS at the individual patient-physician level, where the benefit of statins are less readily discernible, may lead to short term measurement improvements (lower cholesterol). The potential price is the use of unproven treatments like Vytorin with no evidence from a population perspective that anyone is benefiting.

And here’s the punchline: ever wonder why disease management organizations are not more intimately involved in their customers’ HEDIS improvement activities? The disease management blog thinks that these companies – thanks to risk corridors that place a premium on hard money outcomes at a population level – understand the role of HEDIS. Good disease management leads to better clinical outcomes and better HEDIS rates. Improving HEDIS rates does not necessarily mean good disease management.

Something to think about.

A Clintonesque Examination of Quality in Disease Management

Everyone is aware that millions of Americans are being stymied by suboptimal healthcare quality, but the Disease Management Blog has been pondering just what “is” quality? Assuming it is expressed as a fraction of a group defined by a condition that achieves a desired outcome, the higher that fraction, the better. What’s more, with continued interventions, innovations and incentives we should be blessed with 100% quality. One example is NCQA, HEDIS and beta blockers.

But is that realistic in other sectors of chronic illness care?

Those of us who have worked in the trenches know the answer is sadly no. High blood pressure is a good example. As the heart squeezes down and pushes blood out into the arteries, the pressure goes up until the heart reaches its maximum degree of contraction. The maximum pressure in the arteries at that point in time is the “top number” (systolic pressure). As the heart relaxes or dilates (it needs to fill up with blood again), the pressure falls until the heart stops the relaxation and starts to squeeze again. That point of low pressure in the arteries is the bottom number (diastolic pressure). With the contraction and relaxation of the heart, the pressure in the arteries bounces between the systolic and diastolic, typically 120 millimeters of mercury on top and 80 mm of mercury on the bottom.

No one really knows what causes high blood pressure (usually defined as 140 or more systolic, or 90 or more diastolic) but whoever figures it out will likely deserve the Nobel Prize for Medicine, since that kind of discovery can lead to a cure. Absent that cure, we’re stuck with treatments consisting of weight loss, salt restriction, other lifestyle modifications and of course, drugs. Just how well do these treatments work?

To answer that question, the intrepid disease management blog blew tanks and dove into some of the peer review literature. Trials of hypertension therapy are a good window into the topic because in research settings, motivated volunteers agree to fully comply with treatment, have close follow-up by doctor and typically have a research assistant (such as a nurse) provide direction to the patient under protocol as part of a registry. It’s not too dissimilar from disease management or the chronic care model. That was also the approach used in the landmark HOT study.

In that study, patients with hypertension were randomly assigned to one of three targeted treatment protocols. While much has been made of the outcomes in the study, what is not widely appreciated is that only 85% of those assigned to a diastolic blood pressure less than 90 actually achieved it. In other words, even in high intensity research settings, the achievement of blood pressure control is not 100%.

How do I know this? Because I was one of the investigators in HOT and despite nuclear powered education and suitcase loads of pills, some of the participants in my clinic never got to target blood pressure.

And this phenomenon is not confined to high blood pressure. Outcomes less than “perfect” are typical in other clinical trial research reports involving chronic conditions like chronic heart failure, diabetes mellitus and high cholesterol. The HEDIS beta blocker success story will probably turn out to be the exception and not the rule.

Therefore, based on what the science is telling us, even with fully motivated patients who are carpet bombed with disease management (or the advanced medical home), ideal healthcare quality cannot be expressed as 100%. However, absent an adequate comparator, clinically reported clinical trials can yield up a “best of class” success rate of what can be accomplished under optimum conditions involving an optimum population. That puts a new perspective on press releases like this, where the results (compared to published trials) probably ain’t bad, but fail to tell us what is – and what isn’t – possible.

The Tale of Two Simpsons

The relationship between resources used and the benefit obtained has been seen in many pockets of the healthcare industry. In general, the more money you put in, the more you get until you reach a point of diminishing returns. In fact, there is some evidence that overdoing it will cause the curve (way over on the right) to dip down thanks, in part, to patient harm. For example, lots of CAT scans means big doses of ionizing radiation, lots of biotech means flakey off-label use and lots of plastic surgeons means funny looking celebs.

The Disease Management Blog wonders if much of the same phenomenon is true in the disease management part of the industry. Consider the case of the two Simpsons.

Jessica, may not deserve it but let’s face it: she does not have a reputation for high intelligence. However, she is an earnest and well meaning person (Dallas fans may disagree). If Jessica were tasked with some remote patient behavior change, I suspect that, despite her low healthcare skill set, she’d have some success even if all she did was call, leave a message and remind the “client” to regularly check the blood glucose or remind the physician about this study. Jessica is on the lower part of the curve.

On the other hand, Lisa is remarkably talented and insightful and she would undoubtedly be able to adjust her interactions with patients and deploy just the right input of insight, coaching and good cheer necessary to convince even the most unmotivated person to comply with the most complex of Care Plans. Lisa is higher up on the curve.

While the value of disease management is more than a function of telephony, it’s still fun to think about. There may be a role for Jessica, depending on the needs and health status of the population - as well as the negotiated price. Lisa is important also, but having too much of her can lead to diminishing returns. I also think Jessica or Lisa can overdo it and prompt some patients to seek additional high-cost, low-value or unnecessary care. I suspect finding the right balance of “Jessica” and “Lisa” is what distinguishes the high performing disease management companies from the others.

Double standard? You be the judge.....

Isn’t a shame that this personalized care engagement strategy doesn’t get more respect? Advocates have pointed out that participants benefit from six key components:

• identification processes that identify important clinical conditions
• application of evidence-based clinical guidelines
• the participation of other skilled healthcare providers
• high quality, personalized patient education
• ongoing data storage for use in outcomes evaluation and
• performance feedback

Thought I was talking about disease management? Not exactly.

The summary above is also about the annual physical examination, which is supposed to 1) uncover treatable conditions, 2) apply only the best science for prevention and treatment of those conditions, 3) initiate and coordinate appropriate care with other healthcare providers, 4) inform patients about their best care options, 5) establish a complete and retrievable record and 6) create a baseline against which future health can be measured.

If we fairly applied the tone of many reviews about disease management to the time-honored yearly check-up, it might read something like this:

Skeptics have raised doubts about the annual physical exam in the medical literature. There have also been reports from highly respected sources that have extensively reviewed the available evidence and concluded it has failed to demonstrate that it has any consistent value. While other studies indicate it may or may not actually increase quality, its cost amounts to billions and the absence of any good studies on a return on investment makes one wonder if the physical examination industry is intentionally misleading us. Market demand for the annual examination is high however, which has been forcing many commercial health insurers to ignore their actuaries’ advice and pay for it.

Physicians have their unbiased perspective on the topic. Good thing the U.S. Congress and CMS routinely, uniformly and fairly apply a scientifically rigorous process to controversies like this before covering it under the standard benefit.

Disease management’s pushing drugs!

I really like this study that was published in the latest edition of Health Affairs (full citation below).

Briefly, this was a quasi-experimental “differences in (pre-post) differences” study of the impact of a pharmacy benefit co-pay reduction versus no reduction in two self-insured employer groups, both of whom relied on the same ActiveHealth disease management organization (DMO). One employer reduced the co-pays across all tiers for some key drugs that have been indisputably shown to improve clinical outcomes in asthma, diabetes, heart disease and other conditions, while the other employer did not.

The result? Medication adherence (based on the statistically adjusted “Medication Possession Ratio” or MPR) increased approximately 2 to 4% when co-pays were reduced versus no change in the comparison employer. Except for the asthma inhaler, all the differences observed were statistically significant.

Why is this important?

While the effect size may seem modest, these changes in typical insurance settings are not only hard to achieve but could make a big difference between first and second place in a local market’s HEDIS® measures. Insurer Quality Improvement/Assurance VPs, Managers and Directors take note!

The authors are being modest when they describe this as a study of the impact of a pharmacy co-pay reduction while holding disease management (and other sources of bias) neutral. While technically correct and consistent with righteous health services research, it appears ActiveHealth notified its participants about the co-pay reduction. In other words, ActiveHealth’s remote telephony promoted the drug bargains among the patients that needed them, suggesting to me that the change in the MPR was the result of a pharmacy benefit design change combined with the DMO's services. That’s unique. True, there are other excellent studies (for example) that have shown patient coaching can increase medication use, but this was a mainstream commercial vendor program meeting the DMAA definition that was simultaneously caring for a total of 32 chronic conditions.

It’s pretty obvious that disease management and pharmacy should be coordinated, but silos predominate in this corner of health insurance “with pharmacy budgets that result in shifting of expenses from the pharmacy side to the medical side.” Seems to me that an industry with a reputation for not exceeding its customers’ expectations could use all the help it can get.

This is also a great example of the type of rigorous, transparent peer-reviewed research required of the disease management organizations if they are going to convince skeptics (ouch!) that they bring real value to the health care consumer. Quasi-experimental studies using an adequate comparator are well within their reach and they too can survive the rarefied, peer-reviewed academisphere - and in Health Affairs no less!

The authors also deserve credit for being honest about the study’s shortcomings, including the difference in the baseline MPRs between the two groups as well as other demographic differences, the possibility that other unmeasured factors accounted for the observed changes and that an increase in MPR doesn’t necessarily guarantee better clinical and financial outcomes. Kudos to ActiveHealth and may they be blessed with increased market share for better truth in advertising.

Some captains of the managed care industry may disagree with a “reduced co-pay for some patients” approach, arguing that an equal insurance premium should provide equal coverage. I think most State Departments of Insurance would agree. On the other hand, if all health insurance enrollees have a stake in not only pooling but mitigating risk, I don’t think cross-subsidization of the right drugs for the right people is all that bad, especially when generics are not budget busting in the zero sum game. Thank goodness for flexibility afforded by ERISA and the willingness of some employers to test some important concepts.

I got to be reminded about Steppenwolf’s classic tune, The Pusher. I’m showing my age!
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Citation: Chernew ME, Shah, MR, Wegh A, Rosenberg SN, Juster IA, Bosen AB, Sokol MC, Yu-Isenberg K, Fendrick AM Impact of decreasing copayment on medication adherence with a disease management environment. Health Affairs 2008;27;103-112

Quality Challenged Patient Choicery (QCPC), the NCQA and Mr. Cuomo

In 1997, the Annals of Internal Medicine published a seminal article on the Medical Home. Von Korff and colleagues at Health Care of Puget Sound, discerning usual one-on-one care was not up to the task of chronic illness care, argued for a different approach using 1) collaborative provider-patient agreement on the active medical problems, 2) mutually agreed-upon goal setting and planning, 3) provision of patient self-management training and 4) active and sustained follow-up. Note that Advanced Medical Home proposals by the American College of Physicians and the American Academy of Family Practice include “patient self management support” that ideally includes helping health care consumers decide what to treat and how to treat it.

What does this have to do with the National Committee on Quality Assurance (NCQA)? Quality assessment measurement approaches used by the NCQA and others for conditions like diabetes mellitus, high cholesterol, hypertension or need for vaccinations or cancer screening don’t give credit for "negative" patient preferences. So, if a fully informed patient chooses, despite documented state of the art engagement/education/empowerment, to eat fried chicken, use elevators, not take pills and avoid getting poked or naked, they are batched along with the all the other woeful shortcomings of U.S. healthcare.

Here are the commonly encountered retorts to the problem of quality-challenged patient choicery (QCPC):

“If patients refuse, the education was obviously substandard.” Yes, but...

there is a wealth of data showing patients vary greatly in their willingness make changes. Furthermore, it turns out patients require a higher level of risk before a treatment is accepted. That's not all that irrational given the tastiness of fried chicken, dangers of stairs, medication side effects, and the under-recognized burden of false positive screening tests. And then there is implacable patient irrationality.

“The physician is still responsible.” Yes but...

there is a big difference between being responsible and being in control. Ask any married man for details. I think the point is made.

“Patient refusals account for only a part of the low quality.” Yes, but...

the amount is unknown and it could be underestimated.

“It washes out.” Yes but…

this presupposes that the distribution of patient refusals is evenly and randomly distributed across populations. This may be true for health insurers with large numbers of patients. Unfortunately, that is not the case among the low numbers of patients across individual physicians, where random variation can result in some physicians having more QCPC, more responsibility and less control. If pay for performance dollars are at stake, that can also unfairly mean less money or being shut out of a network.

“It’s not our fault, the market is demanding 100% quality.” Yes but…

like the market can’t understand patient rights? Or health insurers don’t have a responsibility to help their customers understand what physician level data does and does not tell them?

By the way, while the nation's health insurers may tell us it’s just too hard to collect these data, don’t believe it. Not when chart reviews are used in other quality measures or when disease management organizations and/or clinic-based medical home team members can use their skill sets and information technology to deliver the education and document (and possibly even submit a claim that reflects?) patients'/enrollee preferences.

The New York Attorney General has established de facto national health insurer physician evaluation guidelines. Rumor has it that the NCQA may be an independent evaluator under the agreement and have a major role in determining how those guidelines are enforced. Let’s hope patient preferences are included as an evaluation co-variate.

Introduction, RAND and DMAA

Welcome to the Disease Management Care blog.

This is not only a chance to keep up with news and events in that growing segment of the health care industry called "disease management" aka care coordination aka population health, but a place for readers to share comments.

Speaking of which, the latest news of some note is another publication casting some doubt on the effectiveness of disease management. Drawing on a wellspring of 317 studies, Dr. Mattke and colleagues at RAND were only able to find evidence of reductions of hospitalizations in chronic heart failure and increases in outpatient care among persons with depression. While there was evidence that DM can improve processes of care, the long term impact on outcomes in other diseases (for example, diabetes mellitus) was unknown. Except for depression (ruh roh, costs increased), there was inconclusive or insufficient evidence of any impact. Not only did RAND post this on their web site, the news spilled over into the lay media.

Think DMAA will take that lying down? Not when their members have a $1.8 billion revenue stream at stake and the folks at Medicare are pondering just what they're going to do about this disease management thingy.

Among the DMAA responses: purchasers, unlike academics, are forced to make seasoned judgments without the benefit of conclusive evidence, that findings from peer review may not apply to their settings and what's more, other swaths of health care are also lacking in evidence. What do the very smart Medical Directors, Operations VPs, CEOs and CFOs in the commercial insurance industry know that the peer review literature doesn't?

If I may say so, waiting for conclusive proof before instituting a needed intervention also has its downsides (like unopposed gravity).

Let the games continue.