ACOs suffer astonishingly high turnover rates among their doctors and patients; their patients are unusually healthy; and those unusually healthy ACO patients constitute about 5 percent of each ACO doctor’s panel of patients. These facts appear in three recent reports: CMS’s final evaluation of the Pioneer ACO program, and two papers published in Health Affairs by John Hsu et al.
Each of these facts – high turnover, healthier patients, and few ACO patients in each physician’s panel – poses problems that cannot be solved without a substantial redefinition of the ACO. How are doctors supposed to influence the health and cost of patients they see only sporadically or not at all? How are ACO doctors supposed to lower costs if their sickest and most costly patients are not in the ACO? How are ACOs supposed to alter physician behavior when their physicians see fewer than 100 ACO patients out of a typical panel of 1,500 to 2,000 patients?
Harvard Professor Katherine Baicker is arguably most acclaimed health policy researcher at arguably the most acclaimed (and not even arguably, the best-endowed) school of public health in the country. Her seminal account of the effect of Medicaid coverage on utilization and health status is a classic. As luck would have it, in 2008 Oregon used a lottery to ration available Medicaid slots. A lottery controls for motivation and as such eliminates participant-non-participant bias, since everyone who enters the lottery wants to participate. That meant only one major variable was in play, which was enrollment in Medicaid or not.
Chance favors the well-prepared, and Professor Baicker jumped on this research windfall. She found that providing Medicaid–and thereby facilitating access to basic preventive medical care–for the previously uninsured did not improve physical health status, but did increase diagnoses and utilization. Because of the soundness of the methodology, the conclusion were unassailable – more access to medical care does not improve outcomes or optimize utilization, which is a proxy for spending. (We ourselves reached a similar conclusion based on a similar analysis on North Carolina Medicaid’s medical home model.)
Yet Professor Baicker herself used exactly the opposite methodology to reach the exact opposite conclusion for workplace wellness. And that’s where the identity crisis begins.
She and two colleagues published a meta-analysis in 2010 of participant-vs-non-participant workplace wellness programs. Somehow—despite her affinity for Oregon’s lottery control—she found this opposite methodology to be acceptable. She concluded that workplace wellness generated a very specific two significant-digit 3.27-to-1 ROI from health care claims reduction alone, with another 2.37-to-1 from absenteeism reduction. The title of the article–now celebrating its fifth anniversary as the only work by a well-credentialed author in a prestigious journal ever published in support of wellness ROI—was equally unambiguous: Workplace Wellness Can Generate Savings.
For those of us who actually think wellness outcomes should be evidence-based, a landmark study was released today: the first evidence provided by a major organization voluntarily (as opposed to being outed by us, like British Petroleum and Nebraska) that wellness doesn’t work. January’s Health Affairs features a case study of PepsiCo, authored by RAND Wellness Referee Soeren Mattke and others, in which a major wellness program was shown to fall far short of breaking even.
The specific highlights of the PepsiCo study are as follows:
- Disease management alone was highly impactful, with an ROI of almost 4-to-1;
- Wellness alone was a money sink, with each dollar invested returning only $0.48 in savings;
- The wellness savings were attributed to an alleged reduction in absenteeism, as self-reported by participants. There was no measurable reduction in health spending due to wellness.
Even though the wellness ROI was far underwater, we suspect that the ROI was nonetheless dramatically overstated, for several reasons. First, the authors acknowledge underestimating the likely costs of these programs, focusing only on the vendor fees without considering lost work time, program staff expense and false positives. Second, no matter how hard one tries to “match” participants with non-participants (the wellness industry’s most utilized measurement scheme), it simply isn’t possible to compare mindsets of the two groups. We learned from one of Health Fitness Corporation’s many missteps that participants always outperform non-participants, simply because they are more motivated. Third, the absenteeism reductions were self-reported, by participants.
Finally, PepsiCo’s human resources department, having made the mistake of accepting Mercer’s advice to implement one of these programs, was already taking some political risk by acknowledging failure. Had they incorporated the adverse morale impact, lost productivity due to workers fretting about false positives, Mercer fees and staff costs, participant bias, and self-reporting bias, the ROI could easily have turned negative (meaning the program would have been a loser even if the vendor had given it away) and the HR staff could have been taking serious career risk.
You’d be forgiven if, after reading last month’s Health Affairs, you came to the conclusion that all manner of wellness programs simply will not work; in it, a spate of articles documented myriad failures to make patients healthier, save money, or both.
Which is a shame, because – let’s face it – we need wellness programs to work and, in theory, they should. So I’d rather we figure out how to make wellness work. It seems that a combination of behavioral economics, technology, and networking theory provide a framework for creating, implementing, and sustaining programs to do just that.
Let’s define what we’re talking about. “Wellness program” is an umbrella term for a wide variety of initiatives – from paying for smoking cessation, to smartphone apps to track how much you walk or how well you comply with your plan of care, and everything in between. The term is almost too broad to be useful, but let’s go with it for now.
When we say “Wellness programs don’t work,” the word work does a lot of, well, work. If a wellness program makes people healthier but doesn’t save lives, is it “working”? What if it saves money but doesn’t make people healthier?
“We spend far more on health care than other peer countries yet have worse outcomes. Why is U.S. health care so expensive?” I’m sure you’ve encountered similar statements, maybe even expressed it yourself. It occurs often, including by knowledgeable people and health-related institutions. However, it’s a fallacy because it confuses health care with population health.
Health care is a proper subset of population health. For example, longevity is determined by more than just health care. Using a specific recent estimate (Appendix Exhibit A6 – gated), an average 20-year old U.S. white male who did not graduate high school will live 10.5 fewer years than a similar man with a college degree. That’s over ten years of life related to educational attainment. Sure, there are many reasons for the difference, and health care or the lack of it is only one of them.
The expansion of health insurance coverage may be the most visible aspect of health reform, but other elements will ultimately have a significant impact on how we all experience health care. One pivotal change is how health care organizations are paid. New payment approaches will reward providers based on whether services actually improve patients’ health and keep costs down versus simply incentivizing them to provide more care.
One of the more consequential changes will be a greater focus on helping patients to be more involved in their care. There is ample evidence that the behaviors people engage in and the health care choices they make have a very clear effect on both health and costs, positively and negatively. The most innovative health care delivery systems recognize this and see their patients as assets who can help them achieve the goals of better health at lower costs. From this point of view, “investing” in patients and helping them to be more effective partners in care makes good sense.
Our study, reported in the February issue of Health Affairs, highlights this role that patients play in determining health-related outcomes. We found that patients who were more knowledgeable, skilled and confident about managing their day-to-day health and health care (also known as “patient activation,” measured by the Patient Activation Measure) had health care costs that were 8 percent lower in the base year and 21 percent lower in the next year compared to patients who lacked this type of confidence and skill. These savings held true even after adjusting for patient differences, such as demographic factors and the severity of illnesses.
Even among patients with the same chronic illness, those who were more “activated” had lower overall health care costs than patients who were less so. Among asthma patients, the least activated patients had costs that were 21 percent higher than the most activated patients. With high blood pressure, the cost differential was 14 percent.
Two recent research papers remind us that it may be difficult to cut U.S. healthcare spending without harming quality. The first, written by a research team led by University of Chicago economist Tomas Philipson, appears in the latest issue of Health Affairs and has deservedly garnered a fair bit of media attention. The authors examine cancer spending and survival times for patients in the United States and ten European countries during the period 1983-1999 (later data were not available.) Their data confirm what we already know about health spending; the average cost of treating a cancer patient was about $15,000 higher in the United States. But the data also show that the typical U.S. cancer patient lives nearly two years longer; most of the difference is attributable to prostate and breast cancer patients. The gain appears to be due to greater longevity rather than early diagnosis. Using generally accepted measures of the value of a life, they conclude that the benefits of additional health spending outweigh the costs by a factor of 4:1 or higher. The latter calculation does not consider QALYs (quality adjusted life years) and so may be overstated. The authors acknowledge that other nations may do a better job of cancer prevention, so that their overall approach to cancer may be superior to that in the U.S., but they can find no evidence of this one way or another.
Philipson’s study suggests that U.S. healthcare consumers may get a substantial bang for their higher bucks. Maybe the U.S. system is not so inefficient after all. What about efficiency within the U.S. system? Some providers are far more expensive than others. Is the higher cost worth it? A new study by a team led by MIT economist Joseph Doyle, and released as an NBER Working Paper, suggests that you may get what you pay for within the United States. Doyle and his colleagues ask whether higher cost hospitals in the United States achieve better outcomes than lower cost hospitals. It is not easy to answer this question, because higher cost hospitals may admit more severely ill patients. This results in a statistical problem known as selection bias that is difficult to eliminate with available severity measures.
Imagine for a moment that you are an oncologist caring for a 53-year-old man with metastatic cancer, a person whose tumor has spread to lung and liver.
With standard chemotherapy, this man can expect to live around 12 months. That standard treatment isn’t all that expensive in today’s terms, only $25,000 and his insurance company will pick up the entire tab since he is already maxed out on his yearly deductible and co-pays.
But wait! Before prescribing the standard treatment, you find out there is a new chemotherapy on the market, one that costs $75,000 (in other words, fifty thousand dollars more than usual care) and has no more side effects than that standard treatment.
How much longer would patients like this have to live, on average, for you to feel that this new chemotherapy is warranted?
That’s not an easy question to answer. But it’s not an impossible one either. Clearly if the treatment would provide only, say, 1 day of additional survival on average, that would not amount to $50,000 well spent. Just as clearly, if this man could expect 10 years of additional life, no one would deny him this new treatment.
So when, between 1 day and 10 years, does it become a tough call whether to prescribe this new treatment?
I am getting caught up on the news after a couple of weeks away and two stories caught my attention. The first is the ongoing debate about the tax exempt status of Illinois nonprofit hospitals, which has received extensive coverage in the Chicago Tribune. Nonprofits avoid paying most state taxes, notably property taxes. For some nonprofit, the tax exemption could be worth tens of millions of dollars annually.
The question before the state is what they should expect of nonprofits in exchange for tax exemption. The current law requires nonprofits to provide “community benefits” commensurate with their tax savings. The state and the Illinois Hospital Association have been unable to find mutually acceptable language to replace this vague standard. The most draconian approach limits community benefits to charity care. At the other extreme, the IHA (and the Chicago Tribune) largely back a proposal by the Civic Federation that defines community benefits broadly to include losses incurred on Medicare, Medicare, bad debt, and community outreach programs.
A few years ago, I advised the state Attorney General’s office on this issue. I argued for the following conceptual approach: In exchange for tax exemption, nonprofit hospitals should be required to perform a commensurate level of “charitable acts,” which I defined as services and programs for which the hospital expects to lose money. Alternatively, charitable acts are those that investor-owned hospitals would not undertake.
Our recent Health Affairs article linking increased test ordering to electronic access to results has elicited heated responses, including a blog post by Farzad Mostashari, National Coordinator for Health IT. Some of the assertions in his blog post are mistaken. Some take us to task for claims we never made, or for studying only some of the myriad issues relevant to medical computing. And many reflect wishful thinking regarding health IT; an acceptance of deeply flawed evidence of its benefit, and skepticism about solid data that leads to unwelcome conclusions.
Dr. Mostashari’s critique of our paper, will, we hope, open a fruitful dialogue. We trust that in the interest of fairness he will direct readers to our response on his agency’s site.
Our study analyzed government survey data on a nationally representative sample of 28,741 patient visits to 1187 office-based physicians. We found that electronic access to computerized imaging results (either the report or the actual image) was associated with a 40% -70% increase in imaging tests, including sharp increases in expensive tests like MRIs and CT scans; the findings for blood tests were similar. Although the survey did not collect data on payments for the tests, it’s hard to imagine how a 40% to 70% increase in testing could fail to increase imaging costs.
Dr. Mostashari’s statement that “reducing test orders is not the way that health IT is meant to reduce costs” is surprising, and contradicts statements by his predecessor as National Coordinator that electronic access to a previous CT scan helped him to avoid ordering a duplicate and “saved a whole bunch of money.” A Rand study, widely cited by health IT advocates including President Obama, estimated that health IT would save $6.6 billion annually on outpatient imaging and lab testing. Another frequently quoted estimate of HIT-based savings projected annual cost reductions of $8.3 billion on imaging and $8.1 billion on lab testing.
We focused on electronic access to results because the common understanding of how health IT might decrease test ordering is that it would facilitate retrieval of previous results, avoiding duplicate tests. Indeed, it’s clear from the extensive press coverage that our study was seen as contravening this “conventional wisdom”.