Below are my notes from reading David Cutler’s The Quality Cure. Indented bits are paraphrases. Block quotes are direct quotes, obviously. Unintended bits are my commentary. Hyperlinks added to quotes are mine, based on references in the text. Asterisks indicate areas I intend to look into more fully in the future.
If you want my short take, skip to the final paragraph of this post.
Unnecessary care or overuse of certain therapies adds up to nearly $200 billion, according to Berwick and Hackbarth. Treatment of back pain is a classic example. Spine surgeries are often not necessary but are done in the U.S. at twice the rate as they are in other countries. There is a sixfold difference in back surgery across U.S. regions, only 1/10th of which is due to patient factors.
Induction of preterm birth is another example. Elective induction of birth should only be done for after 39 weeks of gestation, but 40% are earlier than this.
A third example is stenting.
The point is that care is appropriate in some cases but inappropriate in others, and the medical system does a poor job of separating appropriate use from inappropriate use. To use an analogy, health care waste is like fat layered into beef. One cannot remove it by simply cutting entire slabs. Rather, a delicate and deft knife is needed to separate the good from the bad.
It’s examples like these that cause the number needed to treat (NNT) to achieve one good outcome (relative to the counterfactual of no treatment) to be higher in practice than in clinical trails in which therapies are more targeted to appropriate populations. About elective induction of pre-term birth, see Aaron’s recent post.
There are protocols that medical personnel can follow to essentially eliminate [central line] infections. Peter Pronovost of Johns Hopkins University has pioneered the use of these protocols and has demonstrated that institutions can use them to essentially eliminate infections.
The fact that this is possible is consistent with the idea that health care organizations vary in productivity (quality), or that spending and health outcomes are meaningfully related to provider factors. If spending and outcomes were nearly entirely driven by patient factors, then protocols like this wouldn’t be of much use. One can make the same point with another example found later in the book: implementation of a Crew Resource Management (CRM) program in labor and delivery at the Beth Israel Deaconess Medical Center in Boston that dramatically reduced adverse obstetric events and malpractice claims (pages 132-134).
[T]he death rate from medical errors […] is the equivalent of a medium-sized jumbo jet crashing daily.
[O]bjections notwithstanding, I have added Canada and East Asia to “Europe.”
For humor value, this may be my favorite line of the entire book.
All high-income people in the United States earn more than high-income people in other countries, and thus the pay of physicians is not out of line relative to other highly educated people. […] Clearly one reason for high medical spending in the United States is that income distribution as a whole is more unequal in the United States, and health care uses many highly skilled workers. There is little the health care system can do about this—though overall income distribution is certainly responsive to policy.
I read this as the opportunity cost point that John Goodman is fond of making. But this does not imply that we cannot have better care that is also cheaper per (good) outcome. Even at current prices and wages, there is a lot of overuse, underuse, and misuse.
From the link, it appears as if the U.S. is actually in third place, behind Germany and Belgium, in stent insertion rate in 2009. By 2011, the U.S. rate had come down relative to OECD countries and was in 7th place. The U.K. inserts stents at nearly half the rate as the U.S., just like Canada. Mortality after a heart attack is considerably higher in the U.K. than the U.S., however. In Canada, it’s about the same. Not controlling for anything, it’s hard to draw any conclusions about the right rate of stenting. More international comparisons of quality for patients with chronic conditions here.
The canonical medical treatment in the direct-marketeer framework is Lasik surgery [for which most] people pay out of pocket. Over time, the price of Lasik has fallen, even as quality has improved. Wouldn’t that be true of all medical care if consumers were in charge? […]
Move away from Lasik, and the world suddenly becomes less clear. Dental care is not well covered by insurance, and the environment is leading to healthier teeth, yet the costs of dental care have increased nearly as rapidly as the costs of medical care. Even veterinary costs are increasing over time, at roughly the rate of human care, and very few people have insurance for their pets.
This actually makes the point that opportunity cost isn’t necessarily the only relevant consideration for price reductions and quality improvement. It happened in Lasik! But, Cutler’s point is that that fact does not imply that remaking all of health care to look like the Lasik market would work.
Put simply, no industry ever got better without knowing what it was doing.
This is in the context of low use of information technology in health care.
Cesarean sections are reimbursed more generously than vaginal births, however, so cutting back on [unnecessary] cesarean sections reduced revenues to Intermountain. Insurers saved money, but Intermountain did not. The same was true at Virginia Mason when they reduced the number of MRIs and orthopedic consults for lower back pain. […]
The net effect is that most clinical savings gained by operating hospitals more efficiently are not realized by the hospital undertaking the investments. As a result, hospitals tend to ignore efficiency.
This is in the context of why hospitals might not invest in IT (EMRs) on their own. Contrast with the recent findings of Dranove et al., however.
About a RAND study that concluded in 2012 that investment in health IT had been “disappointing” with only “mixed” impact on efficiency and quality, Cutler wrote,
As of 2012, when the assessment was made, health IT was still in the dissemination phase. The technology was nowhere near ubiquitous […] systems were not […] interoperable, […] appropriate workflow changes [had not yet been made]. It takes a while to learn how to use an entirely new system and reconfigure practices to benefit from it. I expect that seven years from now , the conclusion about health IT will be very different.
There is no perfect system lurking out of sight.
Pages 117-119, 126:*
The growth and success of bundled payments.
It’s not yet clear from Medicare demonstrations whether “lower-performing organizations can transform themselves into higher-performance ones as the payment model changes.” Savings from the physician group practice demonstration was meager, for example.
However, Blue Cross Blue Shield of Massachusetts’ Alternative Quality Contract has achieved more impressive performance. Reasons could include that private prices vary more than Medicare prices, so switching to lower cost providers is more feasible; AQC participants were less integrated, providing more opportunity for care management and coordination; The AQC rolled out during a time of intense focus on costs in Massachusetts.
States are also pushing new payment models.
Cutler explicitly mentions Arkansas, but Illinois, Oregon and other states also come to mind. Note also that alternative payment models promoted by one entity (like Blue Cross Blue Shield) can have spillover effects, altering the cost and quality of care for individuals covered by a different entity (like Medicare). On this point, see the work of McWilliams, Landon, and Chernew.
Payment reform may thus become the province of state policy.
One way or another, payment systems for medical care are likely to change markedly in the next few years. […] My guess is that payment will change substantially within the next five years [by 2019], and the fee-for-service system will be largely gone within a decade [by 2024].
Two things could go wrong: (1) Insurers could work at cross-purposes, each paying in different ways that conflict. (2) Larger hospital organizations are in the best position to organize around new payment models but those models also tend to make hospitals the least efficient setting for care delivery. Physician-led organizations may make more sense, but it is unclear how they will emerge.
The global payment model is fundamentally different from the HMO model. In an HMO, insurers dictate to doctors and patients what they are allowed to do and what they cannot. […]
In the global payment model, in contrast, physicians decide on good care and work with patients to provide that care.
Phase 1 [of health care reform] is covering people, and phase 2 is reforming the payment model to encourage better care. Phase 3 involves organizations changing their internal structures to deliver higher-quality, lower-cost care. […]
The good news is that the difference between good and bad performers is not a wholly different set of employees. […] Rather, the differences have to do with how the organization defines its mission, measures what it does, trains its employees, and motivates them.
An instrument developed by McKinsey asks organizations about performance monitoring, target setting, and incentives/people management. In high performing organizations, information relevant to performance is fed back to workers who are empowered to stop processes to fix problems; goals are established to focus attention on areas for improvement; people are hired and promoted on the basis of performance.
Bloom et al. used the McKinsey survey instrument to examine over 10,000 organizations internationally, mostly manufacturing firms, but also several hundred hospitals and some schools, including those in the U.S., U.K., Japan, Germany, and some developing countries.
In manufacturing, firms that score better are also more profitable and successful. Higher scoring schools do better on standardized tests. Higher scoring hospitals also have better survival rates for heart attacks. Across all three domains, U.S. hospitals have lower management scores than manufacturing firms, but higher than hospitals in other countries. More details here and here.
[T]he most important rule of health care management is this: never put care providers in a position of denying care for financial reasons.
In contrast to the U.K., Cutler does not advocate denying coverage for high-cost, low- (but not zero) value therapies (like Avastin for colorectal cancer, which can extend life by 1.4 months for $50,000) at this time. He recommends we focus on reducing pure waste (zero value care) first.
At some later point, we might need to consider whether Avastin is worth covering as a central benefit, but deferring this decision is reasonable.
The second rule of health care is:
[I]ndividual physicians should not be compensated based on the clinical outcomes of each patient.
Instead, Cutler recommends aggregating over physician groups and the population of patients they serve.
The third rule of health care is:
Patients have a lot to contribute to care improvement, and their voices should be heard.
About medical malpractice reform:
I fully admit that we do not have the answers yet.
About creating a more efficient health care system:
Unfortunately, there is little that society can do to force this change. What we can do is set the stage for it.
Focusing on quality has the potential to fix much of what ails American medicine. But how long will the quality cure take? […]
No one knows.
This is how health care will become more productive and Cutler’s guess about how long it will take:
Economists Stephen Oliner, Daneil Sichel, and Kevin Stiroh studied the sources of the increase in productivity growth over the past two decades. […] They found that higher IT use was associated with more rapid growth in productivity. Industries that used IT above the median level grew 1.5 to 2.0 percentage points more rapidly than industries that were low users of information technology. Here, then, is our first metric: as we move health care from an economic laggard to a leading industry, [productivity] growth might increase by 1.5 to 2.0 percentage points annually.
There is no citation for the Oliner/Sichel/Stiroh paper, but here’s one by them that seems relevant to the discussion. (I have not read it.)
Another way to gauge the potential in health care is to look at what needs to be done and estimate how rapidly it can occur. […] The easiest changes are in the site of care. This involves people who are being hospitalized in expensive institutions when they could be treated just as well in less expensive ones or even on an outpatient basis. […] The groundwork to affect [this] could be laid within one to two years [by 2016]. […]
Somewhat more difficult are changes that need to occur within institutions, to streamline the pathway of care for patients with various conditions […] rationalizing who receives stents and who does not, implementing care pathways for routine labor and delivery, [etc.]. […] My guess is that three to five years of work are required before major savings from these pathways can be realized [by 2021 if these follow after site-of-care changes].
The third tier of savings comes form populationwide prevention and patient engagement. […] Such experimentation will need at least five years to start bearing fruit and likely a decade before major savings can be realized [by 2031 if this follows prior changes]. […]
All told, therefore, improving health care quality is a fifteen- to twenty-year venture. If we are able to pull out 30 percent of costs in fifteen years, this implies a cost reduction [productivity increase] of 2 percent annually. If the transition takes 20 years, the implication is an average cost savings [productivity increase] of 1.5 percent annually. Here is the second piece of evidence: health care reform will increase productivity by perhaps 1.5 to 2 percent annually for fifteen to twenty years.
If this productivity growth were entirely achieved by (or translated to) reductions in spending at the same rate, this would probably bring overall health care spending in line with GDP growth. However, as Cutler points out, we see higher productivity associated with more overall spending in other industries. Michael Chernew made this point about IT: we spend a lot more on it than we used to even as each IT-related product or function becomes cheaper, controlling for quality. (Today’s cell phones do a lot more for their price than do those of a decade ago. But we may spend more overall on cell phones, in part for that reason.)
No one knows for sure how near-term changes will play out, let alone their longer-term effects. But in the end, there is certainly reason for optimism. If we do things right, the future of health care could be very bright indeed.
I thought one of the best aspects of the book was the expression of optimism and realism throughout—evidence-based and without overbearing cheer leading. Too many health policy books take a grumpy “it’s all terrible” tone. Too many also suggest solutions that are politically unrealistic. Cutler’s is decidedly different. He’s neither grumpy nor naive about what’s possible. I also liked that the book didn’t belabor any points. At 171 pages (of main text), given what it covers, it is laudably efficient. Few books are.
* More about these areas another time … maybe.