The 1% Solution
Controlling healthcare spending has been a central policy objective for decades, but there is little consensus on how to proceed and many efforts are contradictory. Recently an article in The New York Times touted “the 1% solutions.” The premise, as stated by Fiona Scott Morton of the Yale School of management was: “I think focusing on the forest misses the fact that there are trees encroaching out of the forest and we need to cut them down.” The article goes on to focus on the value of long-term acute care hospitals, or LTAC’s, which costs about $5 billion annually more than if care had been provided in a skilled nursing facility, or SNF. The economists published a working paper proposing elimination of the LTAC benefit on the basis that it did not provide “better” outcomes. Naturally, the hospital and LTAC organizations disagreed. After examining both sides of the controversy, the article arrived at the following summation.
“The scholars involved in the project know they are not the first group to think small. The sort of deep and narrow investigations they are undertaking have long been the focus of groups like the Medicare Payment Advisory Committee…But the new effort by academics may expand the impact of suggestions. New data about not just government spending but also private insurance has enabled researchers to examine spending and inefficiency in the health care system more broadly than ever before. After all, the health care system is much bigger than just Medicare.”
The work group is looking at value-based care, which is often confused with cost-effective health care, but the two are not the same. The authors observe that value-based health care analyses usually adopt a societal (or payer) perspective in deciding what has value. Cost-effectiveness analysis, on the other-hand, typically compares the results of treatments from the patient’s perspective. To illustrate the difference, they cite the case of anti-viral drugs for treatment of hepatitis C, which are clearly cost-effective, but are unaffordable to many patients and health plans, which limits their utility in value-based analysis. The authors conclude both types of analysis, though, are concerned with the same thing: “bang for the buck.” They also suggest ways the analyses can be complementary.
One current effort to improve “value” is the use of public, on-line ratings of the care provided by hospitals and others. The notion is that the smart “consumer” will check the ratings of the available hospitals before deciding where to go for elective procedures. A recent article reviewed the state of such rankings and proposed an innovative, personalized solution to the challenges.
The authors begin by noting that all such rating schemes attempt to weight various factors and aggregate them into some sort of composite score.
“Responsible creators of overall performance ratings carefully consider the validity and reliability of individual measures. They ask questions such as, Is risk adjustment adequate? And is the signal-to-noise ratio reasonable…Where mathematics ends, however, there is an inescapable value judgment: In computing the overall hospital rating, how much weight should each measure or group of measures receive…No equation can help report makers decide how much relative weight to place on fundamental different dimensions of inherently desirable performance, such as technical quality, patient experience, and efficiency of care.”
These authors note patients place different emphasis on the various elements of a composite rating, so they created a program that took the CMS Hospital Compare star rating system and allowed users to place different weights on each of the factors involved. They then cite examples of how different weights impact the composite score in the star ranking system. Sometimes a “bad” hospital becomes a “good” hospital and vice-versa, depending on what the individual user considers important.
Stowell and Robicsek describe a similar phenomenon about “bad” doctors and “good” doctors when they examine costs and outcomes in a single large system. They note:
“Administrators want physicians to lower costs. Physicians want to optimize patient outcomes…Making the selective pressure productive requires a method of measuring variation in the cost and outcomes of care and tying that variation to discrete differences in clinical practice that can be changed. At Providence St. Joseph Health, the third-largest nonprofit health care system in America, with 24,000 physicians in seven states, we have developed a method for simultaneously measuring costs and outcomes and “drilling down” into the specific practices that drive variation.”
Their example was unilateral knee replacement, where they showed simple analysis plotting cost versus outcome per orthopedic surgeon led to inaccurate conclusions. When they looked at detailed costs, they found surgeons were high in some areas and low in others; very few were “good” or “bad” doctors. Looking at pharmacy costs, for instance, they found use of two drugs accounted for almost all the variation. This sort of analysis has led them to reject the good v. bad surgeon dichotomy in favor of a joint exploration of the costs of doing the procedure where everyone can learn what matters and what does not.
They propose several “rules” for success. First, partner with clinicians. Second, build cohorts that make sense to the clinicians. Third, stratify on risk, but be comfortable with imperfection. Fourth, make sure they “give a darn.” Fifth, develop a normalized patient-level view of costing. In other words, be able to get granular data about costs and attribute it the patient, not the cost center. They also normalize the data across the system, to account for differences in facility cost structures beyond the control of physicians, e. g., labor costs, depreciation, etc. As they note, they want to avoid confusing accounting differences with practice differences. They also have recommendations about ordering costs and presenting the data, which round out their approach. Their conclusion: when presented with good data, physicians make small changes in practice, which when added up over the system amount to real money. In other words, the 1% solution works when it combines the macro and the micro perspectives.
16 September 2018
 Sanger-Katz M. How to Tame Health Care Spending? Look for One-Percent Solutions. The New York Times, 27 August 2018. Accessed 30 August 2018 at https://www.nytimes.com/2018/08/27/upshot/rising-health-care-costs-economists-propose-small-solutions.html.
 Tsevat J, Moriates C. Value-Based Health Care Meets Cost-Effectiveness Analysis. Ann Intern Med 2018;169:829-332. doi:10.7326/M18-0342.
 Rumball-Smith J, Gurvey J, Friedberg MW. Personalized Hospital Ratings—Transparency for the Internet Age. N Engl J Med 2018;379(9):806-807. doi: 10.1056/NEJMp1805000.
 Stowell C, Robicsek A. Endless Forms Most Beautiful: Evolving Toward Higher-Value Care. 26 July 2018. Accessed 8 August 2018 at https://catalyst.nejm.org/evolving-high-value-care-oriented-architecture.html.
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