Nobel Prize winner Bengt Holmström talks about the pros and cons of pay-for-performance. Interesting throughout.
Value-based insurance design looks to be expanding. As the American Journal of Managed Care reports:
The bill calls for a pilot demonstrating the feasibility of incorporating VBID by “reducing co-payments or cost shares for targeted populations of covered beneficiaries in the receipt of high-value medications and services and the use of high-value providers” no later than January 1, 2018.
The pilot will assess how implementing VBID concepts impacts adherence to medication, quality measures, health outcomes, and patient experience. TRICARE currently implements cost sharing in a “one-size-fits-all” way, similar to Medicare. Beginning January 1, 2017, Medicare Advantage is implementing its own VBID demonstration, which will run for 5 years.
The demonstration will only be available in 7 states in 2017 (Arizona, Iowa, Indiana, Massachusetts, Oregon, Pennsylvania, and Tennessee) with 3 additional states (Alabama, Michigan, and Texas) being added in 2018. In the first year, plans can offer varied benefit design for enrollees who fall into certain clinical categories: diabetes, congestive heart failure, chronic obstructive pulmonary disease, past stroke, hypertension, coronary artery disease, and mood disorders. In 2018, the demonstration will expand to include dementia and rheumatoid arthritis.
Tying copayments to care value seems like a good thing, as long as value is measured well. For instance, consider an extreme case where an individual is allergic to a high-value treatment. Would they be required to pay the higher-copay? There are a number of nuances to be ironed out, but tying copayments to value without restricting patient choice does seem like a win-win for patients and payers.
Russ Roberts has a great example demonstrating the benefits and perils of free trade with a health care example.
Suppose a scientist invents a pill that once you take it lets you live until 120 with no health issues whatsoever. Once you turn 120, you die a peaceful death on your birthday. Suppose the scientist, in a gesture of good will, charges $10 for the pill.
Should we let the scientist sell the pill? Is it good for the country? It’s good for almost everyone. But it’s going to be very hard on a very large group of people immediately:
Doctors. Nurses. Health Care administrators. People who build hospitals. People in medical school. People who teach in medical schools. People in health insurance companies. Pharmaceutical companies. Researchers. You get the idea. It’s millions of people. This is a very disruptive technology.
What’s going to happen to all those people? Mass unemployment. All of the skills of all of those people are no longer valued. The past investments made in those skills are now wasted. Incomes of those workers will inevitably plummet overnight.
True, they also get to live until 120. But their incomes are very low and may stay low for a while. They will have to find new things to do. What will they do? They face a very depressing future. Not much else they can do with the skills they have acquired…
But that’s not the whole story. We’re missing a huge part of the story.
The other important part of the story is that everyone is suddenly a lot wealthier. All the money we once poured into health care will now be able to be spent on other things. What are those other things?
We can’t know. No one can. But a whole bunch of areas are going to expand and some of those are going to soak up the time, talents and energy of former doctors, health care administrators and so on.
Roberts makes the convincing argument that trade and innovation are similar in that they both improve the quality of people’s life on average, but can have huge, devastating impacts for a subset of the population. He also makes the case that protectionism is a form of charity. Do read the whole article.
Rapid biomedical progress and rising healthcare costs have led to increasing calls to link spending to value rather than volume of care in the United States. These calls have come from payers, patients, providers, and even innovators. For example, Medicare aims to link 90% of payments to some form of value-based reimbursement. Providers-based organizations such as the American Medical Association have also endorsed using value-based pricing for pharmaceuticals. Joseph Jimenez of Novartis called on the pharmaceutical industry to “…shift to a model that focuses on value and outcomes delivered.” This leads to the question, “How do you quantify value, particularly when outcomes cannot be easily measured in purely economic terms?”
Definitions of value should reflect the reason why we have a healthcare system in the first place. It is to help people live healthier, happier, fuller lives. It is imperative to fulfill that mission as efficiently and affordably as possible, so as to maximize the efficacy of every dollar spent.
This excerpt comes from my article with co-author Mark Linthicum on Advancing Value in Healthcare”. We discuss the need need to use value in pricing and the new Innovation and Value Initiative (IVI), where both Mark and I are heavily involved. Read the full article at GEN (Genetic Engineering and Biotechnology News).
Medicare aims to tie 90% of reimbursement to quality measures. The potential for quality-linked reimbursement to incentivized improved quality of care, however, depends critically on whether physician quality can be measured reliably. Profiling individual physicians is difficult. Sample sizes are small and attributing patients to a single physician can be difficult (as Mehtrotra et al. 2010 notes), particularly with sick patients who visit multiple physicians every month or even every week. Further “Using data from commercial plans, several authors have found low reliabilities for measuring some aspects of physician quality (Hofer et al. 1999; Scholle et al. 2008; Sequist et al. 2011; Smith et al. 2013) and cost profiles (Hofer et al. 1999; Adams et al. 2010). The reliability of quality measures for hospitals also has been called into question (Thompson et al. 2016).
A recent paper by Adams and Paddock (2016) examine quality of care measures for primary care physicians serving Medicare fee-for-service patients in New York or Florida and who had ≥1 attributed quality measure. As quality measures are often measured as the share of patients who reach some benchmark and thus generally fall between 0 and 1, the authors wisely apply a beta-binomial model to estimate the reliability HEDIS quality metrics. [“Applying the standard HLM to reliability calculations in this context would require an assumption of asymptotic normality of physician-level score estimates, which might not be tenable for physician profiling when the number of opportunities for physicians to pass quality indicators is small.”] Under this approach, a physician’s quality is modeled using a binomial distribution assuming conditional on the true quality (i.e., probability an individual patient meets the quality metric) is known. The true probability is modelled using the beta distribution so that.
Reliabilty is calculated as:
where (σp2p)2 is variance in mean quality across providers and p(1-p)/n is the within-individual variance of the estimated quality p.
Using this relibaiblity measure the authors examine 3 scoring systems: (i) classifying an individual as above or below the mean, (ii) classifying provider networks based on whether they fall above or below the 75th percentile, and (iii) classifying provider who meet inclusion criteria based on whether they fall below the 25th percentile quality. The probability of miscalculation is calculated in each case for each individual provider and total misclassification probabilities are simply the sum of these probabilities across providers.
The authors find that:
In the three scoring systems, misclassification ranges were 8.6–25.7 percent, 6.4–22.8 percent, and 4.5–21.7% [across all measures considered]. True positive rate ranges were 72.9–97.0 percent, 83.4–100.0 percent, and 34.7–88.2 percent. True negative rate ranges were 68.5–91.6 percent, 10.5–92.4 percent, and 81.1–99.9 percent. Positive predictive value ranges were 70.5–91.6 percent, 77.0–97.3 percent, and 55.2–99.1 percent.
Of particular interest is that measures of reliability and misclassification are not the same.
median physician reliabilities of greater than 0.90 (e.g., glaucoma screening in NY, minimum denominator size of 100) can produce misclassification rates of more than 10 percent, while those greater than 0.70 can still result in misclassification rates of 20 percent or more.
This information should be helpful for determining the utility of quality metrics for both ranking physicians and tying physician reimbursement to quality metrics.
I have an article up at the Washington Post‘s In Theory blog titled How we should pay for cures, according to economics.
Imagine a major medical breakthrough: a cure for Alzheimer’s. Imagine that cure not only would improve the cognitive abilities of the more than 5 million Americans living with Alzheimer’s but also would give these patients additional years of life. Imagine the economic impact of allowing these individuals to live at home, return to work and become productive members of society.
But what should be the price of a cure? Ideally, it should be based on the value that the drug gives to patients.
The whole article is available here.
Tomas Philipson has an interesting post in Forbes describe how value should be incorporated into the U.S. healthcare system but should be done in way suited for the American market.
The American healthcare system is at a crossroads. Shifting to a system that reimburses based on value, rather than volume, of care requires changes in how we evaluate healthcare and make spending decisions. Elsewhere in the world, governmental agencies perform health technology assessment and make coverage recommendations based on cost effectiveness and other metrics. Such an approach runs contrary to fundamental elements of American healthcare, however. To successfully move to value-based healthcare in the U.S., distinctly American approaches to measuring value must be developed that meet several key criteria.
A JAMA Oncology paper estimating the global burden of cancer is getting a lot of attention in the press. The study’s key findings are:
In 2015, there were 17.5 million cancer cases worldwide and 8.7 million deaths. Between 2005 and 2015, cancer cases increased by 33%
A 33% increase in cancer cases!!! There must be an epidemic!!! Why are so many people getting cancer all of a sudden!!!
It’s time for everyone to calm down a bit. Let’s break down these numbers a bit more. Of the 33% increase in cancer cases over the last 10 years, 16% is due to population aging, 13% to population growth, and 4% due to age-specific rates. Let’s take these one by one.
Cancer is a disease that largely occurs in old age. One good way to avoid getting cancer is to die young. Countries with high infant mortality rates, war, contagious disease (HIV, Zika, etc.) are likely to have lower cancer rates. This is not because they are healthier, but because people die before reaching an age when they would get cancer. This concept is known as the the competing risks. Thus, observing that more people are dying of cancer could mean that people are living longer and are less likely to be dying from other diseases.
The second component of cancer growth is population growth. If cancer incidence as a proportion of the population were constant, clearly an increase in the number of people living on the planet would increase the number of cancer cases. Population growth in and of itself, however, is not a bad thing.
Now we get to the bad news: Age-specific mortality rates increased by 4%. That means, for a given person aged 50-54, or aged 55-59, the likelihood they would get cancer increased by 4%. However, this statistic blurs differences between countries. Age-specific mortality rates decreased by 0-10% in most of the Americas and Western Europe, decreased by 10-20% in Russia and Eastern Europe, and decreased by more than 20% in China. Many African countries, in contrast, saw increasing age-specific morality rates.
Further, if you look at the age-standardized absolute years of life loss metric (AS-YLL), for 24 of 31 cancers the AS-YLL decreased (i.e., there were fewer years of life lost) and for 9 of 31 cancers there was no statistically significant change in AS-YLL.
In this post, I do not mean to play down the severity of cancer. It is a serious disease and additional efforts are needed to improve the care and outcomes of cancer patients. What I do want to highlight, however, is that it pays to did a bit deeper into the statistics and not always rely on the headlines.