- Wine and breast cancer.
- The promise of molecular imaging.
- The world’s most expensive drug?
- Who will be the binding constraint for drug access?
- 7 stages of robot replacement.
Unlike some other organizations, [the Innovation and Value Initiative (IVI)] is not interested in a top-down, bureaucratic process for setting prices or measuring value, says Shafrin. Rather, IVI aims to disseminate best practices for measuring value and reimbursing treatments based on this value. “We are looking at balancing innovation and value,” he says. “Some people are really focused on innovation. ‘We really need new technologies.’ But at same time, there has to be high value. So how do we get these two sides to talk to each other?
“There’s general agreement that we want new technology. But we need to be able to afford it. It needs to be of high value. How do we measure that, and how do we pay for it?
“We can help further that conversation.”
From my Executive Interview on “Measuring Value” in the December 2016 edition of the Journal of Healthcare Contracting.
An interesting story at AJMC on a new type of wellness program that better aligns outcomes with reimbursement:
“Most wellness programs are like most gym memberships,” Adam Fawer, Noom’s chief operating officer, said in an interview. Employers have spent lots of money on programs that fail to help or even reach most of the staff, he said. “They feel like they’ve been burned.”
The culprit is the per member per month (PMPM) payment model, which Fawer said initially appeals to companies because it’s predictable, despite a fundamental flaw: wellness providers make the most from employees who don’t participate, because they don’t generate any expenses.
“The incentives are completely misaligned,” Fawer explained. Under PMPM, “I would be excited about the client that I’m helping the least, because I’ll make money from that person. And I’ll be least excited about the client I’m helping the most.”
In early December, Noom unveiled a new payment model for employers: clients will only pay for staff who achieved “transformational” weight loss—at least 5% of their starting body weight—or who lowered their blood pressure or improved blood glucose levels, depending on their health condition.
More information on the company is on their website here.
Generally, this could be a good idea, but employers also need to worry about the wellness program cherry-picking individuals where results are easiest to achieve. An extreme case, would be measuring weight loss after pregnancy. As most women lose a significant amount of weight after pregnancy, Noom could target pregnant women to increase their reimbursement.
If pregnant women are included in the reimbursement, Noom will have an incentive to enroll these individuals in their program; if they are excluded, some pregnant women who could benefit from a weight loss program will not be able to get this support. This is a simple case, but other cases where the patient’s likelihood of success is not observable to the payer, but is observable to Noom could create more perverse incentives.
An alternative would be to allow employers and/or employees themselves identify the individuals who would receive treatment; this approach would be more likely to mitigate the “cherry-picking” problem.
How does health policy and pricing affect investment in innovation? This is the research question investigated in Amy Finkelstein’s 2004 QJE paper on Static and Dynamic Effects of Health Policy. She examines three policy changes:
The effect of these policies on vaccine research were categorized into four streams of research: (i) basic research (which may result in a patent), (ii) preclinical trials (testing in animals), (iii) clinical trials (testing in humans), and (iv) FDA approval. Finkelstein looks at whether the adoption of one of the three policies listed above affected the number of new clinical trials controlling for year and disease specific effects. She compares “control” diseases unlikely to be affected by the policy changes with “treatment’ diseases where the policy changes are more likely to have a large effect.
Finkelstein finds large effects on clinical trial investment, but less investment in basic research or patents.
These estimates imply that a $1 increase in annual expected market revenue for vaccines against a particular disease stimulates an additional 6 cents in annual present discounted value investment in that vaccine…For most of the affected diseases, I find that the induced innovation is entirely socially wasteful business stealing, although the magnitude of the dynamic social costs is small. In one case, however, I estimate that the “dynamic” welfare benefits from the induced innovation are not only positive, but also larger than the “static” welfare benefits from the policy’s effect on vaccination with the preexisting technology. These findings underscore the inadequacy of the near-exclusive focus in economic evaluations of health policy on the policy’s “static” effects.
…in the case of the Flu vaccine, I estimate that the “dynamic” social welfare benefits from the induced innovation are not only positive, but also larger than the “static” social welfare benefits of the Flu
policy from increasing utilization of the existing vaccines
Finkelstein does mention that for other diseases, the dynamic welfare implications are less because they represent “business stealing”. However, to the extent that additional R&D produces alternative treatments, this could drive down prices of patented products which would be beneficial to society. Further, if vaccine efficacy varies by person of patients have different preferences over vaccines–e.g., if one vaccine is administered orally and another administered through an injection–then this “business stealing” concept obscures the value of having more choice and more competition in the vaccine market.
Nevertheless, this is an interesting paper and makes the important point also highlighted in Acemoglu and Linn that market size and profit potential drive R&D investments.
When running an ordinary least squares (OLS) regression, one common metric to assess model fit is the R-squared (R2). The R2 metric can is calculated as follows.
The dependent variable is y, the predicted value from the OLS regression is ŷ, and the average value of y across all observations is ȳ. The index for observations is omitted for brevity.
One can interpret the R2 metric a variety of ways. UCLA’s Institute for Digital Research and Education explains as follows:
So then what is a pseudo R-squared? When running a logistic regression, many people would like a similar goodness of fit metric. An R-squared value does not exist, however, for logit regressions since these regressions rely on “maximum likelihood estimates arrived at through an iterative process. They are not calculated to minimize variance, so the OLS approach to goodness-of-fit does not apply.” However, there are a few variations of a pseudo R-squared which are analogs to the OLS R-squared. For instance:
There are a number of other Pseudo R-Squared approaches that are listed on the UCLA IDRE website.
There have been a number of studies that have examined how publicly reporting quality ratings (for health plans, physicians, hospitals or other health care providers) affects market share. Less attention has been paid to the effect of measured quality on health care prices. A paper by Huang and Hirth (2016) aim to answer just this question.
We use the rollout of the five-star rating of nursing homes to study how private-pay prices respond to quality rating. We find that star rating increases the price differential between top- and bottom-ranked facilities. On average, prices of top-ranked facilities increased by 4.8 to 6.0 percent more than the prices of bottom-ranked facilities. We find stronger price effects in markets that are less concentrated where consumers may have more choices of alternative nursing homes. Our results suggest that with simplified design and when markets are less concentrated, consumers are more responsive to quality reporting.
Setting higher prices at high quality facilities should be seen as a good thing as allowing high-quality providers to raise prices leads to move investment in quality.
Good news reported from the NY Times:
In a scientific triumph that will change the way the world fights a terrifying killer, an experimental Ebola vaccine tested on humans in the waning days of the West African epidemic has been shown to provide 100 percent protection against the lethal disease.
The vaccine has not yet been approved by any regulatory authority, but it is considered so effective that an emergency stockpile of 300,000 doses has already been created for use should an outbreak flare up again.
The results of the study are available in Henao-Restrepo et al. (2016). For many, this finding may signify an early start to the holiday season.