Outcomes-based contracts are supposed to be the next big thing. When payers are not sure about the real-world benefits or cost of a treatment, outcomes-based contracts and help them diversity risk. For instance, CMS will only pay for the CAR-T product Kymriah when “…these patients respond to Kymriah by the end of the first month after treatment.”
However, outcomes-based contracts are not easy to implement. A paper by Duhig et al. (2018) surveyed AMCP members between May 12, 2017, to June 7, 2017 to get their answer. Below is the list of items that are the top factors that are limiting factors for finalizing outcomes-based contracts with manufacturers, with the number in parentheses the share of respondents saying it is extremely or very limiting:
Inability to obtain accurate data/outcomes measures (73%)
Inability to discuss information that is outside the FDA-approved label 43%
Regulatory barriers that prevent disclosing information (40%)
Potential health care compliance risks (33%)
Inability/unwillingness of leadership to enter into an agreement with payer partners (30%)
Just an additional way to extract rebates from manufacturers (30%)
Privacy of patient data/HIPAA compliance (20%)
Leadership sees no value or necessity (13%)
OBCs in other countries were not optimal (10%)
What works? Payers say:
Simple/easily measurable outcomes (91%)
Risk sharing between manufacturer/payer customer (88%)
Flexibilty in the type of contract (80%)
Sufficient size of patient population (77%)
Reasonable time frame of contract (68%)
Manufacturer support with data mining/infrastructure (43%)
Pharma support of case management and adherence/compliance initiatives (40%)
Potential inclusion of a mediator to analyze risk before OBC discussions are initiated (30%)
At Healthcare Economist, we talk a lot about quality metrics. What they can and cannot do. One of our gurus of health care quality measurement, Avedis Donabedian, weighed in on his thoughts on how best they should be used:
“Systems awareness and systems design are important for health professionals, but they are not enough. They are enabling mechanisms only. It is the ethical dimensions of individuals that are essential to a system’s success. Ultimately, the secret of quality is love. You have to love your patient, you have to love your profession, you have to love your God. If you have love, you can then work backward to monitor and improve the system.”
In this version of the Health Wonk Review, I find the latest greatest from around the web related to health policy. Since more an more people consume their web through photos (see Instagram’s popularity), for each post, a carefully curated picture has been selected to further entice you to read through this great selection of articles. And so without further ado…
Patient access within provider networks is an important concept but difficult to visualize. How did Jay Norris of Colorado Health Insurance Insider make these maps measuring provider networks in Colorado? Read his article “2018 Essential Community Provider Maps” to find out.
One component of the American Patients First focuses on discounts, rebates and targets pharmacy benefit managers (PBMs). How big a deal are these rebates and discounts? According to Adam Fein from Drug Channels, brand-name drugs in 2017 reduced list price revenues by an astonishing $153 billion, largely due to these rebates and discounts. To find out more–and learn why Adam used this picture–go to his website.
In broad strokes, the American Patients First plan aims to lower list prices, lower patients’ out-of-pocket costs while maintaining incentives for innovation. In short, this is a sensible strategy, although there are some concerns about how more restrictive formulary designs could restrict consumer choice.
American Patients First in brief
To maintain manufacturers incentives to innovate, the plan insures that if manufacturers of brand-name drugs do lower their prices, they will keep a larger share of this price through a reduction in mandatory government discounts, such as the 340B program. Further, the government aims to incentivize other countries (outside the US) to increase their drug prices to incentivize innovation.
Consumers will pay less as well because any rebates received would be credited to their cost sharing, some/all Part B drugs would be moved to Part D, and there would be an out-of-pocket maximum. Further, the administration is considering $0 cost sharing for generic treatments for low-income seniors. Additionally, approvals for low-cost generic and biosimilar products would be expedited and Part C/D plans would be able to substitute generic onto their formularies mid-year.
Why would list prices fall? The American Patients First blueprint would allow Part D plans to negotiate similar to drug plans in the private sector. Further, the administration is considering limiting cost increases to inflation after brand names launch.
One of my greatest priorities is to reduce the price of prescription drugs. In many other countries, these drugs cost far less than what we pay in the United States.
Clearly drug prices/cost are important here. However, you will note a number of price/cost related concepts: price, cost, what we pay. Let’s take each in turn:
Price: I’ll define this as the list price for a drug
Cost: Health plans rarely pay the list price. Through negotiated rebates with manufacturers, government mandated discounts (e.g., the 340B program; Medicare best price), and other factors, health plan’s costs is much less than the list price manufacturers charge. Conversely, one could say that manufacturer’s do not keep the full price. One study from USC found that manufacturers keep only 41% of the list price, an amount similar to the amount intermediaries keep.
What we pay. This is patient out-of-pocket cost. This varies by insurance plan, type of drug (oral vs. injectable), but out-of-pocket costs include coinsurance, copayments and deductibles.
What should we be paying? In an ideal system, prices would be high to incentivize innovation, but patient out-of-pocket costs would be low to maximize use of high value treatment. A paper by Lakdawalla and Sood (2013) argues that this two-part price mechanism is actually accomplished though the health insurance system. Health insurance increases dynamic market efficiency. Since most of the cost to develop a drug is R&D and production costs are relatively low, economic theory says that price and marginal cost should be equal implying that patient out-of-pocket costs should be low. This is exactly what health insurance does (or is supposed to do).
Key components of the American Patients First Plan
A key argument in American Patients First is that manufacturers should keep a higher share of the list price. The President’s plan would consider eliminating the ACA’s “excise tax [on brand drugs], an increase in the Medicaid drug rebate amounts, and an extension of these higher rebates to commercially-run Medicaid Managed Care Organizations.” Additionally, the administration seems to want to limit the scope of 340B discounts which would result in more revenue in the hands of manufacturers.
The administration also wants to raise the price that other countries pay. This proposal echoes a recommendation from the Council of Economic Advisors. Life science firms R&D decisions depend on their expected global revenue. As prices in non-US OECD counties are much lower than the US, the US consumer is footing most of the bill for innovation. In short, non-US developed countries are getting a great deal at the expense of US consumers. The Trump plan would “assess the problem of foreign free-riding”, however it is not entirely clear how the US would incentivize/compel other countries to increase reimbursement for drugs.
On the downside, the administration would facilitate introduction of generics and biosimilars. These would include faster regulatory approval and fewer “loopholes” that branded product manufacturers could use to delay the introduction of generics. While this is good for current consumers, it would drive down life sciences revenue and could be an impediment to innovation.
Despite the threat from increased generic competition, on net this is a sensible approach.
Moving toward value-based payment
The administration is also considering novel approaches that link prices more closely to the value they prescribe. Some drugs are used to treat multiple diseases, but these drugs may be more effective for one of the diseases than another. The Trump plan would consider the use of indication-specific pricing whereby high-value treatments are paid more. Outcomes-based contracts would also be considered as well as value-based insurance design. In fact, there is already a value-based insurance design pilot study in Medicare that began in January 2017.
These approaches make sense as high-value treatments would receive higher reimbursement. This would incentivize life sciences firms to focus on developing high-value innovations
Lowering patient cost
This would occur through increased access to generics and biosimilars and an out-of-pocket maximum on Part D drugs. Generic manufacturers would have easier access to brand samples. It appears that the FDA would ease the path for biosimilar regulatory approval. A report from my former colleagues at Acumen explores the impact of moving some Part B drugs to Part D (and vice versa). The plan calls for lowering patient cost-sharing for 340B drugs.
Additionally, if rebates that lowered drug costs were applied to a patient’s coinsurance, this would lower out-of-pocket costs as well.
Sharing the actual list prices more readily would help consumers better know treatment prices. The administration would also update “…Medicare’s drug-pricing dashboard to make price increases and generic competition more transparent.”
Lowering list prices through increased competition (and government mandates)
The administration would allow drug plans more negotiating leverage with drug manufacturers. As stated in American Patients First, they are looking to reform Medicare Part D in order to “…give plan sponsors significantly more power when negotiating with manufacturers.” For instances, some Part B drugs could be moved over to Part D and Part D plans could negotiate prices. In addition, plans could negotiate when there is a single drug available. The proposed plan would “…require a minimum of one drug per category or class rather than two.” The additional negotiating leverage would drive down prices, but it also could severely restrict patient access.
Another provision in the President’s FY2019 budget would establish “an inflation limit for reimbursement of Medicare Part B drugs.” While this restriction would add predictability to drug prices, and would lower prices mechanically; manufacturers may respond by increasing the list price at launch. Thus, the inflation limit may result in higher drug prices at the start of a drug’s patent period but lower prices towards the end.
Although this study about 18 months old, I just came across this and the findings were really surprising.
During the office day, physicians spent 27.0% of their total time on direct clinical face time with patients and 49.2% of their time on EHR and desk work. While in the examination room with patients, physicians spent 52.9% of the time on direct clinical face time and 37.0% on EHR and desk work. The 21 physicians who completed after-hours diaries reported 1 to 2 hours of after-hours work each night, devoted mostly to EHR tasks.
On the one hand, decreasing physician interaction with patients is problematic as this is physicians comparative advantage. That the same time EHR work should not be seen as complete deadweight loss. Physicians take in information and make treatment decisions based on this information. Thus, spending time collecting, updating, and reviewing EHR data, can be productive use of time. However, it is surprising that physicians spend more time on EHR and desk work than patient interactions. This trend likely reduces physician job satisfaction and may decrease the quality of candidates who begin to pursue medical degrees in the future. The effect quality of patient care, however, is a complex question that is still being sorted out.
We health economists deal with medical cost data all the time. One challenge we all face is that the medical cost data is often censored. The censoring may occur because the patient dies. If you are using administrative health insurance claims data, censoring may occur because people switch their health plan and leave your sample.
The most common way of dealing with this problem is to drop all people for which you do not have complete data, and run the analysis only on the people for whom you have complete data. In some cases, researchers will conduct sensitivity analysis based on the continuous enrollment restriction they apply or stratify the results based on whether or not the patient died in the sample.
There is, however, another approach. Lin (2000) first describes a simple approach where people either are or are not censored. In this case, only people whose data is uncensored are included but the observations are weighted by an inverse probability weighting (IPW) based on the probability they will be censored in the data. Lin also proposes applying the same procedure for individual partitions. For instance, you could measure cumulative cost data through month 1, and do the IPW, then repeat the procedure through month 2 and use a different IPW weight and so on until you reach then end of your sample frame. If one is averaging monthly cost, one could average the different monthly cost partitions to get a more accurate estimate of the true average cost across the sample. Instead of just measuring average costs, one can also estimate regression parameters in each partition as well and sum these regression coefficients across partitions.
Griffiths et al. (2012) describes the procedure more eloquently in their application using SEER-Medicare data to examine how chemotherapy use affects costs among breast cancer patients. They describe their procedure as follows.
Patients were followed for up to 48 months (partitions) after diagnosis, and their actual total
cost was calculated in each partition. We then simulated patterns of administrative and dropout censoring and also added censoring to patients receiving chemotherapy to simulate comparing a newer to older intervention. For each censoring simulation, we performed 1000 IPW regression analyses (bootstrap, sampling with replacement), calculated the average value of each coefficient in each partition, and summed the coefficients for each regression parameter to obtain the cumulative values from 1 to 48 months.
Whereas Giffiths uses a linear regression, one can also apply generalized linear models (GLM) as well. Further, one can also use bootstrapping to create confidence intervals around the coefficients as follows:
Confidence intervals (CIs) for the cumulative cost coefficients were calculated by using a bootstrap approach, in which the process of performing 48 partitioned regression analyses and summing coefficients across partitions was repeated 1000 times using sampling with replacement from the original cohort.
In short, there are a number of creative ways for dealing with time-censored cost data.