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Written By: Jason Shafrin - Apr• 28•16

Impact of Medicare Advantage on Hospital Admissions

Written By: Jason Shafrin - Apr• 27•16

Do patients who enroll in Medicare Advantage go to the hospital less frequently? The answer is yes. However, this fact may not be causal. Patients who enroll in Medicare Advantage are generally younger and healthier than patients who enroll in Medicare’s fee-for-service (FFS) program.

A paper by Duggan, Gruber and Vabson (2016) uses a novel approach to identify the causal effect. According to the NBER Digest, the authors:

examine the change in health care utilization by MA beneficiaries after they switch to traditional Medicare because their private insurer has exited the market. By focusing on cases where there are no other MA providers in the county, the authors ensure that the change in MA status is unrelated to the individual’s health or other characteristics.

Using this approach for hospitals in New York State, the authors find that…

MA enrollees who are forced to switch to traditional Medicare due to MA exit experience an increase of 0.11 hospital admissions per capita, which represents a 60 percent increase relative to the mean of 0.18 admissions. This increase in hospitalizations is accompanied by a 48 percent increase in total days spent in the hospital, a 33 percent increase in the number of procedures, and a 53 percent increase in hospital charges.

What other explanations are there for this result? Is this increase in hospitalizations a result of pent-up demand that was constrained by MA utilization restrictions? If so, there should have been only a short-term increase in hospital admissions, when in fact the increase persisted over time.

The authors do not find that MA enrollees face higher cost-sharing than traditional Medicare beneficiaries. This makes sense as MA plans that bid below the benchmark are able to reduce patient cost sharing.

The authors hypothesize that MA plans may restrict patients to hospitals that involve considerably longer travel or that MA plans more tightly restrict elective and non-urgent hospitalizations. However, quality of care and patient mortality are similar when patients are in MA or FFS plans.

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Books to read

Written By: Jason Shafrin - Apr• 27•16

Andrew Soloman has an outstanding article the Guardian discussing the intersection of literature and medicine.  He his article about literature on medicine saying saying:

Medicine can contribute to literature; narrative practice can strengthen medicine. It behoves writers and doctors to learn each other’s fluencies, because their disparate approaches can add up to singular truths.

Of particular interest, the article highlights some of the best books in this genre.  Soloman’s own book Far From the Tree, although not included in the article, is another book well worth reading.

How do consumers perceive low priced health care?

Written By: Jason Shafrin - Apr• 25•16

Is it a bargain?  Or do low prices represent low quality?  Or is cost independent of quality?  To try to answer this question Phillips, Schleifer, and Hagelskamp (2016) conducted a nationally representative survey to investigate whether consumers believe that price and quality are associated.  They found that:

Most Americans (58–71 percent, depending on question framing) did not think that price and quality are associated…

This is food for thought as many value-based health care programs aim to provide information on health care quality and cost to patients.

Will a robot conduct your next cancer screening?

Written By: Jason Shafrin - Apr• 24•16

Machine learning programs have made dramatic steps in recent years.  For instance, AlphaGo beat a world champion Go player recently.  Playing games is great, but can machine learning improve health care?  Science Daily reports that machine learning algorithms may help improve cancer screening accuracy.

Every state in the United States requires cancer cases to be reported to statewide cancer registries for disease tracking, identification of at-risk populations, and recognition of unusual trends or clusters. Typically, however, busy health care providers submit cancer reports to equally busy public health departments months into the course of a patient’s treatment rather than at the time of initial diagnosis…

The Regenstrief Institute and IU researchers have demonstrated that machine learning can greatly facilitate the process, by automatically and quickly extracting crucial meaning from plaintext, also known as free-text, pathology reports, and using them for decision-making…

“We think that its no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not,” said study senior author Shaun Grannis, M.D., M.S., interim director of the Regenstrief Center of Biomedical Informatics. “We have come to the point in time that technology can handle this. A human’s time is better spent helping other humans by providing them with better clinical care.”

The study was published in the Journal of Biomedical Informatics this month.  We may not be at the stage where you will be visiting robots instead of doctors at a physician’s office, but the progress is progressing.

 

Friday Links

Written By: Jason Shafrin - Apr• 21•16

Check out this week’s Health Wonk Review at Peggy Salvatore’s Health System Ed Blog.

If that’s not enough for you, here are some other links for you to read as you head towards the weekend.

Infectious Disease: Themes

Written By: Jason Shafrin - Apr• 20•16

An interesting post by Nicolas Bagley at the Incidental Economist provides a brief overview of a class he taught on infectious diseases and the law.  Looking at diseases ranging from cholera, Spanish flu, polio, AIDS, SARS, and Ebola, Bagley claims that ten key themes emerged:

  1. Governments are typically unprepared, disorganized, and resistant to taking steps necessary to contain infectious diseases, especially in their early phases.
  2. Local, state, federal, and global governing bodies are apt to point fingers at one another over who’s responsible for taking action. Clear lines of authority are lacking.
  3. Calibrating the right governmental response is devilishly hard. Do too much and you squander public trust (Swine flu), do too little and people die unnecessarily (AIDS).
  4. Public officials are reluctant to publicize infections for fear of devastating the economy.
  5. Doctors rarely have good treatment options. Nursing care is often what’s needed most. Medical professionals of all kinds work themselves to the bone in the face of extraordinary danger.
  6. In the absence of an effective treatment, the public will reach for unscientific remedies.
  7. No matter what the route of transmission or the effectiveness of quarantine, there’s a desire to physically separate infected people.
  8. Victims of the disease are often thought to deserve the affliction, especially when those victims are mainly from marginalized groups.
  9. We plan, to the extent we plan at all, for the last pandemic. We don’t do enough to plan for the next one.
  10. Historical memory is short. When diseases fall from the headlines, the public forgets and preparation falters.

The post also has a recommended reading list for people interested in the intersection of infectious disease, history, and public health. Go check it out.

Avoiding Lead poisoning

Written By: Jason Shafrin - Apr• 19•16

Lead poisoning, perhaps surprisingly, is still a major problem in the U.S. Lead poisoning in the water supply in Flint, Michigan is grabbing all the headlines, but other sources of lead poisoning are also problematic.
John Oliver has even dedicated an entire show to the problem of lead poisoning in the U.S.

How do you avoid lead poisoning for your children? First, check out the CDC’s lead poisoning website. Also, follow Harvard’s recommendations to parents for avoiding lead poisoning:

  • If your home was built before 1978, make sure you know if it has any lead paint. If you aren’t sure, get it inspected.
  • If you are going to have lead removed, or do renovations in an older house that may have lead paint under layers of other paint or wallpaper, make sure that the work is done by people who are certified in lead removal. For more information about this, check out the EPA’s web page.
  • Ask questions about the possibility of lead in your tap water. Lead can leach into the water from old pipes in your house, as well as pipes leading to your house. In Flint, the problem was that the city’s supply was changed to a river that had very corrosive water, and this water made lead leach into the water. (Sadly, even though they’ve changed the water supply, the damage done to the pipes is causing lead to still get into the water.) If you aren’t able to get good answers, or if you just aren’t sure, get your water tested. If you have well water, it should be tested when the well is first built and again if a pregnant woman or child younger than 18 moves in.
  • Be mindful of possible exposure from household objects, usually ones made in other countries. The Consumer Products Safety Commission has information about recalls, as well as about products that may contain lead.
  • Get your child tested for lead. Every child should be tested at least at ages 1 and 2, and again at 3 and 4 in areas with older housing stock. However, your doctor can do a simple blood test (preferably not a finger stick) to check at any time if there is a concern about a possible exposure. While no level of lead is normal or fine, a level of 5 or higher is considered dangerous.

Part B drug reimbursement

Written By: Jason Shafrin - Apr• 18•16

In the past Medicare has reimbursed physicians that administer Part B drugs–typically injectable medications administered in a physician’s office–at 6% of the drug’s cost.  The 6% aims to cover the cost of purchasing and storing the drug as well as administering it.  Because physician reimbursement is proportional to the cost of the drug, physicians have an incentive to select more expensive treatments.

A recent CMS proposed rule aims to change physician reimbursement for administering Part B drugs.  CMS states:

Medicare Part B generally pays physicians and hospital outpatient departments the average sales price of a drug, plus a 6 percent add-on. The proposed model would test whether changing the add-on payment to 2.5 percent plus a flat fee payment of $16.80 per drug per day changes prescribing incentives and leads to improved quality and value. The proposed change to the add-on payment is budget neutral.

Oncologists are not happy with the change. MedPageToday reports the Bruce Gould, MD, president of the Community Oncology Alliance wrote in a letter to CMS that:

The proposed payment model “is an inappropriate, dangerous, and perverse mandatory experiment on the cancer care of seniors who are covered by Medicare.”

The question is, what is the right number?  Physician costs to administer drugs have both a fixed and variable component.  The fixed component is likely the time and labor cost to administer a drug likely does not depend on drug price.  The cost of capital tied up in purchasing a drug and the risk that the drug is not used, however, are certainly proportional to price.  Having a fixed and variable component to pricing does make sense.  However, a $16.80 price to administer a drug seems very low.  The reasonableness of the 2.5%.

MedPAC analyzed the benefits and cost of moving entirely to a flat fee system for physician reimbursement of administering Part B drugs and found:

It might increase the likelihood that a provider would choose the least expensive drug in situations where differently priced therapeutic alternatives exist, potentially generating savings for Medicare and its beneficiaries. At the same time, a flat-fee add-on might create other incentives that could increase spending. For example, questions have been raised about whether increased payment rates for very inexpensive drugs might create incentives among some providers to overuse these drugs or spur manufacturers of low-priced drugs to raise their prices.

The “appropriate” reimbursement rate may even vary across drugs.  Further research is needed to better understand the implications of changing physician reimbursement for administering Part B drugs to ensure that both cost savings and patient access to high quality medications are achieved to the greatest degree possible.

Is P4P doomed to fail?

Written By: Jason Shafrin - Apr• 17•16

There have been many pay-for-performance (P4P) programs that have been implemented to attempt to improve quality and reduce cost. The vast majority of these programs have not been able to demonstrate large or even any improvement in quality or cost. Some researchers claim that these programs have not worked due to the size of the bonus, the specific metrics measured, a learning curve in P4P design and a host of other factors.

An interesting article in Health EconomicsSherry (2016)–claims that the the lack of success may be due to a fundamental flaw in P4P design.

…the intuition that P4P increases the output of a rewarded service is guaranteed only in the most simple (and unrealistic) cases of P4P plans where a single quality metric is rewarded. When more than one service is rewarded under P4P, the change in the level of each rewarded service is ambiguous due to multitasking between rewarded services. Multiple rewarded services are more likely to increase when unrewarded services earn lower marginal revenue. The change in unrewarded services is also generally ambiguous, even in settings of joint production. A given unrewarded service is more likely to decrease due to multitasking if other competing unrewarded services are more profitable; conversely, it is less likely to decrease if it is jointly produced with other rewarded services.

To give a simple example, consider the case where a patient should receive 3 types of preventive services but only service #1 and #2 are rewarded. In this case, it is unclear if they would increase the quantity of preventive services of #1 and #2. If service #1 and #2 are rewarded equally but service #2 takes less time to accomplish, physicians may increase provision of preventive service #2, but decrease provision of preventive service #1. Further, if physician time is limited, they may be less likely to provide preventive service #3 since since this services does not receive any reimbursement. On the other hand, if preventive service #2 and #3 typically occur together, then physicians could also perform more preventive service #3.

If this seems confusing, most P4P programs have tens or hundreds of measures and due to the joint production and multitasking interactions, the net effect on quality will be unclear.

Recommendations

Based on some comparative statics the authors conduct on a simple 3 service model, the authors make 3 recommendations for improving P4P programs.

  1. Rewarding fewer quality measures may be preferred.  “Rewarding a smaller set of quality measures limits the scope of P4P programs, but may stimulate greater performance improvement if multitasking between rewarded services is mitigated.”  Further, rewarding fewer services decreases the administrative complexity and cost of the program.
  2. Second, attention should be paid to unrewarded services as well.  The point has been made previously in the multitasking literature, most notably by Holmstrom and Milgrom 1991 (summary post here).
  3. The interactions between the production of different types of health services should be considered when designing a P4P program.  “When rewarding a single quality measure, selecting a measure that is jointly produced with other desirable health services and quality measures can contribute to broader quality improvements beyond the rewarded measure. Similarly, avoiding measures that induce higher amounts of multitasking can help avoid disruptions in care.”

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