Asymmetric Information

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Value-Based Cost Sharing

February 4, 2009 in Asymmetric Information, Health Insurance, Optimal Ins (Theory) | No comments

Many policy experts have been proponents of value-based cost sharing.  Under value-based cost sharing, medical care that is seen to provide a higher marginal benefit to the patient will have lower coinsurance rates than medical care with lower marginal benefits.  If value-based cost sharing would be implemented, preventive care should have low coinsurance rates because of the subsequent health benefit.

This seems like a very attractive way to design health insurance benefits, but a paper by Pauly and Blavin (JHE 2008) asks if value-based cost sharing is truly a novel concept.

First, under perfect information, there is no reason to have value-based cost sharing.  In this case, patients should internalize the marginal benefits–now and in the future.  ”When all agents have perfect knowledge of patient illness states and benefits of care, optimal coinsurance should be zero; insurance should take the form of a fixed dollar (indemnity) payment to cover the full cost of care when care is cost-effective, and should pay nothing in circumstances in which care has benefits that fall short of cost.”

Under asymmetric information, however, patients may make naive decisions.  For instance, patients may decide to forgo preventive care because the do not realize its long-term health benefits.  Coinsurance rates must still be designed to balance the twin goals of risk sharing and averting moral hazard.  Lower coninsurance rates will incentivize patients to get needed care.  However, designing coinsurance rates solely based on the marginal benefit of the procedure may not be optimal once we take into account that patient moral hazard may increase medical care above optimal levels.  

Pauly and Blavin also note that under asymmetric information, “it may now be the more price responsive service that should get the cost reduction, since lowering its cost sharing will have a larger effect in terms of moving it closer to the ideal level than would be the case for a less responsively demanded service.  That is, if patient ignorance resulting in underestimation of benefits from care in a given setting is severe enough, the direct relationship between price responsiveness and optimal cost sharing should be reversed.”

Yet just because value-based cost sharing can work does not mean that is the only solution.  Instead of spending money to reduce coinsurance rates, insurance companies or policymakers could spend money to inform consumers of the true benefits and costs of different types of medical care.  This is especially true of medical services that are not price responsive; where the only way to convince patients to undergo these potentially uncomfortable procedures is to increase information dissemination.  For instance, most people would prefer not to undergo a colonoscopy even at a price of 0.  The only way to convince patients that the procedure is needed is by information dissemination.

Value-based cost sharing is not a magic bullet, but may be a useful technique in the presence of asymmetric information.

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Learning and Drug Diffusion

February 2, 2009 in Asymmetric Information, Pharmaceuticals, Supply of Medical Services | 1 comment

How do doctors know which drugs to give to which patients?  Of course there are clinical trials giving the relative efficacy of each drug.  With less than perfect adherence, however, clinical trials may not accurately predict a drug’s efficacy or the potential side effects.

A paper by  Chintadunta, Jiang and Jin (2008) look at two types of physician learning about pharmaceuticals: learning across patients and learning within patients.  Across patient learning occurs when the physician learns about the average quality of a drug.  Physicians figure out which drugs are best suited for individual patients through within-patient learning.

The authors look at physician learning in the setting of Cox-2 Inhibitors:

“Between 1998 and 2001, the FDA approved three Cyclooxygenase-2 (Cox-2) Inhibitors: Celebrex (Dec. 1998), Vioxx (May. 1999), and Bextra (Nov. 2001). All of them were heavily advertised as safer alternatives to then existing pain killers. By September 2004, the class had more than 10 million patients, annual sales had reached $6 billion in 2003, and total advertising dollars spent in 2003 were as high as $400 million. After a clinical trial associated Vioxx with severe cardiovascular (CV) risks, Merck withdrew the blockbuster drug in September 2004. CV risks and enhanced concerns on skin irritation led to the withdrawal of Bextra in April 2005. As of today, Celebrex is the only Cox-2 Inhibitor remaining on the market, with warnings added in April 2005. ”

Data and Methods

The authors use data from four sources to identify physician learning: 

  1. patient-level prescription and satisfaction data from the IPSOS patient diary database (IPSOS-PD), 
  2. monthly advertising expenditures obtained from the New Product Spectra (NPS) database, 
  3. the number of news articles covering Cox-2s derived from Lexis-Nexis for the period 1999 to 2005, and
  4. the number of academic articles covering Cox-2s from Medline from 1999 to 2005. 

Patient level satisfaction is importance because the more satisfied a patient is with the prescription, the less likely the physician will decide to switch brands.  Physician learns which brands are well-suited to which type of patients.  Advertising, media coverage and academic articles all play a roll in across-patient learning.  

The authors use a Bayesian model to identify learning.  The authors assume each physician has a prior about the relative efficacy and safety of each of the Cox-2 Inhibitors.  Patient satisfaction, advertising, media coverage, and academic articles all will update the posterior probabilities.  

The utility of each of patient, p, from using each drug, j, at time, t, is:

  • Upjt = β0j + βsE(satis)jt + βxjXpt + βzZjt +εpjt

The patient’s utility depends on a drug specific constant, the patient’s average satisfaction rating up to that date, patient specific factors, X, and drug specific information, Z.

Results

The authors find that there is significant physician learning based on the evolution of patient satisfaction measures.  Other types of information seem to have less of an effect on physician prescribing behavior.  Physicians hold strong priors which leads to slow updating.

Interestingly, “News articles have a positive influence on prescriptions, no matter whether these titles sound negative or non-negative. This suggests that the major role of news articles is informing doctors/patients of the existence of Cox-2s, rather than revealing the quality of Cox-2s…In contrast, a medical article about Cox-2s has a significant negative impact on prescription sales, even if its title and abstract are non-negative.”

Thus, the authors find that doctors learn more within-patient than across patient.  

FDA updates have no impact on physician behavior.  How can this be?  By the time the FDA issues a warning on the risks of the potential risk of different drugs, there almost always have been academic articles published on these issues.  This is not to say that the FDA updates are useless, only that they often come after the academic research has already taken place and the physicians have already updated their priors.

For policy makers, the conclusions of this study may be worrisome for proponents of evidenced based medicine.  Currently, physician learning from new information is very slow.  Secondly, physicians seem to focus on tailoring treatments to individual patients rather than updating whether or not the treatment is worthwhile in the first place. 

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Optimal Contracts in the British NHS

January 20, 2009 in Asymmetric Information, Hospitals, International Health Care Systems, Supply of Medical Services | No comments

One of the perennial questions of interest for health services researchers how to pay for health care.  A paper by Chalkley and McVicar (2008) examines this question in the contest of a reform in Britain’s National Health Service (NHS).

“After 1990 hospitals, which had previously been under the direct control of Health Authorities, could apply for NHS Trust status whereby they would be given discretion over employment, remuneration scales and the disposal of assets. This discretion was subject to limits and reservations and could ultimately be revoked if the appropriate authority, in this case the Secretary of State for Health, deemed their actions to be against the ‘public interest’.”

There are three types of payment contracts between the NHS and health authorities.  The first is a block contract where hospitals receive a flat contract to care for a patient population regardless of the actual care given.  The second contract is a cost-per-case contract where the hospital is paid based on the cost of the medical services supplied.  Volume contracts similarly base payment on the quantity of care provided.  The final contract is a sophisticated block contract.   This is similar to the simple block contract but requires the NHS to monitor the hosptials to ensure that they are providing the required care.

The authors offer 4 conjectures of when different contracts will be adopted.  When contracts will be adopted depends on 3 characteristics: variability in cost, variability in volume and easy of observing patient treatment.

  1. When variability in cost and volume is small or variability of cost is small and volume is easily observable]then block contracts will be favored.
  2. When monitoring costs are low, variability of volume is large, variability of cost is small then sophisticated block contracts will be favored.
  3. When variability in cost is large and variability of demand is large then cost-per-case payments will be favored.
  4. Characteristics of purchasers and/or providers that mitigate monitoring costs will give rise to a greater use of volume-dependent and sophisticated block contracts relative to simple block contracts. 

The results from a multinomial logit regression support these conjectures.  

Thus, although policymakers would prefer a one-size-fits all contractual arrangement, the authors show that contractual arrangements between the health purchaser and the provider must take into account the variability inherent the good being supplied.  For instance, simpler contracts should be favored when monitoring costs are high, but more complex contracts may be feasible with lower contractual cost.  Low variation in cost and volume makes block payments very attractive.  However, when there is significant variation in cost or volume, cost-per-case or volume-dependent contracts can help mitigate the providers financial risk.

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Choice, Price Competition and Complexity in Markets for Health Insurance

February 27, 2008 in Asymmetric Information, Health Insurance, International Health Care Systems | Permalink

Yesterday, I spoke about the Swiss health care system. One of the main attributes of this system is that patients are allowed to choose from any health care plan and the health insurers can not refuse to cover them. Further, since the insurance benefit is mandated by law, there is very little quality difference between plans. Only 8% of health plans restrict provider choice in any way.

An NBER working paper by Frank and Lamiraud analyzes how the number of firms offering health insurance in Switzerland has affected price dispersion and expected price.

Economists generally believe that when more firms/products enter the market, this will increase the probability that a superior health insurer will be available to the public. However, when more products enter the market, search costs increase. Thus, it is theoretically possible for more health insurance offerings to decrease utility.

A paper by Janssen and Moraga-Gonzales (2004) show that an the welfare impact of an increase in the number of sellers depends on the consumers’ search intensity. When consumers search with low intensity, having more firms will reduce search, will not affect expected price and will to greater price dispersion. One can think that inexpensive and/or infrequently purchased items would fall in to this category. When consumers search with high intensity, an increase in the number of firms will increase searching and decrease prices. In the 401(k) market, when employees are offered more than 10 choices, there are reductions in consumer responses (i.e.: less investment switching occurs).

Frank and Lamiraud aim to analyze how the number of firms affects insurance choice in Switzerland. They use a panel survey from the Federal Office for Social Insurance (OFAS). It contains a sample of 2152 individuals and asks about their insurance coverage between 1997 and 2000. Yearly premiums are available at the Federal Office for Public Health (OFSP) website. While there was consolidation in the health insurance market among firms, the average number of health plans offered per canton increased from 39 in 1998 to 52 in 2003. The number of people switching plans was 4.8% in 1997, 5.4% in 1998, 2.7% in 1999 and 2.1% in 2000.

How do people choose their plan?

…40% of people choose a health plan following their parents’ and friends’ choices, and what they see as tradition. Furthermore, as many as 25% individuals declare that they do not strive to pick the health insurance plan with the lowest premium. A substantial number of people explicitly report staying with their health plan based on habit (13.5%) or because they are satisfied with their arrangement (79%)

What do the authors conclude from the data?

First, we show that consumers that switch health plan pay 15% to 16% less in health insurance premiums per month holding ceteris paribus. Second, we show that among consumers expressing dissatisfaction with their health plans those in markets with fewer choices are more likely to express intent to switch. Finally, consumers that used an agent to help them purchase insurance consistently paid significantly lower premiums. This set of results suggests that “mistakes may have been made”.

Despite what orthodox economic theory states, no market is perfect. Understanding these market imperfection imparts important knowledge for economists and health policy makers.

Insurance Markets and Advantageous Selection

January 30, 2008 in Asymmetric Information, Health Insurance, Information | 5 comments

Adverse selection is often seen as a major impediment to the efficient functioning of insurance markets. Rothschild and Stiglitz (1976) create a model where high risk people buy full insurance while low risk individuals buy partial insurance. Yet empirically, one finds that in some insurance markets, low risk individuals purchase more insurance than high risk individuals.

An NBER working paper by Cutler, Finkelstein and McGarry (2008) claims that preference heterogeneity may explain this phenomenon. If low-risk individuals also have a stronger risk aversion preferences, than they may buy more insurance than a high-risk individual who has risk loving preferences. This work is an extension of the Finkelstein and McGarry (AER 2006) article discussed in one of my earlier blog posts.

Using data from the Health and Retirement Study (HRS), the authors measure risk tolerance using the following variables: smoking, having 3+ alcoholic drinks per day, job-based mortality risk, receipt of preventive health services, and seat belt usage. The authors find a negative relationship between individuals who engage in risky behavior (i.e., smoking, drinking, and those working in a high-risk occupation) and the percent who purchase various types of insurance, and a positive relationship between those engage in risk reducing behavior (i.e., preventive medical care, seat belt usage) and the purchase of insurance.

  Smoking Drinking Job Risk Prev. care Seat belt
Life Ins - - - + +
Annuity - o - + +
Long-term care o o - + +
Medigap - o - + +
Health Ins - - - + +
           

In the above chart, ‘+’ represents a positive statistically significant correlation, ‘-’ represents a negative statistically significant correlation, and o indicates that the relationship is not statistically significant.

The authors can also measure risk preferences based on respondents answers to income gamble questions. There is a weak relationship between risk preferences and risk behavior however.

The authors confirm that risk behavior lead to an increased probability of adverse events which would be covered by insurance. For instance, smoking increases mortality which would lead to an earlier life insurance payout. Increased preventive health activities decrease the probability of a nursing home stay.

Thus the authors conclude the following:

Our analysis yields two main findings. First, in all five markets, we find that individuals who engage in what are commonly thought of as risky behaviors (smoking, drinking, or prior employment in jobs with higher mortality rates) or who do not take measures to thought to reduce risk (preventive health activities or wearing of a seat belt) are systematically less likely to hold each of these insurance products. Second, we find that these same individuals tend to have higher expected claims for life insurance and long term care insurance, but lower expected claims for annuities; for Medigap and acute health insurance, there is no systematic relationship between the behavior measures and expected claims.

These results can help to explain the puzzle of insurance we started with: why is adverse selection not more common? In annuity markets, there is clear evidence of adverse selection: people who live longer are more likely to buy insurance. The standard adverse selection model is one explanation for this, but so is variation in risk tolerance; people who have less risky behaviors live longer and are more likely to buy annuities. In life insurance, our results suggest that differential risk tolerance can help explain why people with lower mortality rates have more insurance. Similarly, in the case of long-term care insurance, people who use more preventive care or are more likely to wear seat belts buy insurance more readily but also stay out of nursing homes.

When can IT improve medical quality and cut costs

October 31, 2007 in Academic Articles, Asymmetric Information, Healthcare IT | Permalink

Many people believe that better information technology (IT) can help improve the quality of medical care in the U.S. and around the world. For instance, if a doctor prescribes a drug which interacts harmfully with a drug the patient is already taking, a computer program could notify the doctor of this problem. If a patient has a certain disease, a computer program could help to narrow down the physician’s treatment options.

The problem with this top-down, one-size-fits-all doctoring is that every patient is unique. Of course, there are medical standards which should generally be followed, but often these standardized treatments have to be altered slightly or completely depending on patient wishes, the prevalence of multiple diseases, or economic constraints. Further, the physician typically has superior information regarding the patient’s condition than a standardized computer algorithm.

An NBER working paper by Javitt, Rebitzer and Reisman investigates a randomized trial of the implementation of a new IT system at a commercial HMO plan. The program was ‘turned on’ for some patients for each physician in the HMO and ‘turned off’ for other patients. The program would give three care consideration (CC) alerts: stop a drug, do a test, or add a drug. Each of these warnings had 3 severity levels.

The authors compare data on the CC alerts which occurred for the ‘switched on’ patients with the hypothetical CC alerts which would have occurred if the software had been running. The authors found that the IT program lead to 6% lower charges in the turned on group compared to the control. The authors interpret this to be due to lower hospitalization rates and medication complications in the treatment group.

One key aspect of the program is that the CC alerts could be ignored by doctors.

“Physicians often have better information about their patients than does the error detecting software. Actions that look like a misstep to the computer may in fact be the result of informed physician choice, informed patient choice and/or patient non-compliance. For this reason, the HMO and the software company viewed CCs as recommendations that physicians were free to ignore if they disagreed.”

Physicians seemed to heed these CC alerts more than other studies had previously thought they would. Why?

Our results, however, suggest something more: the messages in the CCs seem to have had a bigger effect on physicians than the conventional medical channels used to promote the HOPE trial findings. It seems plausible that the CCs were influential because they linked a general recommendation (“take ACE inhibitors�?) to a specific patient [my italics] and a specific cite to the medical literature.”

To me, this seems to be the most appropriate way to implement medical IT.

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