Supply of Medical Services

Traditional economic theory suggests that when the price of a good falls, the amount supplied will fall as well. Most economists always assume that the supply curve is upward sloping.

But that is not always the case in medical world. Because a physician serves both as the patient’s advisor and the supplier of medical treatment, physicians can induce patients to increase the amount of medical care they wish to receive. Patients are easily convinced because of 2 market failures: asymmetric information and moral hazard.

Asymmetric information means that the physician know more about your health condition than you do. Thus, patients rely on the advice of the physician. Moral hazard occurs because patients have health insurance. Since patients do not pay for the care they receive (or pay for it at a reduced rate), they are very amenable to follow the doctors orders.

A 1998 letter from the Health Care Financing Administration (HCFA) found that it was typical that a 50% offset will occur when Medicare payments are reduced. This means that a 20% reduction in price, will lead to a 12.5% increase in quantity. Overall, this will lead to only a 10% decrease in total cost. Since 10% is 1/2 of 20%, we have a 50% offset. Physicians are increasing the quantity provided in order to make up for the income lost from lower Medicare reimbursement rates.

This is a classic example of supplier induced demand.

An article by Yip (JHE 1998) found that this was the case for coronary artery bypass graft (CABG) surgeries as well.

• Winnie C. Yip, 1998. “Physician response to Medicare fee reductions: changes in the volume of coronary artery bypass graft (CABG) surgeries in the Medicare and private sectors.” Journal of Health Economics, Volume 17, Issue 6, December 1998, Pages 675-699

Simon Caulkin, management editor of The Guardian, has a great article titled “The rule is simple: be careful what you measure.”  The article discusses the fact that measuring performance leads to better performance on the dimensions measured, but can often lead to significantly worse performance on the unmeasured dimensions.  For instance,

What happens when bad measures drive out good is strikingly described in an article in the current Economic Journal. Investigating the effects of competition in the NHS, Carol Propper and her colleagues made an extraordinary discovery. Under competition, hospitals improved their patient waiting times. At the same time, the death-rate following emergency heart-attack admissions substantially increased. Why? As targets, waiting times were and are measured (and what gets measured gets managed, right?). Emergency heart-attack deaths were not tracked and therefore not managed. Even though no one would argue that the trade-off - shorter waiting times but more deaths - was anything but a travesty of NHS purpose, that’s what the choice of measure produced.

Hat tip to DB’s Medical Rants for this one.

The Retired Doc’s Thoughts has an interesting post as well.

“When regulators or policy makers succeed in improving the quality of care provided by some doctors, do patients even notice and/or care?” This is the question which Kenneth L. Leonard attempts to answer. Davies and Ware (1988) do state that patient satisfaction is correlated with average quality levels. One problem with most studies is that quality improvements take place over a long period of time. It is possible that as quality improves, patient expectations also increase and thus overall reported patient satisfaction may be unaffected.

How does a researcher get an exogenous change in quality? Leonard uses the Hawthorne Effect. The Hawthorne effect states that quality will improve whenever there is a change in environment. In this study, doctors are observed by researchers. The study finds that objective measures of quality improve immediately after researchers arrive, but the quality improvement slowly decays over time until after about 10 observations, the doctor returns to their original quality level.

So, do patients actually notice this quality improvement? Leonard find that:

“Patient do, in fact, recognize and value quality care. A 1% increase in protocol adherence (from an average adherence of about 53%) is associated with about a 0.40% increase in the probability that a patient will declare the consultation to have been “very good” (from an average level of about 12%).”

• KL Leonard (2008) “Is patient satisfaction sensitive to changes in the quality of care? An exploitation of the Hawthorne effect” Journal of Health Economics, 27(2): 444-459.
• A. Davies and J.J. Ware (1988). “Involving consumers in quality of care assessment, Health Affairs” 7, pp. 33–48.

Paul Levy, the president and CEO of Beth Israel Deaconess Medical Center in Boston made about $1 million dollars in 2005. Of this, $650,000 was base salary, $195,000 was made up of incentive bonus, and the balance was composed of compensation for health insurance, life insurance, and retirement.

How do I know these figures? Paul Levy told me. Well, he didn’t tell me directly, but he did post these figures on his Running a Hospital blog. Levy writes:

So, if you were on my board, how would you set an appropriate salary? You might look at the competition, and as the Globe notes, the salaries for most of the Boston-area hospital CEOs center around the same level. Would you look at salaries of people in for-profit companies, and, if so, how do you measure comparable size and complexity? Would you look at salaries of other types of non-profits, like universities and museums?

Does it matter that the average tenure of a hospital CEO is under six years? If that is roughly the tenure of a major league baseball player, should CEO salaries be in the same ballpark? Sorry, I couldn’t resist!

The New York Times recently posted an interactive page on executive pay. So the question remains, readers, how would you set Paul Levy’s salary?

An Annals of Internal Medicine survey sheds some light on physicians opinions regarding universal health care. Overall 59% of physicians support national health insurance and 32% oppose it. Support for national health insurance increased 10 percentage points since 2002 (49%). Unsurprisingly, surgical subspecialties, anesthesiologists, and radiologists, were the only specialities where more than half of respondents did not support universal health care.

Any economist would not be surprised by these findings. Primary care is not highly compensated now and universal health care would likely not alter this. Further, primary care would likely simplify the world of primary care: there would either be one insurance company (as in the case of government provided care), or it would likely be clearer which treatments would be covered. Further, since there would be no uninsured, the primary care doctors would not have to provide any uncompensated care.

For specialists, however, it is likely that national health insurance will reduce compensation for physicians. Some procedures may not be covered, or will be reimbursed at lower rates. More referral restrictions and likely rationing of care would lead to lower profits for specialists.

Even physicians are divided about whether or not national health insurance is a good idea.

GoozNews has more information on the article.

A recent article in the Journal of Health Economics found that increasing Medicare reimbursement may have no meaningful effect on hospital use or patient outcomes.

“There is widespread concern about the quality of health care in the US, and the effect of provider payments on the quality of care is an important and unsettled issue in this debate. The critical question is whether changes in provider payments affect health. To date there is relatively little research on this question. Here, we present evidence of the effect of plausibly exogenous changes in Medicare reimbursement – caused by geographical reclassification – on hospital staffing (nurses) and patient outcomes. We find that changes in Medicare reimbursement levels of approximately 10% have no meaningful effect on hospital use of resources or patient outcomes.“ 

• R Kaestner, J Guardado, 2008. “Medicare reimbursement, nurse staffing, and patient outcomes” Journal of Health Economics, 27(2): 339-361.

David Williams of the Health Business Blog reviews an article from the Boston Globe (”Immigrants…“)  stating that immigrants reduce the cost of health care.  How can this be with so many immigrants relying on government programs and free clinics to receive their care?

While it is true that immigrants are consumers of medical care, they are also producers as well.  A study of the health care workforce in Massachusetts finds that 40% of pharmacists, 28% of physician assistants, 22% of opticians, 21% of licensed practical and vocational nurses, 17% of dentists and 14% of paramedics are foreign born workers.  Twenty-eight percent of physicians and surgeons are foreign born.

An increased supply of health care workers from foreign countries can help decrease labor costs for medical care.

The N.Y. Times (”…No Rhyme or Reason“) has an interesting essay about how doctors financial incentives pressure them to run too many tests on patients and refer them to too many specialists.

Doctors are usually reimbursed for whatever they bill. As reimbursement rates have declined in recent years, most doctors have adapted by increasing the quantity of services. If you cut the amount of air you take in per breath, the only way to maintain ventilation is to breathe faster.

The Healthcare Economist believes that money matters in medical matters.  My research regarding specialist compensation shows that financial compensation has a huge impact on surgery rates.

Musing on the modern propensity for physicians to overtreat their patients, one hospital executive said:

“The hospital is a great place to be when you are sick…[b]ut I don’t want my mother in here five minutes longer than she needs to be.”

Most public health officials believe that increasing the supply of primary care doctors is almost always a good thing, while increasing the number of specialists can have mixed results. One problem is that physician supply is endogenous. One may believe that physicians prefer to locate in wealthier areas. If wealthier people are also healthier, then a correlation will exist between physician supply and health even though no causality exists.

In order to isolate the direct causal effect of increasing family physician supply, Gravelle, Morris and Sutton (2008) use an instrumental methods methodology. The two instruments for physician supply are: an index of local area housing prices and average age-related capitation payments. Since physicians location decisions are regulated by the Medical Practices Committee and do not include a cost-of-living adjustment, we would expect lower physician supply where there housing prices are higher. Local area average capitation payments should not effect any individual’s health, but should attract increased family physician supply.

These instruments are implemented on the Health Survey of England data set. Physician supply comes from the General Medical Services (GMS) Statistics database.

Health levels are either measured as very good, good, fair, bad, or very bad. In this case, an ordered probit regression is used. The authors also utilized the EQ-5D continuous scale health measure. With the continuous variable, a least squares regression model is used. What are the results?

When no instruments are used FPs [family physicians] have a positive but statistically insignificant effect on health. When FP supply is instrumented by age-related capitation it has markedly larger and statistically significant effects. A 10 percent increase in FP supply increases the probability of reporting very good health by 6 percent.

Since almost all medical care and pharmaceuticals are free to patients, increased physician supply will not act to reduce prices. Nevertheless, more family physicians can make going to the doctor more convenient and can reduce waiting times, thus increasing the number of family physician visits per individual per year.

One interesting econometric technique used in this paper is that of the anti-test. A paper by Dranove and Meher (1994) criticizes the use of instrumental variables because the use of some instruments can be used to “prove” that increased physician supply “causes” increased childbirth. This is obviously a nonsensical correlation. In this paper, the authors use instrumented and noninstrumented family physician supply to see these variables have any effect on the individual’s ethnicity. Neither the instrumented or noninstrumented physician supply has any impact on ethnicity. Thus, we have some indication that the two instruments chosen by the authors are valid.

• David Dranove and Paul Wehner, 1994. “Physician-induced demand for childbirths” Journal of Health Economics, v13(1): 61-73.
• Gravelle H, Morris S, Sutton M. 2008. “Are Family Physicians Good for You? Endogenous Doctor Supply and Individual Health.” Health Services Research, Early Online Articles.

Diarrheal disease is a leading cause of childhood mortality in many developing countries. The best treatment when diarrhea strikes is to give the patient Oral Rehydration Solution (ORS). Who provides better care for this disease, public or private providers?

A paper in Health Economics by Waters, Hatt and Black (2008) looks at data from the Living Standards Measurement Surveys (LSMS). This data set draw observations from Latin American and Caribbean countries.

The first item that Waters and co-authors note is that richer household are more likely to see the private physician. “Each additional quintile of household economic status is associated with an increase of 6.5 percentage points in the probability that a child with diarrhoea is taken to a private provider.”

But do these wealthier households receive better care? According to the authors, “treatments provided in the private sector are manifestly of worse quality than in the public sector. A total of 33.0% of children visiting a public provider received Oral Rehydration Solution, compared to 13.7% of those visiting a private provider. Conversely, children treated by a private provider are more likely to receive drugs, most commonly unnecessary antibiotics.”

Private physicians have an incentive to prescribe the more costly antibiotics because they make more money. The publicly financed providers do not have this incentive. The authors claim that prescribing antibiotics may also augment the provider’s reputation with patients. This would be analogous to an American physician preforming an MRI when it is not necessary. The physician makes money on the MRI, the patient believe they are getting high-tech care that is the best in the world, but the MRI may often be a waste of resources.

Winner: Public Providers

• Hugh R. Waters, Laurel E. Hatt, Robert E. Black (2008) “The role of private providers in treating child diarrhoea in Latin America” Health Economics, 17(1): 21-29.

An interesting article (”Sudden Death…“) at the Covert Rationing blog addresses the poor care given to cardiac patients in hospitals. Dr. Rich states that:

“…hospitalized patients who have cardiac arrest (sudden loss of cardiac function due to the onset of a heart arrhythmia known as ventricular fibrillation) are often not receiving defibrillation (an electrical shock delivered to the chest) within the recommended 2-minute window of opportunity. Further, patients whose defibrillation is delayed beyond the 2-minute window have a substantially reduced chance of surviving the cardiac arrest….An accompanying editorial (written by Dr. Leslie Saxon, an old friend of DrRich) points out that in public areas where Automatic External Defibrillators (AEDs) are available, such as casinos, the odds of surviving a cardiac arrest is over 50%. In contrast, the odds of surviving cardiac arrest in a hospital, according to this new study, is only 34%“

Why would this be the case?  Casino’s may have a business interest in saving people’s lives in order to get some good publicity and more consumer loyalty by survivors.  On the other hand, hospitals may actually save money by not treating cardiac patients appropriately since patients experiencing cardiac arrest are likely to be “individuals with chronic and expensive medical problems - most often they have coronary artery disease, diabetes, or heart failure - and (as DrRich has pointed out before) their sudden death today will save the system countless dollars tomorrow.”

Insurance companies do have a financial incentive not to treat cardiac arrest patients optimally.  The question is whether or not these financial incentive impact the manner in which physicians practice medicine in the hospital.  Dr. Rich gives some evidence that financial incentives may be playing a significant role in the quality of hospital care in the U.S. today.

There has been a recent trend for more and more surgeries to take place in ambulatory surgery centers (ASCs). In fact according to the Medicare Payment Advisory Commission, in 2004 up to 70% of surgeries took place in these ASCs. Do ASCs offer better quality surgical procedures than Hospital Outpatient Departments (HOPD)?

ASCs may be an improvement over HOPDs because these centers preform a high volume of a few specific procedures which may increase quality. Further, ASCs often have newer equipments than hospitals. On the other hand HOPDs generally have more resources than ASC to deal with complications and economies of scope may help hospitals to provide more effective care.

How do we find out which facility offers better quality? This question seems easy to answer: find a quality metric and measure whether ASCs or HOPDs score higher. The problem is that physicians may choose to conduct surgery on healthier patients at the ASC and perform the surgery on sicker patients in the HOPD. This way, if there are complications from surgery on a relatively sicker individual, the hospital will have more capabilities to deal with the situation. This selection problem, however, can bias studies which simply compare the quality levels of ASCs and HOPDs without taking into account difference in patient characteristics in each facility type.

A study by Chukmaitov et al. (HSR 2007) attempts to measure these quality differences using patient-level surgery data in Florida between 1997 and 2004. The authors attempt to eliminate the selection bias using physician diagnoses (i.e.: DRG/HCC methodology) to quantify the ex-ante and ex-post health status of the patient. The key to this methodology is that the authors also have information on any of the patients’ secondary diagnoses.

With this data, the author do in fact find that HOPDs do have a sicker patient base. Thus, it is important for researchers to correct for this selection problem. Secondly, Chukmaitov and co-authors found that “…neither organizational type (ASCs or HOPDs) performed better overall, there appear to be important differences in quality outcomes for certain procedures. These differences may be related to variations in organizational structures, processes, and strategies between ASCs and HOPDs.”

On problem with the study is that the authors use mortality as their quality metric. A more sensitive metric could better capture quality differences. Also, one may worry about the accuracy of the doctor diagnoses. HOPDs may be more sensitive to DRG creep than ASCs–or vice versa–and this may leaded to an incorrect selection correction methodology.

• Askar S. Chukmaitov, Nir Menachemi, L. Steven Brown, Charles Saunders, Robert G. Brooks “Comparative Study of Quality Outcomes in Freestanding Ambulatory Surgery Centers and Hospital-Based Outpatient Departments: 1997-2004”
  Health Services Research (OnlineEarly Articles).

Doctors are often perceived as benevolent professionals. They are hard-working individuals who extend their largesse by giving away free medical care to those in need. Studies by Cunningham and May (2006) and the American Hospital Association find that doctors provide uncompensated care equal to 6.3% or 5.6% of their cots annually respectively.

A recent Journal of Health Economics article by Gruber and Rodriguez concludes that these figures may be overstated. In fact, the study finds that physicians provide negative amounts of uncompensated care to the uninsured.

How is this possible? While it is true that doctors do give away free care to the uninsured and that many of those without insurance do not pay their bills, the uninsured patient often pay a large portion of the list price whereas those who have insurance receive a negotiated lower price. Thus, the authors find that “the majority of physicians actually make money, on net on their uninsured patients…12-14% of physicians found their uninsured patients patients more than twice as profitable as their insured patients; that is the net payments from the uninsured were more than twice the expected payments from the insured patients.”

Even when the authors ignore the higher list prices the uninsured pay, they still find that only about 1% of total revenues are given away as free care to the uninsured. Much of this amount, however, is due to non-payment by the patients rather than free care given away by the physicians.

Medicine may be a business after all.

• Gruber J, Rodriguez D. 2007. “How much uncompensated care do doctors provide?” Journal of Health Economics, 26(6): 1151-1169.

A New Yorker article (”The Checklist“) recounts Peter Pronovost’s efforts to improve the delivery of medical care. One of his simplest ideas was to invent a 5 step checklist to reduce line infections:

Doctors are supposed to (1) wash their hands with soap, (2) clean the patient’s skin with chlorhexidine antiseptic, (3) put sterile drapes over the entire patient, (4) wear a sterile mask, hat, gown, and gloves, and (5) put a sterile dressing over the catheter site once the line is in.

All doctors know these 5 steps, but in the distraction-filled world of the I.C.U., it is very easy for the physician to forget any one of the steps. Dr. Pronovost’s checklist idea has extended to other treatment areas as well. Yet he believes that Americans are still not getting serious about treating medical care as a science.

“The fundamental problem with the quality of American medicine is that we’ve failed to view delivery of health care as a science. The tasks of medical science fall into three buckets. One is understanding disease biology. One is finding effective therapies. And one is insuring those therapies are delivered effectively. That third bucket has been almost totally ignored by research funders, government, and academia. It’s viewed as the art of medicine. That’s a mistake, a huge mistake. And from a taxpayer’s perspective it’s outrageous.�? We have a thirty-billion-dollar-a-year National Institutes of Health, he pointed out, which has been a remarkable powerhouse of discovery. But we have no billion-dollar National Institute of Health Care Delivery studying how best to incorporate those discoveries into daily practice.

Checklists are not the solution to every problem.  A large portion of medicine deals with complex condition with large uncertainties and many disease interactions.  Further, it may be more difficult for an insurance company to institute checklists than a hospital manager or someone further down the chain of command.  Nevertheless, standardization in medicine should help to dramatically improve quality.

A paper by Alexander S. Preker and April Harding at the World Bank analyze the roles of the public and private sector in health care.  One of the more interesting portions discusses medical care financing using some examples from ancient history.

“Ideological views on the roles of the state and the private sector belong to a long list of false antitheses in the field of medicine and health care. Since the beginning of written history, the pendulum has swung back and forth between minimalist and heavy-handed state involvement in the health sector.

During antiquity, people used home remedies and private healers when they were ill. Yet, as early as the second millennium B.C., the papyri give fascinating evidence that Imhotep, archetypal physician, priest, and court official in ancient Egypt, introduced a system of publicly provided health care with healers who were paid by the community.

At best, financing, organization, and provision of health care was limited to the royal courts of kings, emperors, and other nobility who might have a physician for their personal use and for their troops at the time of battle. The masses got by with local healers, midwives, natural remedies, apothecaries, and quacks.” 

•  Alexander S. Preker and April Harding. 2000. “The Economics of Public and Private Roles in Health Care: Insights from Institutional Economics and Organizational Theory.” HNP Discussion Paper, World Bank.

Economists and health researchers have generally shown that when doctors are paid on a fee-for-service basis, they will advice the patient to undergo more medical procedures than when the doctor is paid on a capitation or salaried basis (see my own paper: “Operating on Commission“). Which payment method maximizes welfare has not been proven and is likely to vary based on the medical procedure in question.

To add to the confusion, Astrid Selder writes in “Physician Reimbursement and Technology Adoption” that physician reimbursement mechanism may also affect the rate at which new technologies are adopted.

Selder lays out the following simple theoretical structure in which the social planner maximizes the following:

• (1-π)U(Y-P)+πU(Y-P-acm-ε +G(m) -nm)
• s.t.: P + πacm-%pi;R=πpm>=0
• s.t.: R + (p-c)m>=0
• s.t.: m<=B

The first constraints represent the zero-profit conditions for the insurer and the physician respectively and the third is a technology constraint. The probability of falling ill is π. Individuals have income Y, an insurance premium of P, and a coinsurance rate of a to may for the marginal cost, c, of a given level of medical treatment, m. Non monetary treatment costs (e.g.: side effects, time costs) are represented by the parameter n, while the benefits of the treatment are represented by the function G(m). The doctor receives a capitation payment of R and a marginal revenue amount given by p-c. B represents the technology constraint.

It seems obvious, but Selder shows that an increase in monetary medical costs (c) or non-medical costs (n) are welfare destroying. Technological innovations, represented as an increase in B are welfare improving if the technological constraint is binding or have no impact on welfare is the technological constraint is not binding. This results is intuitive: higher costs are bad, technological innovation is good.

This result, however, only holds when there is perfect information. With asymmetrical information, technological innovations may be welfare enhancing or welfare destroying. The welfare enhancing argument is similar to above: better technology leads to better medical care. If there is a moral hazard problem, however, using more basic technologies may actually help to counteract the tendency to over-consume medical care in the presence of insurance. Also, in the presence of moral hazard, technological innovations will cause insurance companies to charge higher premiums in order to cover the additional cost of this care.

How does the technological constraint, B, affect welfare in different physician compensation schemes?

• “If B is a binding constraint and increases in B are welfare enhancing then a fee-for-service system should be used. Increases in B and reductions in [monetary medical costs] c are induced all of which are welfare improvements. Incentives with respect to [non-monetary costs] n cannot be improved upon.
• If B is a binding constraint and increases in B are welfare reducing then a system of cost sharing [e.g.: capitation] should be implemented which induces reductions in B and c and gives no clearcut incentives with respect to n. Incentives with respect to B and c are thus optimal, and incentives with respect to n cannot be improved upon.
• If B is not binding the optimal reimbursement system depends on the demand elasticities with respect to costs.”

In words:

“It has turned out that the state should classify diseases or treatments into subgroups which are reimbursed differently in order to achieve the desired welfare effects in each subgroup. Especially for the case of extremely severe illness shocks the introduction of a fee-for-service system may be socially desirable. It is these very severe diseases where the capitation system seems to induce negative welfare effects with respect to the technologically feasible boundary of treatment. Cost sharing/capitation systems are most suitable for treatments where a reduction of the amount of health care consumed is desirable.”

While the solution Selder proposes is logical, it does not take into account the cost to the state to collect information regarding these disease classes. Lobbying will likely influence whether a given treatment falls into the fee-for-service or cost-sharing grouping. Nevertheless, Selder’s general findings seems reasonable and applicable to the medical care finance design in the real world.

• Selder, Astrid. 2005. “Physician Reimbursement and Technology Adoption” Journal of Health Economics Volume 24, Issue 5, Pages 907-930

“On the biggest shopping weekend of the year, you’ll know the price of the big-screen TV and the Wii you’re about to buy - but you’ll likely be in the dark about the cost of any health care you need.”

Two Wisconsin state senators are trying to change this.  The Milwaukee Journal Sentinel (”…Health cost disclosure“) reports that “Sen. Jim Sullivan (D-Wauwatosa) and Rep. Steve Wieckert (R-Appleton) this week introduced a bill requiring health care providers and insurance companies to make available information about the cost of procedures or services to patients who ask for it.”

Here is one type of government intervention I support: the government is helping to reduce asymmetric information in the medical care market without fixing prices.  The bill would compel providers to state the cost of the 50 most common medical procedures.  These costs would be divided into 3 groups: the usual rate for the service (whatever that means); the rate paid by government programs; and the average rate for health plans.

Milwaukee pediatrician Carl Eisenberg, does note that the cost of a procedure can vary from patient to patient.  Nevertheless, the provider could charge a fee in which for some patients they lost money, but for some “easy” patients they made above average profits. 

There are other problems with the pricing structure.

Larry Rambo, chief executive officer for Humana’s Great Lakes region, said cost information might best come from an insurance company because it can encompass the “episode of care,” meaning all the costs that go into treatment.

“If you’re going in for knee surgery, you’re going to get a bill from the hospital, you’re going to get a bill from the surgeon, you’re going to get a bill from the anesthesiologist and you’re going to get a bill from the radiologist,” Rambo said.

Nevertheless, I am always in favor of more information.  As a supreme court jurist once wrote: “sunlight is the best disinfectant.”

I recently read a Health Affairs article analyzing a pay-for-performance (P4P) demonstration. The Local Initiative Rewarding Results (LIRR) demonstration in California involved seven Medicaid-focused health plans in California between 2003 and 2005. Here are some of my most recent thoughts on P4P:

• The article seemed to show that P4P worked best when there was much consensus over how doctors should treat patients. The irony of P4P is the following: P4P programs with high levels of physician consensus work, but if everyone physician is already acting appropriately P4P does not make a major change in behavior; larger behavioral changes can occur when physicians are greatly deviating from the optimal practice, but in this case it is very difficult to implement P4P.
• P4P plans are often implemented so those that do well gain income and those that preform poorly maintain their status quo income. When no one loses any money, P4P is supposed to gain more acceptance among physicians. In equilibrium, however, an economist would predict that those with low P4P scores will receive lower wages in the future (or wage increases below inflation) due to P4P.
• Unsurprisingly, the authors of the Health Affairs paper found that meager financial incentives do not significantly change physician behavior, while larger financial incentives have a greater impact. Also, good communication with physicians helps to increase physician compliance with P4P mandates.
• If P4P evaluates doctors on care levels which depend on patient compliance, health plans can help physicians to increase the quality of care. For instance in the above study, “many providers used the plan lists of members turning fifteen months old as the basis for outreach…”
• When P4P financial incentives vary across plans, it is difficult for physicians to focus on specific quality indicators due to complex reimbursement schemes in each plan.
• When HEDIS scores improve, some of this improvement is due to better quality while much of it may be attributed to better documentation in a patient’s medical records.

Suzanne Felt-Lisk, Gilbert Gimm and Stephanie Peterson (2007) “Making Pay-For-Performance Work In Medicaid” Health Affairs, v26(4) pp. w516-w527.

Arnold Kling of the EconLog site has some commentary on P4P when discussing Tim Hartford’s latest book (”The Logic of Life“).

I have a very different approach to compensation. I think that the key is to change compensation schemes frequently. The reason is that any scheme can be gamed, and the longer you wait to change any given scheme, the more effectively the participants will have gamed it. That is one reason I think that “Pay for Performance,” the newest miracle cure for health care costs, will fail miserably. The doctors will be able to run circles around the bureaucrats. In the U.K., they already have–all of a sudden, 91 percent of doctors were receiving bonuses for being above average.

I can’t verify this ‘91%’ figure.  Using pre-P4P measure as the measuring stick will lead to many physicians reaching the ‘above average’ mark in the post P4P mark.  Much of this increase is due to better record keeping; some of it is due to better care.

Looks like the convenience clinic trend is coming to my neck of the woods in Southern California.  According to the San Diego Union-Tribune (”Are retail clinics a healthy choice?“) six Minute Clinics are opening in San Diego county with ten more on the way before year’s end.

These clinics likely will lower the cost of obtaining a flu shot.  Not only will providing the flu shot be less expensive for the provider, the patient will have fewer time costs waiting for a doctor or driving to an inconvenient physician location.  These clinics are likely just as safe as a physician’s office if the patient is healthy.  However, if a patient has multiple diseases and needs a more in depth check-up, these convenience clinics will be poor substitutes in terms of quality.  There is always a tradeoff.

Here are some tips from Dr. Marissa Weiss on building a good doctor-patient relationship from the patient side. Thanks to Dr. Rich’s Covert Rationing blog for the link.

• Greet the doctor–or introduce yourself if this is a new physician–with a professional handshake.
• Let your doctor know what is on your mind and how the doctor can be most helpful.
• Before you get to the doctor’s office, write down your symptoms and the questions you have for the doctor.
• Record the doctors answers to your questions. This can either be done by taking notes, using a tape recorder (ask the doctor’s permission first) or bring a friend or family member to take notes for you.
• Sit close enough to the doctor to feel like you are having a personal conversation, but not too close to make the situation uncomfortable for either of you.
• Feel free to ask the doctor to repeat him/herself if you did not understand what he/she said.
• Make sure that when you receive a test result, that your other doctors are informed of these results.
• Ask yourself what do you value in a doctor. Be sure that your doctors satisfy your needs.
• Go to a doctor who likes being a doctor. These physicians will make the extra effort to provide you with the best care.

According to the Telegraph (’Record numbers go abroad for health‘), “More than 70,000 Britons will have treatment abroad this year – a figure that is forecast to rise to almost 200,000 by the end of the decade.”  Many of these individuals are seeking treatment in countries such as India, Hungary, Turkey, Germany, Malaysia, Poland and Spain.  Why are these individuals going abroad?  Long wait lists and shortages of qualified clinicians (such as dentists - see May 9, 2006 post).

Ezra Klein notes that 100,000 Americans travel abroad for plastic surgery.  Why do they do it?  Here, wait lists and physicians availability are not the major motivators (living in Southern California, I can tell you that there is no shortage of plastic surgeons).   In the U.S., monetary cost is the major consideration for most patients who become medical tourists.

Thus, in both countries, people are trying to find better deals abroad.  Health care is expensive in the UK; not in monetary terms, but due to a high time cost from long waiting lists. Health care is expensive in the U.S.; not due to time costs from waiting lists, but from high monetary cost.

Megan McArdle of Asymetrical Information notes that Baumol’s Cost Disease is one explanation as to why health care is so much cheaper outside the OECD.

Health care systems suffer from Baumol’s cost disease: it’s a labor-intensive service that doesn’t offer huge scope for gains in labor productivity. The number of hours it takes to manufacture a car is consistently falling, but the number of hours it takes to perform doctor’s visits is roughly the same as it has always been. As a society gets richer, in order to attract workers, the labor intensive service has to pay competitive wages with the sectors where productivity is rising rapidly; that means that costs for labor-intensive services rise faster than the general price level.

Bangkok’s doctors are so cheap because a doctor making a modest wage by British standards can have an enormous house and a flock of servants to take care of him, putting him in the very top echelon of Thai earners. Nurses too, can make an American pittance and still live very well. As Bangkok gets richer, the servants and the gigantic house will not be so affordable–and neither will the health care.

This is how a very interesting article (”P4P is changing me“) in Medical Economics begins. The essay won an award in the 2006 Doctors’ Writing Contest.

The author of the story is a physician who has been under much financial pressure of late; a divorce, med school loans, a mortgage, alimony and child support were all eating away at the author’s income. One day an 84-year old WWII vet walks into his office. He had a left renal mass, but did not want any further medical treatment for this problem. The author must contemplate how–or if–he should treat the patient.

“There’s very little I could do to improve [the patient's] life. I asked him once what I could do to help, and he requested merely that I talk with him. That’s what I’ve done, every three months, but I won’t get any extra pay for that. I could check his A1C, though—after all, he does have diabetes, and it has been more than a year since it’s been checked.“

The patient had been very diligent in maintaining the correct levels of blood pressures, heart rates, and blood sugars. The author believed that an A1C test was clinically unnecessary. But…

We talked for 30 minutes (10 minutes over the scheduled 20-minute appointment); I was then behind. How would I wrap this up? Should I make one last plea that he open himself up to counseling and antidepressants, and not suicide? Or should I tell him that I need him to go to the lab and have his blood work checked? Well, I doubted I could do much to improve his life, but I did need the $50 . . .

Should I start statins on the drooling demented to lower their LDL? Should I preach to paranoid schizophrenics that they must quit smoking? Doing so might help ease my burdens—will it ease theirs? Without a financial incentive, I treated practice guidelines as guidelines, and I treated patients as patients. With financial incentives, will the guidelines become my goal? Will I lose patience for patients who are just a means to my means?

• Mansfield, Richard J. (2007) “P4P is changing me,” Medical Economics, May 4, 2007.

On the news yesterday that Microsoft purchased a 1.5% stake in Facebook for $240 million, social networking appears to  highly valued commodity.

“…[T]he strength of social networks among Mexican-Americans is positively related to access to care.”

Is this true? That is hypothesis that a recent NBER working paper by Roan Gresenz, Escarce and Rogowski attempt to test. The authors uses data from 1996-2002 MEPS, along with data from the 2000 Census, Area Resource File, the CPS and the Bureau of Primary Health Care Uniform Data System.

Methods

The econometric specification is relatively simple. A probit regression is used where the dependent variables are either (1) whether the person has a “usual source of care,�? (2) whether or not the person has an office-based physician or non-physician visit during a year; (3) whether the individual has any prescription drug expenditures during the year; (4) whether the individual has any medical expenditures during the year.

Social networks are measured three ways. The first is the percentage of Hispanics in an individuals ZIP code of residence, the second is the percentage of the population that is foreign born and Spanish speaking, and third is the percentage of Spanish speakers in a ZIP code. Each social network measure is interacted “…with an individual-level variable indicating whether the individual was born in the U.S., is foreign-born but has lived in the U.S. for more than five years, or is foreign-born and a recent immigrant.”

Results

The authors find that Mexican-Americans who only speak Spanish have less access to care than bilingual Mexican-Americans. Insured Mexican-Americans who live in areas of dense Hispanic populations have more access to care than insured Mexican-Americans living in areas with a weaker Hispanic presence. The results for uninsured Mexican-Americans are mixed. It seems that uninsured individuals living in an area with more Hispanics or more Spanish speakers leads to more medical expenditures and office visits, but seems to decrease the percentage of the population who spend money on pharmaceuticals or have a usual source of care.

Healthcare Economist commentary

One worry is that areas with larger Hispanic populations are areas with higher economic growth or are somehow different than areas with fewer Hispanics or Spanish speakers. Since the authors use data on a ZIP code level, this may be less of a problem. Further the authors state the following:

“We find no effects of these characteristics of the local population on access to care for U.S. born Mexican-Americans, suggesting that similarities in race and language may contribute more to the formation of social ties among individuals who are less acculturated to the U.S. “

While the data seem to prove that Mexican-Americans–especially the insured– have more access to care in areas with high levels of Spanish-speakers, the mechanism of how this manifests itself is unclear. The authors claim that social networks is the cause. Social networks provide information concerning which doctors are of high quality and are Spanish-speaking. On the other hand, a supply-side story could explain this phenomenon as well. In areas with many Hispanics, there will be more Spanish-speaking doctors and Mexican-Americans will gain more utility from a physician office visit. This is likely true regardless of whether a single individual has a large or small social network.

Even though this paper uses a large data set and has an intuitive result, since their is no measure of an individual’s social network the results can not prove that social networks–rather than other features of heavily Hispanic areas–are causing changes in access to care.

• Roan Gresenz, Carole; Escarce, Jose J.; Rogowski, Jeannette (2007) “Social Networks and Access to Health Care Among Mexican-Americans” NBER Working Paper #13460.

Revascularization (bypass surgery or angioplasty) have been frequently used procedures to treat patients who have experienced a myocardial infarction (MI). These procedure are expensive, but are supposed to enhance longevity. Do they?

This is the question analyzed by David Cutler in his NBER working paper titled “The Lifetime Benefits of Medical Technology.” The problem with many MI studies is that they are short term. Cutler uses data on Medicare beneficiaries who had a heart attack between 1986 and 1988. This means that Cutler can utilize 17 years of follow-up mortality data.

Another issue with non-randomized trials is that of patient selection. Very sick MI patients likely will not receive revascularization surgeries because they not be well enough to survive the surgery. Relatively healthy MI patients may not need revascularization. Thus, even the direction of the selection bias is unknown in this case. In order to account for this selection problem, the paper uses an instrumental variables approach.

The instrument is “the distance to the nearest revascularization hospital [defined as a hospital capable of preforming a revascularization] minus the distance to the nearby hospital of any type.” This instrument was used in a paper by McClellan, McNeil and Newhouse (JAMA 1994). In order for the instrument to be valid, “patients who are more likely to benefit from invasive treatments do not select their residential location based on distance to high-tech medical care.” Cutler argues that this likely true since most covariates are balance above and below the median differential distance. It is possible, however, that richer, healthier people live in more affluent areas and revascularization hospitals locate their facilities to attract these types of patients. I do not know whether or not this is the case. Also, “…if hospitals that provide revascularization are also better at providing aspirin at admission, at managing post-acute follow-up, or at treating subsequent illnesses years later, the instrumental variables estimates will overstate the importance of revascularization.”

Cutler does show that his instrument is relevant as “people who live closer to a revascularization hospital are 3 percentage points more likely to receive a revascularization procedure than those who live farther away.”

Results

The affect of revascularization on mortality is not clear.

“The marginal person receiving a revascularization is about 4 percentage points more likely to survive the first day after the MI than if the person did not receive a revascularization (although not statistically significantly). This gap narrows over time and even reverses by 1 year. At that time interval, people who received revascularization are 6 percentage points more likely to have died than people not receiving revascularization.”

After 17 years, people with MI have a mortality benefit of 5% but this result is not statistically significant. Cutler also examines the cost-effectiveness of revascularization procedures:

“…The greater survival for the marginal patients receiving revascularization translates into 1.1 years of additional life expectancy. The cost of this gain is about $38,000. Thus, the cost per year of life is $33,246.”

A year of life is generally valued at around $100,000, which might lead one to conclude that this is a worthwhile procedure. Since the mortality differential is not measured very precisely, however, one should be somewhat skeptical of this conclusions. Further, Cutler wisely notes that he is not sure whether or not the actual revascularization caused the decreased mortality. Being admitted to a revascularization hospital may simply be a proxy for the receipt of other hospital services, or it could be the case that revascularization hospitals provide superior patient management and patient care than non-revascularization hospitals. Also, due to data constraints, this paper only examines mortality effects. The impact of heart surgery on quality of life is not considered.

• Culter, David M. (2007) “The Lifetime Costs and Benefits of Medical Technology” NBER Working Paper # 13478.

Many health care policy researchers believe that non-physician clinicians, such as physician assistants, nurse practitioners and midwives can help to reduce cost while maintaining quality. Midwifery has gained in popularity over recent years. Groups such as the American Public Health Association, Public Citizen and the National Organization for Women all support increased access to midwifery care.

The question remains whether or not midwife care during pregnancy is inferior, equivalent, or superior to physician care. Amalia R. Miller attempts to answer this question in her 2006 B.E. Press paper.

Differences between midwives and physicians

• Skills: Physicians receive an MD while midwife training is comparable to a nursing background. Only physicians can preform surgery such as a cesarean delivery.
• Financial Incentives: Physicians have a financial incentive to recommend cesareans since they will reap the financial rewards of preforming the surgery. Since midwives do not receive any compensation from surgery, they may be more likely to look out for the best interest of the patient.
• Attitude toward childbirth: The midwifery model of care views birth as a natural process and gives the mother more input towards shaping the birth experience. The physician’s medial approach “…highlights the risks of childbirth, viewing the event as inherently medical, even pathological, requiring hospital admission and technological intervention.”

Quality

One problem with measuring the quality of care received by those who use midwives is that of selection bias. For instance, healthy women may be more likely to use a midwife. Pregnant women with serious complications will be more likely to use physicians. Thus, a researcher may erroneously conclude that pregnant women treated by physicians are more likely to have cesareans, when a more appropriate conclusion would be that physicians treat a less healthy patient base and thus preform more cesareans.

The best study preformed to date is a Chambliss et al. (1992) paper. The authors conducted “[a] randomized blinded clinical trial was conducted in which 492 low-risk patients were assigned to either physician or midwifery management.” Unlike most non-randomized trials, the authors found a small positive relationship between midwifery and cesarean rates.

Methods

The Miller (2006) paper looks at how midwifery affects cesarean rates. They use 1989-1999 Natality Detail Files as well as the 1989-1999 March Current Population Surveys (CPS) as her data source. The author considers a simple OLS regression using cesarean rates as the dependent variable and the use of midwifery–along with other covariates–as the independent variables. The problem of selection bias remains.

Thus, the authors use the enactment of Any Willing Provider laws as a proxy for state-wide midwifery usage. These laws “prohibit discrimination against a class of providers.” While Any Willing Provider laws are not directly related to midwives, the authors also use specific state midwifery reimbursement laws to proxy for midwifery usage. The claim made by Ms. Miller is that these laws are exogenously enacted; this creates a natural experiment and allows a difference-in-difference regression.

Results

Ms. Miller finds that midwifery reimbursement laws, unsurprisingly, do increase midwifery usage by pregnant women but more general Any Willing Provider laws do not alter midwifery usage rates. The authors continues to state that “…The main finding of the Chambliss et al. (1992) study is confirmed: there is no evidence that the expansion of midwifery led to a reduction in cesarean section rates. Hence, the results from the small randomized trial appear to generalize to the population at large, while the non-random trials likely suffered from selection bias due to inadequate controls for patient health and preferences. ”

• Miller, Amalia R. (2006) “The Impact of Midwifery-Promoting Public Policies on Medical Interventions and Health Outcomes,” Advances in Economic Analysis & Policy: Vol. 6 : Iss. 1, Article 6.
• Chambliss LR, Daly C, Mederaris AL, Ames M, Kayne M, Paul R (1992) “The role of selection bias in comparing cesarean birth rates between physician and midwifery management” Obstetrics and gynecology, v80(2) pp. 161-165.

What has happened to physician assistant (PA) education in recent years? An article by P. Eugene Jones (Academic Medicine 2007) enlightens readers with the latest information.

The states with the most PA programs are: New York (19), Pennsylvania (15) California (10), Texas (8) and Florida (6). Alaska, Delaware, Hawaii, Mississippi, Vermont and Wyoming all have zero PA programs. Unsurprisingly, PA concentrations are highest in the states with the highest population.

For the 2002-2003 academic year, one year of PA education costs $36,000 for residents and $44,000 for non-residents. These figures include tuition costs and student fees plus expenses for books and equipment.

The most surprising finding of the article is that there is a trend for PAs to choose jobs outside the primary care fields. I once thought that PAs could be a replacement for primary care doctors when: 1) a patient was healthy or had a disease which was relatively simple to diagnose and treat, and/or 2) when patients wished to spend more time with their medical provider. In fact, Dr. Jones cites an article which claims that PA productivity is 84% of that of a FTE family medicine physician.

Nevertheless, Dr. Jones finds “…the distribution of PAs in the primary care settings of family medicine, general internal medicine, and general pediatrics was 50.8% in 1996. By 2006, only 36.1% were reportedly practicing in theses settings.”

• Jones, PE (2007) “Physician Assistant Education in the United States” Academic Medicine, 82(9) pp. 882-887.

A new study by Tai-Seale, McGuire, and Zhang (HSR 2007) analyzes how primary care physicians allocate their time in a typical office visit. The authors use data from 392 videotaped office visits conducted in three settings: 1) a salaried group practice in an academic medical center (AMC), 2) a managed care group (MCG), and a fee-for-service inner city solo (ICS) practitioners with an Independent Practice Association contract. The authors found that the average length of a visit was 17.4 minutes but the median visit length was only 15.7 minutes.

The data above show that only about 5 minutes was spent on discussing the major issue facing the patient. For minor issues, the doctor and patient only spent one minute discussing the issue. We see that physicians at the AMC spent the most time with their patient while the physicians at the ICS spent the least. The authors find that “…while time spent by the patient and physician on a topic responds to many factors, time of the visit overall is much less malleable.”

The paper also notes that “[i]ncentives in prevailing physician payments favor procedure-based patient care over time-intensive evaluation and management care.” One could easily imagine a system with a flexible physician schedule. The patient could schedule a standard 15 minute appointment for the usual co-pay, or could pay the physician extra if they wanted a 20 minute, 30 minute or 1 hour appointment. This way, the physician would be reimbursed for their time. If the insurance company paid for these extra minutes, the physicians would have an incentive to exaggerate the number of minutes spent on each patient. Thus, a likely solution is for the insurance company to pay for the base appointment length (15 minutes) and anything over this the patient will have to pay for.

• Tai-Seale M, McGuire TG, Zhang, W (2007) “Time Allocation in Primary Care Office Visits” HSR vol 42(5), pp. 1871-1894.

Much has been written on this site about the growth of convenience clinics (see posts on July 25, April 26, and April 17).  The Economist’s Free exchange blog adds to the discussion (”A spoonful of monopoly…“).  It warns how the AMAs monopoly powers may be a threat to convenience clinics such as Medical Marts.

“The fact that there is a shortage of family physicians in many areas did not stop the AMA from trying to stem the growth of these clinics by passing a resolution in June that asked government authorities to investigate the possibility of a conflict of interest in clinic-housing drug store chains that, in effect, both write and fill prescriptions.”

Free exchange agrees with the opinions stated previously on Healthcare Economist.  Convenience clinics should expand choice, offer a low cost alternative, reduce waiting times, make available expanded hours of operation, and should maintain high quality standards for basic ailments.  The Free exchange blog concludes:

“However, it seems the AMA would like to make sure its members profit no matter what choice you make. As it happens, nurse practitioners are required by law to practise under the supervision of a physician in 28 states — including in Illinois. I’ll swallow a stethoscope if the AMA didn’t have more than a little to do with the existence of those laws. “

Many papers have attempted to calculate hospital efficiency before and after a policy change. Most research, however, does not take into account quality levels when analyzing these changes in efficiency. For instance, a doctor may increase the number of patients per hour that they visit by 50%, but if this is accomplished simply by lowering the quality of each visit, there may not necessarily be a quality improvement.

Pablo Aroncena and Ariadna García-Prado (2007) look at efficiency measures in Costa Rica after the implementation of management contracts with the local public hospital administrators. Unlike most papers, however, they attempt to take into account how quality plays a role in efficiency metrics.

Costa Rican Health Care System

In Costa Rica, health care is primarily provided by the government. In 2001, 83% of providers worked in the public sector. Approximately 77% of health care expenditures are public compared to only 23% private. The Ministry of Health provides regulatory oversight of the health care system and the Caja Costarricense de Seguro Social (CCSS)–Costa Rican Social Security Institute is in charge of public health care service delivery and financing. Most all physicians in public health care system are salaried and receive civil service status. Physicians and hospital managers receive little or no merit-based pay and thus have little incentive to improve performance.

The 1990s brought much restructuring of the Costa Rican health care system. For example:

• 1994: a population-based model of primary care was created. Basic health care teams–Equipo Básico de Atención Integrada a la Salud (EBAIS) were established to decentralize health care services and offer primary care and preventative services to the entire population.
• 1998: Decentralization Law. This law gave managerial autonomy for public hospitals.
• 2000: Management agreements fully implemented. “These contracts specified targets that managers pledged to achieve in a given time frame and were mainly deemed to get quality improvements and a better utilization of hospital resources.” Spain introduced similar management contracts in the 1990s and while hospital efficiency did increase, the measures were ineffective in primary care centers.

Data and Methods

The authors use Costa Rican public hospital data between 1997 and 2001. The authors define a productive process as follows:

• P(x)={(y,b) : x can produce (x,b)}

In the paper, x are inputs, y are good outputs, and b are bad outputs. The vector x consists of measures of MD and RN labor hours, the number of beds as a proxy for capital, and real monetary expenditures on goods and services. The good outcomes y are the number of discharges and number of outpatient hospital services used, while the bad outcome, b, is given by hospital readmissions.

The authors claim that hospital readmissions is a good proxy for quality. This may not be true. A sicker patient base may have more hospital readmissions. The authors do try to control for health status using DRGs and also their study examines changes in readmission rates so the baseline case mix of a hospital is not as important. Similarly, if a hospital has poorer patients, they may be less likely to comply with the doctors directives (either due to financial issues or misunderstanding the physician). If the case mix remains unchanged, however, this will not bias the results. A final issue noted by Arocena and García-Prado is that managers may have misreported re-admission rates in order to receive favorable reviews on their performance contracts. This problem is mitigated somewhat by the fact that the CCSS conducted audits of each hospital facility.

The distance function employs a nonparametric data envelopment analysis approach. The function used by the authors is:

• D^t(x^t,y^t,b^t,α)=min {λ: (λ^1-αb, λ^-αy) ∈ P^t(x)}

Results

The authors find that hospital efficiency increased significantly after the management contracts only in small hospitals. In large hospitals, there was neither an increase or decrease in efficiency. The paper believes that increased physician compliance with clinical protocols and better record keeping drove the improved performance for small hospitals. Peer pressure mechanisms are less effective in large hospitals, and the finding that absenteeism rates increased in large hospitals after the management agreements may show one reason why efficiency improvements did not increase in large hospitals.

Further, the authors actually find an efficiency decrease when quality is not taken into account. While the management contracts may not have strictly increased throughput, they do seem to have increased quality in small hospitals and the quality gains outweigh the quantity losses, at least in the case of small hospitals.

• Aroncena P, García-Prado A (2007). “Accounting for quality in the measurement of hospital performance: Evidence from Costa Rica,” Health Economics. Volume 16, Issue 7, Pages 667 - 685.

Should hospitals with long waiting times have higher or lower budget transfers? Offering hospitals who have low wait times more money will increase a hospital’s incentives to decrease wait times. On the other hand, thus policy may hurt the busier hospitals and may not alleviate the wait times of those who are waiting the longest. In the case of public school transfers, if the best schools are rewarded, this encourages achievement, but may punish the worst off kids (i.e.: those at poorer schools). Transfers to low-performing school may mute incentives to increase achievement.

The issue of hospital payment structure is analyzed by Luigi Siciliani in his article on optimal contracts B.E. Journal of Economic Analysis & Policy. As with any thesis which claims to give an optimum solution, this optimum is based on some assumptions. This paper uses four major assumptions.

1. Demand for treatment can be controlled by dumping some patients. Doctors can tell patients who wish to have medical treatment that they either a) don’t really need it or b) that they will not provide it
2. The purchaser (i.e.: NHS, an insurance company, Medicare, etc.) can not observe the number of people dumped.
3. Dumping is costly for the specialist. By dumping patients, the specialist receives more complaints about their service level. Thus, either the physician’s reputation is tarnished (a cost) or the physician must spent more time (another cost) convincing the patient that they do not need treatment.
4. Hospitals differ in potential demand for treatment, either due to the catchment area of the hospital or from having a better or worse reputation.

Another key assumption is that no co-payment charges can be issued. This assumption is plausible, because it basically represents the British NHS system. Thus, the optimal solution must be seen not as the ideal optimal, but as the optimal with a centralized payer and no co-payments.

The Model

Hospitals have parameter θ which describes the public hospital type. This parameter θ indicates potential demand in the absence of a rationing system.

For each treatment, patients differ in the value they would receive from treatment. For instance, healthy patients would not benefit from heart surgery, but individuals with coronary artery blockages likely would benefit from surgery. Thus the author assumes that individuals’ value from treatment is uniformly distributed between v₀ and v₁.

Patients have three options:

1. They can be treated at a public sector hospital after a wait of time w, [u^p(v,p)]={∫^T₀ v dt} -p=vT-p]
2. They can be treated in a private sector hospital with no wait, but pay a price of p, [u^NHS(v,w)]={∫^T_w v*g(w) dt} =vg(w)(T-w)]
3. or they can receive no treatment[u^none=0]

There are two costs to going to the public hospital. First, the individual has to wait w weeks longer, so they do not get to enjoy the benefit of the treatment for as extended a period of time. Secondly, since 0<g(w)<1, the treatment becomes less effective or less valued the longer the patient waits.

Thus, from the math above, we can see that a person will choose a public hospital if and only if:

• v<V(w)=p/{T-g(w)*(T-w)

The comparative statics show that longer wait times decrease the probability of using a public hospital, higher prices, p, decrease the probability of using a private hospital, and higher valuations, v, increase the probability of using a private hospital.

Demand for public hospital services is written as:

• D(θ,w,x)=θV(w)-x
• x is the number of patients who are dumped (i.e.: not added to the waiting list)

The number of treatment supplied by hospital θ is y(θ) and since supply must equal demand, we have:

• θV(w)-x=y(θ)

The authors claim that providers receive disutility from dumping patients. Also, hospitals receive more disutility when they dump patients who value the treatment more (i.e.: high v, this is more likely to be the sicker patients). Thus, we are lead to our first major conclusion.

• Conclusion 1: The patients who are dumped are the ones with the lower benefits from treatment. This means that hospitals dump the patients who don’t really need the treatment.

After some more math, the Dr. Siciliani states a second conclusion:

• Conclusion 2: A mix of explicit rationing (through dumping) and implicit rationing (through waiting) is therefore optimal. Siciliani explains that: “Rationing by waiting alone induces excessive disutility for patients. Rationing by dumping alone generates excessive disutility for the specialists.”

The author continues to conclude that a separating or pooling equilibrium may occur.

“Under symmetric information, the optimal contract is for the purchaser simply to over a transfer in exchange for the provision of the desired level of activity and waiting time, without leaving any rent to the provider…Under asymmetric information, we found that a separating equilibrium exists when it is optimal for the purchaser of health services to contract more activity and higher waiting times to hospitals with higher demand. In this case providers with low potential demand have an incentive to mimic hospitals with high potential demand. To induce hospitals to self-select, the purchaser needs to pay a rent to hospitals with lower potential demand. [But] if it is not optimal for the purchaser to contract more activity and higher waiting time to hospitals with higher demand, then a separating equilibrium may not exist.”

Problems

One main problem with the paper is that it assumes that patients with a high value, v, cost the same to treat as low value patients. If v is a proxy for sickness, this is likely not to be the case; sicker patients with a high v are more expensive to treat. If this were the case, then conclusion 1 would not hold. Public hospitals would instead treat patients with the lowest benefit and dump patients with intermediate benefits–the high benefit patients would still go to the private sector hospital.

Also, the paper does not take into account any strategic interaction between hospitals. “If hospitals with higher potential demand are contracted higher waiting times, then patients will switch from the hospitals with high potential demand to hospitals with low potential demand, increasing excessively the amount of dumping and consequent disutility for hospitals with low potential demand.”

• Luigi Siciliani (2007) “Optimal Contracts for Health Services in the Presence of Waiting Times and Asymmetric Information,” The B.E. Journal of Economic Analysis & Policy: Vol. 7 : Iss. 1 (Contributions), Article 40.

Physician scorecards have been a highly touted means to improve healthcare quality. One example is NY state’s coronary artery bypass graft (CABG) Surgery Reporting System. One side effect of scorecards is that surgeons may choose to operate on healthier patients in order to maximize their scorecard grade. In fact, over 60 percent of NY cardiothoracic surgeons reported refusing to operate on high risk patients on at least one occasion (Burack et al. 1999).

A paper by Glance et al. (HSR 2007) investigates whether or not high-quality cardiac surgeons are less likely to operate on high-risk patients. The paper uses a data set with over 57,000 patients treated by 189 surgeons. The authors first estimate a regression as follows:

• log[p_i/(1-p_i)] = α + Σβ_kx_kij + Σλ_jP_j

The equation above estimates the predicted probability of the ith patient treated by the jth surgeon with risk factors x