Hospitals

There is an interesting article a few weeks back in the Wall Street Journal (”Opting Out“) which describes the plight of Amish and Old Order Mennonites who refuse to buy health insurance. Further, since these groups also refuse to participate in Medicaid government assistance will not bail them out either.

Nevertheless, these societies do have one form of insurance: mutual aid. When one member of the community becomes ill, the rest will pitch in to help finance the cost of the needed medical care. “Thousands of Amish families rely on the age-old system of churches paying bills members can’t afford, through voluntary donations.”

Because they are very closed societies, however, many Amish and Old Order Mennonite individuals marry distant cousins which can lead to a handful of genetic diseases. With such a high rate of expensive-to-treat diseases, this mutual aid system is faltering.

Further, since the Amish and Mennonite are uninsured, they actually pay more for medical care than would someone with private or public health insurance. This phenomenon was documented in my “Uncompensated Care” post.

What is the solution?

The Amish hope to persuade their local hospital to lower medical costs, but it is unlikely that a hospital will negotiate a lower rate for uninsured Amish compared to the uninsured non-Amish. The local Lancaster General hospital “…has increased its discount for uninsured patients to 25% from 15%…uninsured patients now receive the same discount that commercial insurers do, though not as much as the government does.”

The moral of the story is that it is very difficult to receive medical care in America today without health insurance.

• H.T.: Chronic Positivity
• H.T.: Lancaster Online “Faith not paying bills“

A Side note: If everyone receives at least a 25% discount, isn’t that just the regular price?

The N.Y. Times reports (”Concerned about costs…“) that Congress is trying to impose new restrictions on physician-owned, for-profit hospitals. The legislators fear that these hospitals 1) drive up costs and 2) provide poor quality.

Legislators worry that when physicians own the hospital, they may have more of an incentive to order more procedures to increase their profits. If this is true, I am not sure that physician owned hospitals are the problem, it may be the case that Medicare or insurance companies need to change how they compensate physicians in these hospitals.

The second case is also of dubious merit. It was shown in a previous post that ambulatory surgery centers and hospital outpatient departments have similar quality levels. It is true that ambulatory surgery centers generally have a healthier patient base, but treating healthier patients in a lower cost setting is not necessarily a bad thing. It is true that many of these physician-owned hospitals not equipped to handle complications requiring emergency care, but if the complications are lower, then the cost savings may be worth not having the emergency care equipment.

Of course, not all physician-owned hospitals will be subject to these new restrictions. Lobbyists have convinced politicians that facilities such as Aurora BayCare Medical Center in Green Bay, Wisconsin and Wenatchee Valley Medical Center in Wenatchee, Washington should be exempt from these restrictions.

Michael C. Burgess a Texas Republican and an obstetrician-gynecologist states that “This is a free country…If you want to invest in a hospital, if you are willing to put personal capital at risk, you should not be forbidden to do so just because you are a doctor.”

How much care should doctors give to terminally ill patients in the ICU? This is a question which can be answered on many levels (e.g., societal, individual, technical). One physician gives his thoughts in an n+1 magazine article titled “First, do no harm.”

While advanced medical technology has lead to greater longevity and healthier lives, scientific advances are not always easily applicable to the ICU.

Well-conducted studies of medical interventions test a medically homogeneous population by manipulating one aspect of their care. ICU patients are so sick, in such diverse and unique combinations of ways, that there is shockingly little sound information on whether or how much the interventions we can offer will help. And so a day seldom passes on rounds without us standing around scratching our heads about what to do next. Dialysis? Could work. Twelve-thousand-dollar-a-day drug that is “effective,” at least temporarily, in 10 percent of cases? Let’s give it a shot.

Many of these patients would actually prefer less invasive treatment. The most important features of medical care for patients with chronic diseases are: being kept clean, maintaining their dignity, trusting their physician, and being free of pain. “Only 48 percent of patients felt it was important to ‘use all available treatments, no matter what the chance of recovery.’”

If the relative is not able to voice their own opinion (e.g., if they are unconscious), a relative often decides how much care the patient receives. In order to avoid the guilt burden of giving up on their loved one, many relatives will ask for the most intensive treatment possible. Since insurance companies often bear the full costs of these decisions, relatives are not hesitant to order as much treatment as possible.

Physicians often preform unnecessary intensive procedures for fear of malpractice lawsuits or “fear of withholding care in one of the rare cases when last-ditch efforts grant a patient extra months or years of quality life.”

Thus, we are left with lots of doctors flogging patients. In the words of the author, flogging is “slang term we use for performing these procedures on people unlikely to benefit from them.”

Many studies have attempted to determine how the manner in which physicians are compensated by health insurance companies affects the quantity of medical care provided. Today I will summarize some seminal studies in this field.

Epstein, Begg and McNeil (NEJM 1986)

In this study, the authors examine whether or not there is a difference in the rate of ambulatory testing between physicians in a large fee-for-service (FFS) group and a large prepaid group. The physicians were internists who were caring for patients with uncomplicated hypertension. The authors found that 50% more electrocadriograms (EKGs) were obtained in the FFS group and 40% more chest radiographs were obtained in the FFS group [compared to the prepaid group]. There did not seem to be any difference in testing rates between FFS and prepaid doctors for blood counts and urinalyses. This is not surprising, however, because the profit physicians earned from EKGs and chest radiographs was significantly higher than the profit they made from each blood count or urinalysis. Further, the cost of preforming EKGs and chest radiographs was much higher than the cost to preform blood counts and urinalyses.

Thus, this study concludes that patient testing rates are different between FFS and prepaid doctors, but this effect is only observed for high cost and/or high margin ambulatory tests.

Newhouse and Marquis (JHR 1978)

Physicians may treat patients differently based on how the patient’s insurance company pays them. However, the patient base of most physicians includes a wide variety of health plans and thus it may be difficult to discriminate care levels by insurance type. The “norms hypothesis” predicts that physicians treat patients in accordance with the average or modal insurance coverage in a given metropolitan area. In other words, “the level of a community’s insurance coverage determines physician norms.” If this were true, it would imply that there would be significant variation in medical care quantities across geographic regions but very little variations by patient for each physician.

The authors use data from the RAND Health Insurance Study in Dayton, Ohio. The authors test whether or not the patients own insurance rate will affect hospital admissions, the length of a hospital stay or the number of physician office visits. The authors find no evidence that community-level coinsurance rate impact hospital admissions, while the patient’s own coinsurance rate significantly affects hospital admission. In the 1963 data, neither the community insurance variable nor individual insurance variable had any affect on the length of a hospital stay or the number of physician visits, however in the 1970 data, individual coinsurance rates had a large impact on the length of a hospital stay or the number of physician visits, while the community level coinsurance rate had no impact on these dependent variables.

Hellerstein (RAND J Econ 1998)

Most health policy wonks believe we could significantly reduce health care costs without sacrificing quality by using more generic drugs. Many states have passed “permissive substitution laws” which allow pharmacists to substitute generic drugs in place of name-brand ones unless the physician explicitly instructs them not to do so. Other states use a “two-line” prescription method. With these prescription pads, doctors can sign their name in one of two spots: the first indicates that generic substitution is allowed and the other indicates that the brand name option is medically necessary.

Hellerstein uses data from the 1989 NAMCS, and finds that “30% of the unobserved (residual) variance in the prescription choice is physician-specific, rather than patient specific.” Does an individual’s health insurance influence the physician’s prescribing behavior? This paper finds that an individual’s health insurance does not influence prescribing decisions. However, “conditional on a patient’s insurance status, a patient who switches to a physician with a marginally greater fraction of HMO patients is 10.12% more likely to receive a generically written prescription.”

Summary

Why does the composition of the physician’s patient base seem to matter for Hellerstein (1998), but not for Marquis and Newhouse (1978) or Epstein, Begg and McNeil (NEJM 1986)? The main reason is likely that Hellerstein examines medical care in which physician do not receive any compensation. Physicians receive no more or less revenue from prescribing name brand compared to generic drugs and thus have no incentive to find out how they are being paid by each individual’s insurance company. On the other hand, Marquis and Newhouse found that hospital admissions, the length of the hospital stay, and the number of office visits is affected by an indvidiual’s health insurance variables since this type of medical care is high margin and high cost. Similarly, Epstein, Begg and McNeil (1986) found that how an individual’s insurance company compensates physicians matters for high margin, high cost EKGs and chest radiographs, but does not seem to matter for low margin, low cost blood counts and urinalyses.

• Epstein AM, Begg CB, McNeil BJ. (1986) “The use of ambulatory testing in prepaid and fee-for-service group practices: Relation to perceived profitability.” N Engl J Med. 314(17):1089-94.
• Newhouse JP, Marquis MS. (1978) “The norms hypothesis and the demand for medical care.” J Hum Resour. 13 Suppl:159-82.
• Judith K. Hellerstein (1998) “The Importance of the Physician in the Generic versus Trade-Name Prescription Decision,” The RAND Journal of Economics, Vol. 29, No. 1, pp. 108-136 .

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?

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.

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).

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.

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 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.

Why do nonprofit hospitals exist? If they act exactly as for-profit hospitals, then they should be under private ownership. If they act according to some other maximization strategy, what is it?

These are the questions that Jill Horwitz and Austin Nichol look to answer in their 2007 NBER working paper. First, let us examine the composition of hospital ownership between 1988 and 2005.

We see that there is a slow change towards more for-profit (FP) ownership, and less government ownership. We can also look at the hospital figures weighted by admissions.

Here, we see a similar increase in FP ownership and a decrease in government ownership. Nevertheless, non-profit (NP) hospitals, still serve the bulk of patients in the United States.

Theories of Non-Profit ownership

1. Output Maximization. This theory was developed by Newhouse (AER 1970) and claims that non-profit hospitals offer more health care until profits are driven to zero. But the non-profit’s choice of how to maximize output is affected by neighboring hospitals. “If their neighbors are driven more by profit motives, then the nonprofit will tend to treat less profitable patients who seek less profitable types of care. In this case, the nonprofit’s behavior will be affected through the binding constraint on profits—in the absence of the profit-seeking competitors ‘cream-skimming’ patients, they would have offered a mix of services (and served a mix of patients), call it X, that generated zero profit, but in the presence of the profit-seekers, the mix X will lose money, so they must alter their behavior to generate additional profits. Thus a nonprofit will be induced to look more like a profit-seeker in an environment where there are more profit-seekers, by both being less likely to offer unprofitable services and more likely to offer profitable ones.”
2. Market Output Maximization. This theory was developed by Weisbrod (1988) and claims that non-profits seek to maximize the total medical service output for the entire community.
3. For-profits in disguise. “Pauly and Redisch ([AER] 1973) develop a formal model in which physician employees capture nonprofit hospitals, operating them to benefit physician cartels by maximizing doctors’ incomes.”
4. Mixed Objective.

Results

Horwitz and Nichol use AHA Annual Survey of Hospitals data between 1988 and 2005. The authors conclude that the output maximization theory–the Newhouse model–is supported by the data. The evidence includes the following:

• Non-profit hospitals in markets with a high concentration of for-profit hospitals are more likely to offer profitable services (e.g.: MRI) than those in low for-profit concentration markets.
• Non-profit hospitals are less likely to provide unprofitable services (e.g.: HIV/AIDS treatment) in high for-profit penetration markets than in other markets.
• “Perhaps the most convincing evidence for the effect of market mix is the results for home health and skilled nursing, post-acute services that were first ambiguously profitable, then profitable, then less profitable again. During the most profitable period, nonprofits were more likely to offer them in high, compared to low for-profit markets. During less profitable periods, depending on the specification, there was either no discernable difference or more dramatic exit among nonprofits in for-profit markets.”

References

• Horwitz, J.R.; Nichols A.; (2007) “What Do Nonprofits Maximize? Nonprofit Hospital Service Provision and Market Ownership Mix” NBER Working Paper 13246.
• Newhouse, J. (1970) “Toward a Theory of Nonprofit Institutions: An Economic Model of a Hospital,” The American Economic Review, Vol. 60, No. 1., pp. 64-74.
• Pauly, M.; Redisch M.; (1973) “The Not-For-Profit Hospital as a Physicians’ Cooperative,” The American Economic Review, Vol. 63, No. 1., pp. 87-99.

The popularity of specialty medical facilities (SMF) has increased over the years. The number of Medicare-certified ambulatory surgery centers (ASCs) has doubled to 3,371 during the past decade. A question remains: are these “Focused Factories” good for society?

In an article by Casalino, Devers and Brewster, (”Focused Factories…“) the authors try to answer this question.

What are SMFs?

At this point there are 2 types of SMFs: specialty hospitals and ambulatory surgical centers (ASCs).

• Specialty hospitals - According to the Community Tracking Study, most specialty hospitals are either “heart hospitals” or hospitals specializing in orthopedic surgery. They are generally joint ventures between physicians and national specialty firms or local hospitals, but a few are wholly owned by either physicians or by local hospitals.
• ASCs - Most ASCs are small and have four or fewer operating rooms. The most common services provided at ASCs are ophthalmology and gastroenterology.

Are they specialized medical facilities (SMFs) good for society?

Physicians who work at or own the SMFs claim that this type of care increases quality and reduces cost. Proponents of SMFs claim productivity increases due to specialization and the fact that there is less down time between procedures. Some hospitals say that these facilities engage in competition that is ‘unfair’, but if lowering cost and increasing quality is ‘unfair’ competition, mark me down as a fan of the unfair. The hospitals also argue that the SMFs are formed around the most profitable services and thus the hospital can not subsidize money-losing departments (e.g.: ERs, burn units, trauma centers). If the hospital is losing money on certain procedures, this does not mean that they should be making excess profits on other procedures, only that health plan compensation schedules should be altered.

The hospitals, however, are not all in the wrong. The SMFs have been accused of ‘cherry picking’ healthy patients. For instance, if physicians are reimbursed $5000 for a surgical procedure, the cost of preforming the surgery may be $2000 for a (relatively) healthier patient and $4000 for a relatively sicker patient due to increased likelihood of complications during surgery for the sicker patient. Thus, the hospital is shouldered with caring for the sicker patients and it may be more difficult for them to turn a profit. This solution to this is of course to make surgery payment in a risk-adjusted manner. In reality, risk adjustment is a delicate process which depends on many unobserved health variables so this solution may not be as easy to implement as it would seem in theory.

Another issue is whether SMFs create incentives for excess medical care. This is related to the problem of integrating the diagnostician of a problem and the treater of a problem (see 10 April 2007 post). If physicians own the SMFs, there may be an even larger incentive for them to recommend that their patients have invasive medical procedures since the physicians themselves often will profit not only from the labor compensation they will receive from preforming the procedure, but will receive additional income as return on capital from their investment in the SMF. Even physicians who do not treat patients and only diagnose them will have an incentive to recommend surgeries if they own a share of the SMF where the surgery would be preformed.

To counteract this problem, some politicians are considering bills which would “prohibit physicians from referring Medicare
and Medicaid patients to specialty hospitals in which the physicians have an investment.” While this is certainly a problem, the authors wise note that the negative aspect of these types of laws “…is that it would cause society to lose any advantages that might come from physician ownership and management of such facilities.”

So are SMFs good for society? At this stage it is difficult to say with certainty whether they are or not. Further investigation on this topic is certainly merited in the future. Any comments on your opinions regarding specialized medical facilities would be greatly appreciated.

• Casalino; Devers; Brewster (2003) “Focused Factories? Physician-Owned Specialty Facilities” Health Affairs, 22, no. 6, pp. 56-67.

A frequent topic of investigation in health economics is to estimate the elasticity of the demand for medical procedures with respect to wait times.  A paper by Martin, Rice, Jacobs and Smith in the Journal of Health Economics uses quarterly data from 200 English hospitals between 1995-2002 in order to separately estimate the supply and demand curves for elective surgery.  On the demand side, patients care about the cost of the surgery as well as the quality of the doctor.  Quality is comes in two forms: clinical quality (i.e.: health-related outcome measures such as QALYs) and responsiveness (i.e.: notions of patient autonomy and convenience).  Clinical quality is measure by the mortality rate of each surgery, the staff sickness rate and the staff surgery rate.  Responsiveness is measured by patient wait times.  On the supply side, hospital managers care about the volume of care and quality of care as well as managerial effort. One issue to deal with is that British patients can elect to have either NHS or private treatment, but if NHS treatment is chosen then provider choice is usually limited.   Price is approximately the same for each person; the only difference in terms of cost is implicit cost of the accessibility of private facilities.  Thus, the authors include a term for ease of access to private acute hospital beds.  More private hospital bed availability should decrease demand for NHS services.
The major problem with estimating supply and demand separately is that the residuals of the two regressions could be correlated.  Since the demand and supply equations are overidentified (i.e.: there are explanatory variables in the demand equations which do not appear in the supply equation, and vice versa), the authors can use a seemingly unrelated regressions (SUR) framework.  Also, because of the time series nature of the data, a lagged dependent variable is also employed in some specifications.

Results

As predicted the authors find that longer wait times decrease demand.  The estimated elasticities are similar to figures found in  earlier studies [Martin and Smith 1999 (-.021), Gravelle et al. 2003 (-0.21), and Dusheiko et al. 2005 (-0.10)].   The SUR estimates give a lower elasticity of demand than those obtained with OLS.  A more surprising result is that longer wait times increase supply in terms of the number of admissions from the waiting list and the number of outpatient referrals seen.  The authors found the elasticity of supply with respect to wait time was small however 0.05 to 0.10, whereas Martin and Smith (1999) found an elasticity of supply of 2.93 and a later paper [Martin and Smith (2003)] found an estimated elasticity of supply of 5.29.

• Martin; Rice; Jacobs; Smith (2007) “The market for elective surgery: Joint estimation of supply and demand” JHE, vol 26, issue 2, pp. 263-285.

Approximately one year ago today, I wrote about whether or not non-profit hospitals should be tax exempt (”Should Non-Profit Hospitals get a Tax Break?“). Generally, I concluded that they should not.

Flash forward to March 2007 and we see that The New York Times has an article titled “In Shriner Spending, A Blurry Line of Giving.” John Goline was struck by polio as a child, but can walk today due to treatment in a Shriners hospital. Mr. Goline was a huge supporter of the Shriner cause. This article continues:

“But [Mr. Goline's] faith was shaken when he joined the leadership of the Suez Shriners in San Angelo, one of 191 temples affiliated with the order. He found that much of the money collected to support the hospitals was commingled with money used for liquor, parties and members’ travel to Shrine events. The Shrine’s national auditor largely confirmed his findings, but not before Mr. Goline was forced out of office.”

Other problems in the Shriner organization include:

• “More than 57 percent of the $32 million the Shriners raised in 2005 through circuses, bingo games, raffles and a variety of sales went to costs of the fraternity, including keeping temple liquor cabinets full and offering expenses-paid trips to Shrine meetings and other events.”
• “A top Shrine official told a meeting of temple treasurers that poor accounting for cash coming into the organization was ‘an increasingly common problem,’ and that more than 30 temples had discovered fraud — like theft of money and inventory, altered bank statements, padded payrolls and fake invoices — amounting to as much as $300,000 and involving members of their ‘divans,’ the five-member boards that govern each temple.”

Most economists believe preferences are monotonic. This means that economic researchers believe the more of something you have (e.g.: money, burritos, cars, friends, etc.) the more well-off you become. This assumption likely holds if we view health as an argument in a person’s utility function; more health generally makes people better off. Putting ‘medical services’ into a person’s utility function does not work in a similar manner. Many medical services used in excess can actually do harm to an individual.

The monotonicity of utility in medical care would not be a problem if there were few injuries resulting from medical care; in fact, most people believe that physicians are highly-skilled individuals (which they are) and that the chance of an iatrogenic injury are extremely small. An article in the Milwaukee Journal Sentinel (”A dose of prevention“) contradicts this assumption by finding that “medical errors were killing between 44,000 and 98,000 people a year in U.S. hospitals, enough to rank among the top 10 causes of death in the U.S., in roughly the same league as diabetes and Alzheimer’s disease.”

Dr. Donald Berwick is a pediatrician and safety advocate. As CEO of the NGO Institute for Healthcare Improvement, Dr. Berwick is trying to reduce the amount of iatrogenic injuries with his 100,000 lives campaign. The campaign aims to eliminate unnecessary errors in the health care sector. Some of the IHI’s recommendations include:

1. Preventing patients who receive medications and fluids through a central line from developing infections. The steps for stopping these infections include: proper hand washing, selecting the best site for the central line and cleaning the patient’s skin with an antiseptic called chlorhexidine.
2. Taking steps known to reduce the risk of heart attacks, including giving patients aspirin and beta-blockers to prevent further damage to heart muscle.
3. Avoiding drug errors by verifying the patient’s medication history and reviewing and updating medication lists, especially when patients move to different units or get released.
4. Preventing patients on ventilators from getting pneumonia, through several steps, including raising their heads to between 30 and 45 degrees to prevent a buildup of fluids, and giving breaks in sedation that help determine the earliest point at which the ventilator can be removed.
5. Dispatching rapid response teams to treat patients before a decline in condition becomes a full-blown crisis.
6. Preventing surgical patients from developing infections through several steps, including timely use of antibiotics and appropriate hair removal.

Froedtert Hospital in Milwaukee instituted many of these changes and saw its mortality rate drop from 23.4 deaths/1000 discharges to 19.5 deaths/1000 discharges.

As an aside, measuring deaths per discharge is wise way to proxy for mortality rates from medical errors. If only pure mortality rates were measured, the hospital staff would have an incentive to send very sick patients to other hospitals to decrease the mortality rate at their hospital.

It is often very difficult to determine price data for various medical procedures. With the exception of Medicare or Medicaid data, hospitals often do not publish their prices for each service and even when the prices are provided they rarely correspond to the true prices paid. Published prices often do not take into account different discounts negotiated by each insurance company with the hospital. How can we determine the returns to performing a surgical operation without any price data?

Chernew, Gowrisankaram and Fendrick (2001) attempt to solve this problem. They model hospital entry behavior into the coronary artery bypass graft (CABG) market as a function of the predicted patients flow to the hospital after entry. For instance if a hospital in a poor neighborhood would enter the CABG market, they may attract only the uninsured, Medicaid, and Medicare patients. If these types of insurance compensate doctors at a lower rate than FFS insurance, entry would likely be unprofitable for the hospital. On the other hand, a hospital in a wealthy suburb may attract FFS patients whose insurance would pay physicians at a higher rate than Medicaid or Medicare. Entry would be profitable in this case.

Estimation - Predicted Volume

To estimate the predicted volume at a hospital, the authors use a conditional choice model based on McFadden (J Pub E 1974). The latent utility function for individual ‘i’ choosing hospital ‘j’ is:

• U_{ij}=X_{i,j}β_i + f(D_ij)λ_i + ε_{ij}
• ε_{ij} ~ Type I extreme value

Here, ‘X‘ are a vector of covariates and ‘D_ij‘ is the distance from the individual’s home zip code to hospital ‘j‘. Using McFadden’s derivation (see also Wooldrige pp. 500-504), we find the probability that person ‘i’ is treated at hospital ‘j’ is:

P(Y_{ij}=1) = exp(X_{ij}β_i + f(D_ij)λ_i) / [Σ_k exp(X_{ik}β_i + f(D_ik)λ_i)]

The predicted volume for hospital ‘j’ for payer type ‘h’ is simply the sum of P(Y_{ij}=1) over all individuals ‘i’ who have payer type ‘h’. The estimation of Q_h,j must be performed for each type of insurance payers.

• Q_h,j= Σ_i P(Y_{ij}=1)

Estimation - Entry Decision

Now that the predicted volumes are estimated, the authors must try to predict whether or not a hospital will enter the CABJ market. A hospital will enter if the total net revenue will exceed profits. In other words, the hospital will enter if and only if:

• Σ_h [Q_{h,j}*(P_{h} - AVC_{h}) ] + K_j > 0
• K_j=Z_j*γ + ν_j
• Σ_h [Q_{h,j}*R_{h}] + Z_j*γ + ν_j > 0

Here, ‘Q_{h,j}’ is calculated from the regression above, ‘R_h‘ is the average net revenue for patient with insurance type h, and the fixed cost ‘K_j’ is made up an observable (’Z') and unobservable (’ν_j’) component. If ν_j ~ iid N(0,1), the we can use a standard probit to estimate the probability of entry.

• P(entry_j|no CABJ at t-1)=Φ[Σ_h Q_{h,j}*R_{h}] + Z_j*γ]

Entry will be determined by the predicted volume of patients by payer type. The authors also test the robustness of their specification by later allowing serial correlation and then instituting a cox hazard model into their formulation.

Results

The paper uses data from the California Office of Statewide Health Planning and Development (OSHPD) which has over 300,000 hospital discharges between 1984 and 1994. The authors find that when a hospital will attract many FFS patients, it is approximately 20% more likely that the institution will enter the CABJ market. On the other hand, when a hospital attracts many Medicaid patients it is approximately 60% less likely to enter the CABJ market.

This paper’s main contribution is to create a novel estimation method for the estimation of price on hospital entry without needing explicit price data.

Chernew; Gowrisankaran, Fendrick (2002) “Payer type and the returns to bypass surgery: evidence from hospital entry behavior” Journal of Health Economics, vol 21, pp. 451-474.

McFadden, D.L. (1974) “Conditional Logit Analysis of Qualitative Choice Analysis” Frontiers in Econometrics, ed. P. Zarembka. New York: Academic Press, pp. 105-142.

The University of California San Diego (UCSD) is where I currently attend graduate school.  The UCSD Medical Center has been in some hot water lately.

• The Office of the Inspector General (OIG) of the Department of Health and Human Services has alleged that UCSD has overcharged Medicare $48 million for pension costs.  According to a UCSD Guardian report (”Med Center…“), “…the OIG reported that the medical center overstated pension wage costs by $22 million, and overstated postretirement wage costs by $25 million. The newly-issued corrections propose a 19-percent decrease from the UC-released hourly wage averages, from $41.74 to $33.75.“
• UCSD has two Medical Centers.  The first is on the UCSD’s main campus in the affluent neighborhood of La Jolla.  The second center is situated in an the urban, central city Hillcrest area (only a few blocks from where I live).  The University has decided to begin closing down operations in Hillcrest while expanding service offerings at their La Jolla location.  According to the UCSD Guardian (”Study says…“) the plan may overburden some of the city’s hospitals located in poorer areas.  ” ‘We fully expect that once the UCSD Hillcrest Medical Center is gone as a full-service hospital, other hospitals in the area will see a gigantic influx in patients,’ said Don Stanziano, a spokesman for Hillcrest’s Scripps Mercy Hospital [another San Diego area hospital]. ‘We don’t have the capacity to be the only hospital in the area. To us, it’s a hostile closure.’ “  The University responds to this criticism by citing the fact that the Hillcrest hospital has “aging” facilities and the La Jolla location is more “cost-effective.”