Specialists

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.

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_kij. The mortality of each patient if they are treated by the average surgeon is

• log[p_n/(1-p_n)] = α + Σβ_kx_ki

To determine the surgeon’s quality the authors used the observed to expected mortality rate.

Results

The paper finds that high quality surgeons actually treat higher risk patients. This finding is reassuring for those who favor medical scorecards. The authors due note some issues with the paper. For instance, one must be sure that the risk adjustment calculation is correct, and high-quality surgeons may be miscoding patients more frequently as high-risk. Another explanation could be that high quality surgeons have more experience and are closer to the end of their careers so they care less about scorecards and more about informal reputations. However, as young surgeons age and have been conditioned to believe that scorecards matter, this finding that high quality surgeons treat high risk patients may not hold in the long run. Nevertheless, the study is straightforward, clear and assuages some fear of patient selection by doctors operating under a scorecard system.

• Glance LG, Dick A, Mukame DB, Li Y, Osler TM. (2007) “Are High-Quality Cardiac Surgeons Less Likely to Operate on High-Risk Patients Compared to Low-Quality Surgeons? Evidence from New York State.” Health Services Research (OnlineEarly Articles). doi:10.1111/j.1475-6773.2007.00753.x

In a recent NBER working paper (”Tradeoffs“), authors Christopher Afendulis and Daniel Kessler, pose an interesting question: should the physician who is diagnosing you also be the one who provides treatment? On the one hand, a physician who both diagnoses and provides treatment has a financial incentive to recommend to the patient that they should have more aggressive (i.e.: read ‘expensive’) treatment. Similarly, car mechanics both diagnose the car’s problem and sell the appropriate remedies (i.e.: parts and labor). For this reason, car mechanics also are often stereotyped as recommending services you don’t need.

On the other hand, having the same physician (or mechanic) provide diagnosis and the treatment, may create efficiency gains. For example “the diagnostician may
have better information about how to treat the problem than he could (or would) provide
to an independent third party. Or, the diagnostician may be able to treat the problem
himself less expensively or more effectively (’half the cost is opening the engine block’).”

Data

To test how the diagnosis/treatment dynamic works in health care, Afendulis and Kessler use 1998-2000 data on Medicare patients with coronary artery disease. They compare type of treatment, spending and outcome measures when the patients were treated by the following 3 types of doctors:

• ‘Non-integrated’ Cardiologists - these physicians only diagnose the disease and provide non-surgical treatment (e.g.: pharmaceuticals). They do not conduct angioplasties.
• ‘Integrated’ Cardiologist - cardiologists who both diagnose and are able to preform surgical procedures (e.g.: angioplasties).
• Cardiac surgeons - unlike cardiologists, cardiac surgeons do not provide catheterization services for diagnostic purposes. These physicians preform the more complex heart surgeries (e.g.: bypass surgery).

Econometrics

The authors wish to identify how the type of physician impacts treatment types. The treatment types are angioplasty, bypass surgery, and drugs/non-surgical care. The authors use a multinomial logit framework but worry about selection bias. Selection bias would occur if there were “…unobserved differences in the health or preferences of patients treated by an interventional versus a non-interventional cardiologist.” To control for this, the authors employ predicted values of both physician type and hospital characteristics based on a patient’s three-digit-zip-code average rather than the patient’s actual choice of provider and hospital. This should increase the accuracy of the estimates, but the precision likely suffers.

Results

The authors conclude that treatment by an integrated cardiologists (rather than a non-integrated cardiologist) increases spending by $2800 per patient, but leads to no statistically significant change in health outcomes. However this is not the entire story. Some patients who would have not received surgery now receive angioplasties. On the other hand, some patients who would have been referred by the non-interventional to the surgeon for the more invasive, expensive bypass surgery, now have the less expensive, less invasive angioplasty preformed by the interventional cardiologist.

Looking at efficiency measures, diagnosis by an interventional cardiologist leads to higher spending on angioplasty patients with no health improvement; diagnosis by an interventional cardiologist leads to higher spending on bypass patients as well, but mortality drops significantly. Non surgical patients treated by the interventional cardiologist have similar spending levels, but higher mortality.

Conclusion

After analyzing the data, the authors make an interesting point regarding ‘kickback’ payments for referrals.

“Explicit ‘kickback’ payments from treating to diagnosing doctors are banned by law (for public purchasers such as Medicare and Medicaid) and by contract (for private purchasers like insurance companies and large employers). However, the principle underlying this ban is not generally applied to doctors’ decision to provide integrated diagnosis and treatment, even though integration can have the same effects on incentives and behavior as kickbacks do. In addition, allowing integration but banning kickbacks effectively allows rent capture by integrated but not non-integrated doctors, which can distort treatment decisions even further.”

• Afendulis, Christopher; Kessler, Daniel (2006) “Tradeoffs from Integrating Diagnosis and Treatment in Markets for Health Care” NBER Working Paper 12623.

Today I will be briefly be reviewing four recent articles which examine how physician payment methods affect the amount of medical service provision.

GP reimbursement in Ireland

Madden, Nolan and Nolan (Health Econ 2005) use a quasi-experimental framework to see how changing physician compensation affects the number of doctors visits. In Ireland approximately 30% of the population receives free general practitioner services (”medical card services”) while the remainder (”private patients”) must pay for their medical services. In 1989, the Irish government changed the manner in which physicians were compensated for ‘medical card’ patients from FFS to capitation. The authors take this policy change to be exogenous as use the private patients as a baseline group to control for any time trends. The authors use the a pooled cross sectional framework with data points in 1987, 1995 and 2000, to preform a difference-in-difference estimation. Unfortunately the 1987 and the 1995/2000 data points come from two different surveys which could bias their results. Econometrically, the authors first model whether a patient will visit a doctor at all using a logit regression and then among those who visit a doctor, a negative binomial framework is used to estimate how financial compensation affects the number of doctor’s visits. The authors’ results show that the capitation payment method reduced the number of doctors visits for each patient.

Physician Reimbursement and Cataract Surgery

This study by Shrank, et al. (2005) looks at how surgery rates change when an eye care network of ophthalmologists and optometrists in St. Louis changed from a FFS compensation method to a capitation method. The managed care organization with whom the network maintained a large contract changed their compensation method from FFS to capitation between 1997 and 1998. The authors find that cataract surgery rates dropped significantly after the change, but non-cataract surgery rates did not change much. In the authors’ words “The finding that cataract surgery was more responsive to reimbursement methodology than other procedures supports the hypothesis that elective procedures are more responsive to physician incentives than nonelective procedures.”

Insurance Type and Orthopaedic Surgery Rates

This study by Brinker, et al. (2006) looks at clinical and financial data between 1999 and 2004 for a group practice of 40 orthopaedic surgeons. The authors examined surgery rates for different insurance types: capitation HMO, HMO, PPO, indemnity, self-pay, worker’s comp, Medicaid, and Medicare. The results show that there is little difference in surgery rates between the insurance types. One, however, may worry about selection problems: it is possible that those with capitation payments who ended up going to the orthopaedic surgeon would be sicker than those patients who went the surgeon and had a FFS insurance. Also, the authors’ estimation strategy conditions on diagnosis, but if insurance reimbursement is based on the diagnosis given by the physician, the diagnosis could be an endogenous variable. The authors also acknowledge that they have no knowledge of the severity of the illness, just the patient’s primary diagnosis. Nevertheless, this paper suggests that financial incentives may not be as important a factor in the case of specialist care as primary care.

Payment Procedures in HMOs and variation in Specialty Services

Saver and his colleagues look at three large HMOs in the Midwest and the West between 1996-1998 who compensate physicians via FFS, salary and capitation depending on the physician or practice group. To avoid problems of adverse selection, the authors look at compensation variation within each HMO to determine if/how payment mechanisms drive specialist procedure rates. The procedures examined included the specialties of Cardiology, Cardiothoracic surgery, Ear Nose & Throat (ENT), Gastroenterology, Ophthalmology, and Orthopedics. General results found that FFS had higher odds ratios than salaried and capitation compensation, but capitation and salaried compensation had similar odds ratios.

We can see that FFS never leads to fewer procedures being preformed (compared to capitation and salary) at least in any statistically significant way. One problem with the results is that compensation decisions by insurers are often based on geographic market considerations, so regional differences may be biasing some of the results.

• Madden; Nolan; Nolan; (2005) “GP reimbursement and visiting behaviour in Ireland” Health Econ vol 14, pp. 1047-1060.
• Shrank; Ettner; Slavin; Kaplan; (2005) “Effect of physician reimbursement methodology on the rate and cost of cataract surgery” Arch Ophthalmol, vol 123, pp. 1733-1738.
• Brinker; O’Connor; Pierce; Spears (2006) “Payer type has little effect on operative rate and surgeons’ work intensity” Clinical Orthopaedics and Related Research, nbr. 451, pp. 257-262.

If my October 10th post did not satiate your desire for knowledge regarding referrals, today I give you even more information. 

• Franks, Zwanziger, Mooney and Sorbero examine a large Rochester, NY Independent Practitioner Association (IPA).  The authors find a mean patient referred/patients seen/year of 0.37.  The data show that referral rates remain very stable by physician over time, likely due to physician stable case mix.  Franks et al. conclude that referrals are driven by physician recommendations, not patient demand.
• Shea, et al. look at data from the 1992-1993 Medicare Current Beneficiary Survey (MCBS).  They find that only 36% of referrals are from primary care physicians to specialists.  Primary care to primary care and specialist to specialist referrals account for 45% of referrals and specialist to primary care referrals make up 4%.  The balance is made up from within-specialty referrals.  As opposed to Franks et al., the authors conclude that patient demand (not supplier recommendations) are the major driver of referrals. 

Franks, Peter; Zwanziger, Jack; Mooney, Cathleen; Sorbero, Melony (1999), “Variations in primary care physician referral rates,” Health Services Research, vol 34(1), pp. 323-329.

Shea, Dennis; Stuart, Bruce; Vasey, Joseph; Nag, Soma (1999),”Medicare physician referral patterns,” Health Services Research, vol 34(1), pp. 331-348.

Years ago, when someone needed care from a doctor they visited the physician directly whether they were a general practitioner or a specialist.  Nowadays, it is rarer for patients to visit a specialist without a referral.  The typical referral comes from a primary care physician, but it is also common for a specialist to refer the patient to another specialist (cross-referral).  Even when a patient sees a specialist without consulting another physician, this is now called a self-referral. 

Forrest and Reid (1997) use 1989 to 1995 data from the National Ambulatory Medical Care Survey to give more detail on the nature of referrals.  The authors compare the amount of referrals which occur inside and outside of managed care.  One might think that referrals are less common in managed care since these organizations are known to restrict the supply of specialist services; on the other hand, managed care organizations often require more referrals (fewer self-referrals are allowed) so it is possible that the number of referrals is higher in managed care as well. 

The authors found that primary care patient visits in the HMO setting were 66% more likely to lead to referrals than such visits under an indemnity plans.  Patients in HMOs were less likely to be able obtain self-referrals, however.  Thirty one percent of specialists’ managed care patients were self referred compared to 49.5% of their patients in indemnity plans.  Cross referrals between specialists occurred at similar rates in the managed care and fee for service settings. 

Economists have focused on primary care physicians’ financial incentives to refer patients to specialists.  Stephen Shortell (1973) claims that a social exchange model may more accurately reflect how referrals are performed today.  Mr. Shortell’s article in the Journal of Health and Social Behavior claims that non-financial incentives largely influence to which specialist a patient is referred.  Shortell hypothesizes that 1) a specialist’s status in the field, 2) their friendship level with the primary care physician, 3) their office’s distance from the primary care physician’s office, and 4) whether or not they are on the same network as the primary care physician likely influence the primary care physician’s decision-making process. 

 Forrest, Christopher; and Reid, Robert; (1997) “Passing the baton: HMOs’ Influence on Referrals to Specialty Care,” Health Affairs, vol 16(6), pp. 157-162.

Shortell, Stephen M.; (1973) “Patterns of Referral Among Internists in Private Practice: A Social Exchange Model,” Journal of Health and Social Behavior, vol 14(4), pp. 335-348.