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|Study Goal||Results||Data||Type of Study||Reference|
Topic: Effect of Earnings on the Supply of Primary Care Services and Providers: Role of Medicare Primary Care Bonuses (Section III)
|Estimate a model where medical students consider entry probabilities when selecting a specialty.||The income elasticity estimates range from 1.03 in family practice/pediatrics to 2.20 in radiology.||Data set with the preferred and realized specialties for 7,200 medical students.||Econometric model (conditional logit)||Nicholson, 2002|
|Estimated career wealth accumulation across specialists, primary care physicians, physician assistants, business school graduates, and college graduates. Comparing specialists, represented by cardiologists, to primary care physicians in scenarios.||The present value of career wealth from college graduation through age sixty-five was estimated at $5,171,407 for cardiologists, $2,475,838 for primary care physicians, $1,725,171 for MBA graduates, $846,735 for physician assistants, and $340,628 for college graduates. For a primary care physician’s lifetime earnings to equal that of a cardiologist the primary care physician would have to receive a bonus of $1.1 million upon completion of medical school.||Data from multiple sources included income, income taxes, living expenses, and graduate school expenses.||Compound interest wealth accumulation model||Vaughn et al., 2010|
|Determine effect of educational indebtedness on the specialty choices of new physicians, especially in light of the perceived shortage of primary care physicians.||Medical students are more likely to choose primary care when the expected earnings are relatively large. A $10,000 (20 percent of the mean earnings) increase in expected earnings in primary care relative to the non-primary care yields a 1.4 percentage point increase in the probability of choosing primary care.||Data set from American Medical Association’s 1983 ‘Survey of Resident Physicians’.||Econometric model (utility maximization)||Bazzoli, 1985|
|Estimation of career choice and medical student’s decision to specialize given lifetime utility maximization.||In response to 9.1 percent reduction in relative fee-per-consultation for a general practitioner would lead to between a 0.4 percent reduction and a 2.29 percent reduction in the proportion of Medical students entering general practice.||Data from multiple sources. Model estimated from sample of 30,184 physicians who practiced in Canada between 1989 and 1998 and whose year of graduation from medical school is between 1975 and 1991.||Econometric model (multinomial logit)||Gagne and Leger, 2005|
|Determine the causes of the primary care-specialist income gap; why the Resource-Based Relative Value Scale failed to prevent the income gap.||The RBSVS failed due to four factors: (1) the volume of diagnostic and imaging procedures has increased more rapidly than the volume of office visits; (2) the process of updating fees every five years is heavily influenced by the Relative Value Scale Update Committee, which is composed of a majority of specialists; (3) Medicare’s formula for controlling physician payments penalizes primary care physicians; and (4) private insurers tend to pay for procedures at higher rates than office visits relative to Medicare.||Data from multiple sources, income information between 1995 and 2005.||Literature review||Bodenheimer, 2007|
|Determine the effects of expected future earnings and other attributes on specialty choice.||Simulated policy changes indicate increasing general practitioners earnings by $50,000, or increasing opportunities for procedural or academic work (specifically limiting the amount of on-call hours) can increase the number of junior doctors choosing general practice by between 8 and 13 percentage points. This implies an earnings elasticity of specialty choice of 0.95.||Data from the Medicine in Australia: Balancing Employment and Life (MABEL) survey.||Econometric model (logit)||Sivey & Scott, 2012|
|Estimate a model of physician labor supply, focusing on the impacts of wage and non-wage income.||Evidence of significant income effects. For male physicians, the income effect of a wage change on labor supply is negative, with an elasticity of - 0.26. The pure substitution effect of a wage change increases labor supply: a 1 percent increase in wages leads to a 0.49 percent increase in labor supply, controlling for income effects.||Data from the 1987 Practice Patterns of Young Physicians Survey (YPS),||Econometric model (log linear regression)||Rizzo and Blumenthal, 1994|
|Estimate the labor supply of physicians employed at hospitals in Norway, using personnel register data merged with other public records.||Research indicates a 10 percent wage increase would lead to a 3 percent increase in physician labor supply (wage elasticity of 0.3). The magnitude of the wage elasticity in this case may be relatively small because of hospital employed physicians tendency to have a lower wage than self-employed physicians, though still larger than previous estimates.||Data set used is a sample of 1303 male physicians observed over the period 1993 and 1997.||Econometric model (GMM, system GMM)||Baltagi et al., 2005|
|Estimate wage elasticities for Norwegian nurses.||40 percent of PCC students returned to New Mexico to practice compared to 32 percent of traditional students||Data includes detailed information on 18,066 individuals over 5 years totaling 56,832 observations between 1993 and 1997.||Econometric model (fixed effects)||Askildsen & Baltagi , 2002|
|Effect of a permanent 10 percent increase in fees for primary care ambulatory visits on volume of services and cost to Medicare.||Analysis shows the fee increase would increase primary care visits by 8.8 percent, and raise the overall cost of primary care visits by 17 percent. However, these increases would yield more than a six-fold annual return in lower Medicare costs for other services—mostly inpatient and post-acute care—once the full effects on treatment patterns are realized. The net result would be a drop in Medicare costs of nearly 2 percent. These||Data set contains Medicare claims data (2004 to 2006) as well as physician data from the Community Tracking Study (CTS) Physician Survey (2004 to 2005).||Econometric model||Reschovsky et al., 2012|
|Estimates the relationship between Medicare fees and quantities provided by physicians for eight specific services.||Results show that Medicare fees are positively related to quantity provided for all eight services, and are significantly different from zero and elastic for five. Estimates are that a 10 percent reduction in Medicare fees would lead to 1.8 percent to 6.6 percent reduction in the volume of office visits.||Data set contains 13,707 physicians who responded to surveys in 2000/2001 and/or 2004/2005 and were linked to all Medicare claims for their Medicare patients.||Econometric model (GLM)||Hadley et al., 2009|
|Estimate trends in hours worked by US physicians and assess for association with physician fees.||After remaining stable through the early 1990s, mean hours worked per week decreased by 7.2 percent between 1996 and 2008 among all physicians (from 54.9 hours per week in 1996-1998 to 51.0 hours per week in 2006-2008. Excluding resident physicians, whose hours decreased by 9.8 percent the last decade due to duty hour limits imposed in 2003, nonresident physician hours decreased by 5.7 percent||Data set from US Census Bureau Current Population Survey between 1976 and 2008 (N=116733).||Outcomes of a Problem-based learning model (specialty and practice location)||Steiger et al., 2010|
|Study Goal||Results||Data||Type of Study||Reference|
Topic: Financial Incentives for Primary Care Providers in Underserved Areas (Section IV)
|Evaluate effectiveness and developing trends in bonus payments to rural physicians.||In 1991 the total amount of HPSA bonus was almost 31.6 million dollars. Estimates show that 58.3 percent of the total HPSA bonus payment went to rural HPSAs, while the remaining 41.7 percent went to urban HPSAs. They also observed that the total bonus payment grew to reach about 106 million dollars in 1996, but then gradually declined to almost 77 million dollars in 1998. The rural proportion of HPSA bonus payments decreased to 51.1 percent.||Data set includes Area Resource File (ARF) and Medicare Part B claims data for multiple years.||Trend examination||Shugarman et al., 2001|
|Examine trends in Medicare spending for basic payments and bonus payments for physician services provided to beneficiaries residing in nonmetropolitan counties.||Payments under the Congressionally-mandated bonus payment program accounted for less than 1 percent of expenditures for physician services in nonmetropolitan, underserved counties. Physician payments increased from 1992 to 1998, while bonus payments increased through 1996 but then declined by 13 percent by 1998. The share of bonus payments to primary care physicians declined throughout the decade, but the share for primary care services increased.||Data set includes Area Resource File (ARF) and Medicare Part B claims data for multiple years.||Trend examination||Shugarman and Farley, 2003|
|Evaluate the effectiveness of programs that provide financial incentives to physicians in exchange for a rural or underserviced area return-of-service (ROS) commitment.||The majority of studies reported effective recruitment despite high buy-out rates in some US-based programs. Increasing Canadian tuition and debt among medical students may make these programs attractive. The one prospective cohort study on retention showed that physicians who chose voluntarily to go to a rural area were far more likely to stay long term than those who located there as an ROS commitment. Multidimensional programs appeared to be more successful than those relying on financial incentives alone.||Limited literature available given quality limitations.||Literature Review||Sempowski et al., 2004|
|Assess the effect of various incentive measures introduced in Quebec (Canada) to influence the geographical distribution of physicians||On average, a 10 percent increase in the general practitioner fees for medical services in a region increases the propensity of a beginning GP to work in this region by 7 percent. Thus the implied elasticity of location choice probability with respect to fee increases is about 0.7. However, this elasticity varies across regions: it is higher in remote regions (with a maximum of 1.28). On the other hand, on average, the elasticity of location choice probability with respect to non-labor incomes, such as study grants, is estimated to be about 1.11.||Data for physicians come from la Corporation Professionnelle des Medecins du Qu6bec. Population data come from Canada Census (various years), and inter-census estimations from le Bureau de la Statistique du Qu6bec (unpublished data).||Econometric model (spatial autoregressive multinomial probit)||Bolduc et al., 1996|
|Compare the types of locations chosen by alumni and non-alumni of a United States program charged with increasing physician supply.||Eliminating the program would decrease the supply of physicians in medically underserved communities by roughly 10 percent.||Data from multiple sources, primarily the
American Medical Association (AMA) master file for 1981, 1986, 1991, and 1996.
|Econometric (multinomial logit)||Holmes, 2005|
Examine the history and results of The Physician Shortage Area Program (PSAP) of Jefferson Medical College and identify factors independently predictive of rural primary care supply and retention.
|Freshman-year plan for family practice, being in the PSAP, having a National Health Service Corps scholarship, male sex, and taking an elective senior family practice rural preceptorship were independently predictive of physicians practicing rural primary care. Among PSAP graduates, taking a senior rural preceptorship was independently predictive of rural primary care. However, non-PSAP graduates with 2 key selection characteristics of PSAP students (having grown up in a rural area and freshman-year plans for family practice) were 78 percent as likely as PSAP graduates to be rural primary care physicians, and 75 percent as likely to remain, suggesting that the admissions component of the PSAP is the most important reason for its success.||Data includes a total of 3414 Jefferson Medical College graduates from the classes of 1978-1993, including 220 PSAP graduates.||Retrospective cohort study||Rabinowitz et al., 2001|
|Study Goal||Results||Data||Type of Study||Reference|
Topic: Impact of the Increase in Medicaid Reimbursement Rate Relative to the Medicare Rate (Section V)
|Identify the causal effect of increases in Medicaid reimbursement rates relative to the Medicare rate on the propensity of primary care physicians accepting new Medicaid patients.||For primary care physicians, a 10 percentage point increase in the Medicaid/Medicare fee ratio for primary care is associated with only a 2.1 percentage-point increase in Medicaid patient acceptance. The average Medicaid reimbursement rate relative Medicare in 2008 was at 66.2 percent and the national average acceptance rate of new Medicaid patients by PCPs was 41.5 percent. Therefore, the implied elasticity of accepting primary care patients with respect to the payment rate is about 0.33. Excluding pediatricians it is 0.41.||Data acquired from HSC 2008 Health Tracking Physician Survey and American Medical Association (AMA) master file.||Econometric model (OLS)||Cunningham, 2011|
|Examine the effects of Medicaid payment generosity on access and care for adult and child Medicaid beneficiaries.||Higher payments increase the probability of having a usual source of care and the probability of having at least one visit to a doctor and other health professional for Medicaid adults, and produce more positive assessments of the health care received by adults and children. However, payment generosity has no effect on the other measures that we examined, such as the probability of receiving preventive care or the probability of having unmet needs.||Data from the National Surveys of America’s Families (1997, 1999, 2002) and are linked to the Urban Institute Medicaid capitation rate surveys, the Area Resource File, and the American Hospital Association survey files.||Econometric model (difference-in-difference)||Shen & Zuckerman, 2005|
|Evaluation in California of whether the expansion of Medicaid managed care and a physician payment increase were associated with an increase over time in the percentage of physicians caring for Medicaid patients.||Despite large increases in the use of Medi-Cal,
managed care and the implementation of Medi-Cal physician fee increases between 1996 and 2001, there was no significant in- crease in the percentage of primary care physicians accepting new Medi-Cal patients or having any Medi-Cal patients in practice over time.
|Dataset comprised of surveys of probability samples of primary care and specialist physicians in California in 1996, 1998, and 2001 and AMA master file data.||Chi-square tests||Bindman et al., 2003|
|Examine the impact of California’s Medicaid re- imbursement for nursing homes which includes incentives directed at staffing.||Consistent with the rate incentives and rational expectation behavior, expected nursing home reimbursement rates in 2008 were associated with increased RN staffing levels in 2006 but had no relationship with licensed practical nurse and certified nursing assistant staffing. The effect was estimated at 2 minutes per $10 increase in rate.||Data from Medicaid master file for a total of 927 California free-standing nursing homes in 2006.||Econometric model (two-stage MLE)||Mukamel et al., 2012|
|Assess the effects of Medicaid fee changes on physician participation, enrollee access, and shifts in the site of ambulatory care using several natural experiments in Maine and Michigan.||The reimbursement changes included substantial percentage changes in fees; however the value of the Medicaid fee improvements relative to the private market eroded very rapidly in the months following the interventions. Implied elasticities ranged between 0.39 and -0.021.||Dataset from Medicaid claims and enrollment data between 1988 and 1992||Program Overview & Outcomes of community-based training.||Coburn et al., 1999|