Family Practices in Transforming Clinical Practice Initiative Showed No Changes in Medicare Costs or Utilization.

BACKGROUND: The Centers for Medicare and Medicaid Services proposed that the Transforming Clinical Practice Initiative (TCPI) would improve health outcomes for patients, reduce utilization of institutional services, and generate significant savings for payers by the end of September 2019. OBJECTIVE: The objective of this study was to investigate whether participation in TCPI's Practice Transformation Networks (PTNs) was associated with improved cost and utilization outcomes for Medicare patients of family medicine-based practices in the first 2 years, that is, 2016-2017, of the Initiative. STUDY DESIGN: A quasi-experimental design with a longitudinal cohort of family medicine-based practices and a propensity-matched comparison sample. SUBJECTS: A total of 761 PTN practices and 3451 non-PTN practices. MEASURES: To measure practice-level patient outcomes, we attributed patients to practice based on the plurality of office visits. We obtained Medicare claims from 2011 to 2017 to assess PTN participation effects for Medicare Part A and B costs, hospital admission, and emergency department visit rates using a Difference-in-Differences design, adjusting for baseline characteristics. RESULTS: The differences in Medicare Part A and B costs (-1.71%, P=0.25), annual rates of hospitalization (-0.59%, P=0.12) and emergency department visit (-0.29%, P=0.46) were not significantly lower among PTN practices (N=761) than among propensity score-matched non-PTN practices (N=3541). CONCLUSIONS: TCPI's transforming efforts, such as the outcomes examined in the study, might need a longer time frame to manifest and require evaluation after the full 4-year participation period. The indistinguishable effect of PTN participation may also be attributed to the fact that non-PTN practices might have participated in other initiatives that changed their care and curbed health care utilization and costs consequently.

Primary Care's Historic Role in Vaccination and Potential Role in COVID-19 Immunization Programs.

PURPOSE: Coronavirus disease 2019 (COVID-19) pandemic recovery will require a broad and coordinated effort for infection testing, immunity determination, and vaccination. With the advent of several COVID-19 vaccines, the dissemination and delivery of COVID-19 immunization across the nation is of concern. Previous immunization delivery patterns may reveal important components of a comprehensive and sustainable effort to immunize everyone in the nation. METHODS: The delivery of vaccinations were enumerated by provider type using 2017 Medicare Part B Fee-For-Service data and the 2013-2017 Medical Expenditure Panel Survey. The delivery of these services was examined at the service, physician, and visit level. RESULTS: In 2017 Medicare Part B Fee-For-Service, primary care physicians provided the largest share of services for vaccinations (46%), followed closely by mass immunizers (45%), then nurse practitioners/physician assistants (NP/PAs) (5%). The Medical Expenditure Panel Survey showed that primary care physicians provided most clinical visits for vaccination (54% of all visits). CONCLUSIONS: Primary care physicians have played a crucial role in delivery of vaccinations to the US population, including the elderly, between 2012-2017. These findings indicate primary care practices may be a crucial element of vaccine counseling and delivery in the upcoming COVID-19 recovery and immunization efforts in the United States.

Using Machine Learning to Predict Primary Care and Advance Workforce Research.

PURPOSE: To develop and test a machine-learning-based model to predict primary care and other specialties using Medicare claims data. METHODS: We used 2014-2016 prescription and procedure Medicare data to train 3 sets of random forest classifiers (prescription only, procedure only, and combined) to predict specialty. Self-reported specialties were condensed to 27 categories. Physicians were assigned to testing and training cohorts, and random forest models were trained and then applied to 2014-2016 data sets for the testing cohort to generate a series of specialty predictions. Comparing the predicted specialty to self-report, we assessed performance with F1 scores and area under the receiver operating characteristic curve (AUROC) values. RESULTS: A total of 564,986 physicians were included. The combined model had a greater aggregate (macro) F1 score (0.876) than the prescription-only (0.745; P <.01) or procedure-only (0.821; P <.01) model. Mean F1 scores across specialties in the combined model ranged from 0.533 to 0.987. The mean F1 score was 0.920 for primary care. The mean AUROC value for the combined model was 0.992, with values ranging from 0.982 to 0.999. The AUROC value for primary care was 0.982. CONCLUSIONS: This novel approach showed high performance and provides a near real-time assessment of current primary care practice. These findings have important implications for primary care workforce research in the absence of accurate data.

Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments.

BACKGROUND: Risk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments. METHODS: We employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators. Healthcare claims from privately-insured US adults (2016-2017), and Census data were used for analysis. Data from 1.02 million adults were used for derivation, and data from 0.26 million to assess performance. Model performance was measured using coefficient of determination (R2), discrimination (C-statistic), and mean absolute error (MAE) for the overall population, and predictive ratio and net compensation for vulnerable subgroups. We provide 95% confidence intervals (CI) around each performance measure. RESULTS: Linear regression without SDH indicators achieved moderate determination (R2 0.327, 95% CI: 0.300, 0.353), error ($6992; 95% CI: $6889, $7094), and discrimination (C-statistic 0.703; 95% CI: 0.701, 0.705). ML without SDH indicators improved all metrics (R2 0.388; 95% CI: 0.357, 0.420; error $6637; 95% CI: $6539, $6735; C-statistic 0.717; 95% CI: 0.715, 0.718), reducing misestimation of cost by $3.5 M per 10,000 members. Among people living in areas with high poverty, high wealth inequality, or high prevalence of uninsured, SDH indicators reduced underestimation of cost, improving the predictive ratio by 3% (~$200/person/year). CONCLUSIONS: ML improved risk adjustment models and the incorporation of SDH indicators reduced underpayment in several vulnerable populations.

Higher Primary Care Physician Continuity is Associated With Lower Costs and Hospitalizations.

PURPOSE: Continuity of care is a defining characteristic of primary care associated with lower costs and improved health equity and care quality. However, we lack provider-level measures of primary care continuity amenable to value-based payment, including the Medicare Quality Payment Program (QPP). We created 4 physician-level, claims-based continuity measures and tested their associations with health care expenditures and hospitalizations. METHODS: We used Medicare claims data for 1,448,952 beneficiaries obtaining care from a nationally representative sample of 6,551 primary care physicians to calculate continuity scores by 4 established methods. Patient-level continuity scores attributed to a single physician were averaged to create physician-level scores. We used beneficiary multilevel models, including beneficiary controls, physician characteristics, and practice rurality to estimate associations with total Medicare Part A & B expenditures (allowed charges, logged), and any hospitalization. RESULTS: Our continuity measures were highly correlated (correlation coefficients ranged from 0.86 to 0.99), with greater continuity associated with similar outcomes for each. Adjusted expenditures for beneficiaries cared for by physicians in the highest Bice-Boxerman continuity score quintile were 14.1% lower than for those in the lowest quintile ($8,092 vs $6,958; ß = -0.151; 95% CI, -0.186 to -0.116), and the odds of hospitalization were 16.1% lower between the highest and lowest continuity quintiles (OR = 0.839; 95% CI, 0.787 to 0.893). CONCLUSIONS: All 4 continuity scores tested were significantly associated with lower total expenditures and hospitalization rates. Such indices are potentially useful as QPP measures, and may also serve as proxy resource-use measures, given the strength of association with lower costs and utilization.

Predictors of Attrition From Family Medicine Board Certification.

Board certification is associated with higher quality care. We sought to determine the rates and predictors of attrition from certification among family physicians who achieved initial certification with the American Board of Family Medicine from 1980 through 2000. In this period, 5.6% of family physicians never attempted recertification, with the rate increasing from 4.9% between 1990 and 1995 to 5.7% from 1996 to 2000. Being male, an international medical graduate, or 30 years of age or older at initial certification was associated with not recertifying. With information about those likely to leave certification, the board can design and implement interventions that minimize attrition.

Evidence-based HMO care for prostate specific antigen testing.

BACKGROUND: There is pressure in the U.S. system to move away from fee-for-service models to a more pre-paid system, which may result in decreased costs, but the impact on evidence-based care is unclear. We examined a large pre-paid Health Maintenance Organizations (HMO) in Israel to see if evidence-based guidelines are followed for prostate specific antigen (PSA) testing. METHODS: A retrospective cohort of ambulatory visits from 2002 to 2011 of patients age >75 receiving care from Clalit Health Services was conducted. Historically reported U.S. cohorts were used for comparison. The main measure was the percent of patients who had at least one PSA after age 75. RESULTS: In each of the 10 years of follow-up, 22% of the yearly Israeli cohort, with no known malignancy or benign prostatic hyperplasia, had at least one PSA, while for the total 10 years, 30% of the men had at least one PSA. These rates are considerably lower than previously reported U.S. rates. CONCLUSIONS: In a pre-paid system in which physicians have no incentive to order tests, they appear to order PSA tests at a lower rate than has been observed in the U.S. system. Additional quality of measures should continue to be examined as the U.S. shifts away from a fee-for-service model. Copyright © 2016 John Wiley & Sons, Ltd.

Rural Family Physicians Are Twice as Likely to Use Telehealth as Urban Family Physicians.

BACKGROUND: Telehealth has the potential to reduce health inequities and improve health outcomes among rural populations through increased access to physicians, specialists, and reduced travel time for patients. INTRODUCTION: Although rural telehealth services have expanded in several specialized areas, little is known about the attitudes, beliefs, and uptake of telehealth use in rural American primary care. This study characterizes the differences between rural and urban family physicians (FPs), their perceptions of telehealth use, and barriers to further adoption. MATERIALS AND METHODS: Nationally representative randomly sampled survey of 5,000 FPs. RESULTS: Among the 31.3% of survey recipients who completed the survey, 83% practiced in urban areas and 17% in rural locations. Rural FPs were twice as likely to use telehealth as urban FPs (22% vs. 10%). Logistic regressions showed rural FPs had greater odds of reporting telehealth use to connect their patients to specialists and to care for their patients. Rural FPs were less likely to identify liability concerns as a barrier to using telehealth. DISCUSSION: Telemedicine allows rural patients to see specialists without leaving their communities and permits rural FPs to take advantage of specialist expertise, expand their scope of practice, and reduce the feeling of isolation experienced by rural physicians. CONCLUSION: Efforts to raise awareness of current payment policies for telehealth services, addressing the limitations of current reimbursement policies and state regulations, and creating new avenues for telehealth reimbursement and technological investments are critical to increasing primary care physician use of telehealth services.

Regional Variation in Primary Care Involvement at the End of Life.

PURPOSE: Variation in end-of-life care in the United States is frequently driven by the health care system. We assessed the association of primary care physician involvement at the end of life with end-of-life care patterns. METHODS: We analyzed 2010 Medicare Part B claims data for US hospital referral regions (HRRs). The independent variable was the ratio of primary care physicians to specialist visits in the last 6 months of life. Dependent variables included the rate of hospital deaths, hospital and intensive care use in the last 6 months of life, percentage of patients seen by more than 10 physicians, and Medicare spending in the last 2 years of life. Robust linear regression analysis was used to measure the association of primary care physician involvement at the end of life with the outcome variables, adjusting for regional characteristics. RESULTS: We assessed 306 HRRs, capturing 1,107,702 Medicare Part B beneficiaries with chronic disease who died. The interquartile range of the HRR ratio of primary care to specialist end-of-life visits was 0.77 to 1.21. HRRs with high vs low primary care physician involvement at the end of life had significantly different patient, population, and health system characteristics. Adjusting for these differences, HRRs with the greatest primary care physician involvement had lower Medicare spending in the last 2 years of life ($65,160 vs $69,030; P = .003) and fewer intensive care unit days in the last 6 months of life (2.90 vs 4.29; P <.001), but also less hospice enrollment (44.5% of decedents vs 50.4%; P = .004). CONCLUSIONS: Regions with greater primary care physician involvement in end-of-life care have overall less intensive end-of-life care.

The Effects of Training Institution Practice Costs, Quality, and Other Characteristics on Future Practice.

PURPOSE: Medicare beneficiary spending patterns reflect those of the 306 Hospital Referral Regions where physicians train, but whether this holds true for smaller areas or for quality is uncertain. This study assesses whether cost and quality imprinting can be detected within the 3,436 Hospital Service Areas (HSAs), 82.4 percent of which have only 1 teaching hospital, and whether sponsoring institution characteristics are associated. METHODS: We conducted a secondary, multi-level, multivariable analysis of 2011 Medicare claims and American Medical Association Masterfile data for a random, nationally representative sample of family physicians and general internists who completed residency between 1992 and 2010 and had more than 40 Medicare patients (3,075 physicians providing care to 503,109 beneficiaries). Practice and training locations were matched with Dartmouth Atlas HSAs and categorized into low-, average-, and high-cost spending groups. Practice and training HSAs were assessed for differences in 4 diabetes quality measures. Institutional characteristics included training volume and percentage of graduates in rural practice and primary care. RESULTS: The unadjusted, annual, per-beneficiary spending difference between physicians trained in high- and low-cost HSAs was $1,644 (95% CI, $1,253-$2,034), and the difference remained significant after controlling for patient and physician characteristics. No significant relationship was found for diabetes quality measures. General internists were significantly more likely than family physicians to train in high-cost HSAs. Institutions with more graduates in rural practice and primary care produced lower-spending physicians. CONCLUSIONS: The "imprint" of training spending patterns on physicians is strong and enduring, without discernible quality effects, and, along with identified institutional features, supports measures and policy options for improved graduate medical education outcomes.

Mobility of US Rural Primary Care Physicians During 2000-2014.

PURPOSE: Despite considerable investment in increasing the number of primary care physicians in rural shortage areas, little is known about their movement rates and factors influencing their mobility. We aimed to characterize geographic mobility among rural primary care physicians, and to identify location and individual factors that influence such mobility. METHODS: Using data from the American Medical Association Physician Masterfile for each clinically active US physician, we created seven 2-year (biennial) mobility periods during 2000-2014. These periods were merged with county-level "rurality," physician supply, economic characteristics, key demographic measures, and individual physician characteristics. We computed (1) mobility rates of physicians by rurality; (2) linear regression models of county-level rural nonretention (departure); and (3) logit models of physicians leaving rural practice. RESULTS: Biennial turnover was about 17% among physicians aged 45 and younger, compared with 9% among physicians aged 46 to 65, with little difference between rural and metropolitan groups. County-level physician mobility was higher for counties that lacked a hospital (absolute increase = 5.7%), had a smaller population size, and had lower primary care physician supply, but area-level economic and demographic factors had little impact. Female physicians (odds ratios = 1.24 and 1.46 for those aged 45 or younger and those aged 46 to 65, respectively) and physicians born in a metropolitan area (odds ratios = 1.75 and 1.56 for those aged 45 or younger and those aged 46 to 65, respectively) were more likely to leave rural practice. CONCLUSIONS: These flndings provide national-level evidence of rural physician mobility rates and factors associated with both county-level retention and individual-level departures. Outcomes were notably poorer in the most remote locations and those already having poorer physician supply and professional support. Rural health workforce planners and policymakers must be cognizant of these key factors to more effectively target retention policies and to take into account the additional support needed by these more vulnerable communities.

Retirement Age Ranges from Clinical Practice of Maternal-Fetal Medicine Physicians.

Objectives Retirement of "baby boomer" physicians is a matter of growing concern in light of the shortage of certain physician groups. The objectives of this investigation were to define what constitutes a customary retirement age range of maternal-fetal medicine (MFM) physicians and examine how that compares with other obstetrician-gynecologist (ob-gyn) specialists. Study Design This descriptive study was based on American Medical Association Masterfile survey data from 2010 to 2014. Data from the National Provider Identifier were used to correct for upward bias in reporting retirement ages. Only physicians engaged in direct patient care between ages 55 and 80 years were included. Primary outcomes involved comparisons of retirement ages of male and female physicians with other ob-gyn specialties. Results Interquartile ranges of retirement ages were similar between specialists in MFM (64.1-71.1), gynecologic oncology (62.1-68.9), reproductive endocrinology and infertility (64.1-71.7), and general ob-gyn (61.5-67.9). In every specialty, women retired earlier, while males in MFM were most likely to retire at the oldest age (median 70.0). Conclusion MFM physicians usually retired from clinical practice between ages 64 and 71 years, which is similar to other ob-gyn specialists. Females retired earlier, however, which may impact the overall supply as more females pursue MFM careers.

Measuring the attractiveness of rural communities in accounting for differences of rural primary care workforce supply.

INTRODUCTION: Many rural communities continue to experience an undersupply of primary care doctor services. While key professional factors relating to difficulties of recruitment and retention of rural primary care doctors are widely identified, less attention has been given to the role of community and place aspects on supply. Place-related attributes contribute to a community's overall amenity or attractiveness, which arguably influence both rural recruitment and retention relocation decisions of doctors. This bi-national study of Australia and the USA, two developed nations with similar geographic and rural access profiles, investigates the extent to which variations in community amenity indicators are associated with spatial variations in the supply of rural primary care doctors. METHODS: Measures from two dimensions of community amenity: geographic location, specifically isolation/proximity; and economics and sociodemographics were included in this study, along with a proxy measure (jurisdiction) of a third dimension, environmental amenity. Data were chiefly collated from the American Community Survey and the Australian Census of Population and Housing, with additional calculated proximity measures. Rural primary care supply was measured using provider-to-population ratios in 1949 US rural counties and in 370 Australian rural local government areas. Additionally, the more sophisticated two-step floating catchment area method was used to measure Australian rural primary care supply in 1116 rural towns, with population sizes ranging from 500 to 50 000. Associations between supply and community amenity indicators were examined using Pearson's correlation coefficients and ordinary least squares multiple linear regression models. RESULTS: It was found that increased population size, having a hospital in the county, increased house prices and affluence, and a more educated and older population were all significantly associated with increased workforce supply across rural areas of both countries. While remote areas were strongly linked with poorer supply in Australia, geographical remoteness was not significant after accounting for other indicators of amenity such as the positive association between workforce supply and coastal location. Workforce supply in the USA was negatively associated with fringe rural area locations adjacent to larger metropolitan areas and characterised by long work commutes. The US model captured 49% of the variation of workforce supply between rural counties, while the Australian models captured 35-39% of rural supply variation. CONCLUSIONS: These data support the idea that the rural medical workforce is maldistributed with a skew towards locating in more affluent and educated areas, and against locating in smaller, poorer and more isolated rural towns, which struggle to attract an adequate supply of primary care services. This evidence is important in understanding the role of place characteristics and rural population dynamics in the recruitment and retention of rural doctors. Future primary care workforce policies need to place a greater focus on rural communities that, for a variety of reasons, may be less attractive to doctors looking to begin or remain working there.

When Do Primary Care Physicians Retire? Implications for Workforce Projections.

PURPOSE: Retirement of primary care physicians is a matter of increasing concern in light of physician shortages. The joint purposes of this investigation were to identify the ages when the majority of primary care physicians retire and to compare this with the retirement ages of practitioners in other specialties. METHODS: This descriptive study was based on AMA Physician Masterfile data from the most recent 5 years (2010-2014). We also compared 2008 Masterfile data with data from the National Plan and Provider Enumeration System to calculate an adjustment for upward bias in retirement ages when using the Masterfile alone. The main analysis defined retirement as leaving clinical practice. The primary outcome was construction of a retirement curve. Secondary outcomes involved comparisons of retirement interquartile ranges (IQRs) by sex and practice location across specialties. RESULTS: The 2014 Masterfile included 77,987 clinically active primary care physicians between ages 55 and 80 years. The median age of retirement from clinical activity of all primary care physicians who retired in the period from 2010 to 2014 was 64.9 years, (IQR, 61.4-68.3); the median age of retirement from any activity was 66.1 years (IQR, 62.6-69.5). However measured, retirement ages were generally similar across primary care specialties. Females had a median retirement about 1 year earlier than males. There were no substantive differences in retirement ages between rural and urban primary care physicians. CONCLUSIONS: Primary care physicians in our data tended to retire in their mid-60s. Relatively small differences across sex, practice location, and time suggest that changes in the composition of the primary care workforce will not have a remarkable impact on overall retirement rates in the near future.

Solo and Small Practices: A Vital, Diverse Part of Primary Care.

PURPOSE: Solo and small practices are facing growing pressure to consolidate. Our objectives were to determine (1) the percentage of family physicians in solo and small practices, and (2) the characteristics of and services provided by these practices. METHODS: A total of 10,888 family physicians seeking certification through the American Board of Family Medicine in 2013 completed a demographic survey. Their practices were split into categories by size: solo, small (2 to 5 providers), medium (6 to 20 providers), and large (more than 20 providers). We also determined the rurality of the county where the physicians practiced. We developed 2 logistic regression models: one assessed predictors of practicing in a solo or small practice, while the other was restricted to solo and small practices and assessed predictors of practicing in a solo practice. RESULTS: More than one-half of respondents worked in solo or small practices. Small practices were the largest group (36%) and were the most likely to be located in a rural setting (20%). The likelihood of having a care coordinator and medical home certification increased with practice size. Physicians were more likely to be practicing in small or solo practices (vs medium-sized or large ones) if they were African American or Hispanic, had been working for more than 30 years, and worked in rural areas. Physicians were more likely to be practicing in small practices (vs solo ones) if they worked in highly rural areas. CONCLUSIONS: Family physicians in solo and small practices comprised the majority among all family physicians seeking board certification and were more likely to work in rural geographies. Extension programs and community health teams have the potential to support transformation within these practices.

Shifting tides in the emigration patterns of Canadian physicians to the United States: a cross-sectional secondary data analysis.

BACKGROUND: The relative ease of movement of physicians across the Canada/US border has led to what is sometimes referred to as a 'brain drain' and previous analysis estimated that the equivalent of two graduating classes from Canadian medical schools were leaving to practice in the US each year. Both countries fill gaps in physician supply with international medical graduates (IMGs) so the movement of Canadian trained physicians to the US has international ramifications. Medical school enrolments have been increased on both sides of the border, yet there continues to be concerns about adequacy of physician human resources. This analysis was undertaken to re-examine the issue of Canadian physician migration to the US. METHODS: We conducted a cross-sectional analysis of the 2015 American Medical Association (AMA) Masterfile to identify and locate any graduates of Canadian schools of medicine (CMGs) working in the United States in direct patient care. We reviewed annual reports of the Canadian Resident Matching Service (CaRMS); the Canadian Post-MD Education Registry (CAPER); and the Canadian Collaborative Centre for Physician Resources (C3PR). RESULTS: Beginning in the early 1990s the number of CMGs locating in the U.S. reached an all-time high and then abruptly dropped off in 1995. CMGs are going to the US for post-graduate training in smaller numbers and, are less likely to remain than at any time since the 1970's. CONCLUSIONS: This four decade retrospective found considerable variation in the migration pattern of CMGs to the US. CMGs' decision to emigrate to the U.S. may be influenced by both 'push' and 'pull' factors. The relative strength of these factors changed and by 2004, more CMGs were returning from abroad than were leaving and the current outflow is negligible. This study supports the need for medical human resource planning to assume a long-term view taking into account national and international trends to avoid the rapid changes that were observed. These results are of importance to medical resource planning.

Considerations about retirement from clinical practice by obstetrician-gynecologists.

Retirement of obstetrician-gynecologists is becoming a matter of increasing concern in light of an expected shortage of practicing physicians. Determining a retirement age is often complex. We address what constitutes a usual retirement age range from general clinical practice for an obstetrician-gynecologist, compare this with practitioners in other specialties, and suggest factors of importance to obstetrician-gynecologists before retirement. Although the proportion of obstetrician-gynecologists ≥55 years old is similar to other specialists, obstetrician-gynecologists retire at younger ages than male or female physicians in other specialties. A customary age range of retirement from obstetrician-gynecologist practice would be 59-69 years (median, 64 years). Women, who constitute a growing proportion of obstetrician-gynecologists in practice, retire earlier than men. The large cohort of "baby boomer" physicians who are approaching retirement (approximately 15,000 obstetrician-gynecologists) deserves tracking while an investigation of integrated women's health care delivery models is conducted. Relevant considerations would include strategies to extend the work longevity of those who are considering early retirement or desiring part-time employment. Likewise volunteer work in underserved community clinics or teaching medical students and residents offers continuing personal satisfaction for many retirees and preservation of self-esteem and medical knowledge.