Impact of Service-Based Student Loan Repayment Program on the Primary Care Workforce.

PURPOSE: As the average level of medical education indebtedness rises, physicians look to programs such as Public Service Loan Forgiveness (PSLF) and National Health Service Corps (NHSC) to manage debt burden. Both represent service-dependent loan repayment programs, but the requirements and program outcomes diverge, and assessing the relative uptake of each program may help to inform health workforce policy decisions. We sought to describe variation in the composition of repayment program participant groups and measure relative impact on patient access to care. METHODS: In this bivariate analysis, we analyzed data from 10,677 respondents to the American Board of Family Medicine's National Graduate Survey to study differences in loan repayment program uptake as well as the unique participant demographics, scope of practice, and likelihood of practicing with a medically underserved or rural population in each program cohort. RESULTS: The rate of PSLF uptake tripled between 2016 and 2020, from 7% to 22% of early career family physicians, while NHSC uptake remained static at 4% to 5%. Family physicians reporting NHSC assistance were more likely than those reporting PSLF assistance to come from underrepresented groups, demonstrated a broader scope of practice, and were more likely to practice in rural areas (23.3% vs 10.8%) or whole-county Health Professional Shortage Areas (12.5% vs 3.7%) and with medically underserved populations (82.2% vs 24.2%). CONCLUSIONS: Although PSLF supports family physicians intending to work in public service, their peers who choose NHSC are much more likely to work in underserved settings. Our findings may prompt a review of the goals of service loan forgiveness programs with potential to better serve health workforce needs.

Clinicians' Overestimation of Their Geographic Service Area.

In this study, we evaluated family physicians' ability to estimate the service area of their patient panel-a critical first step in contextual population-based primary care. We surveyed 14 clinicians and administrators from 6 practices. Participants circled their estimated service area on county maps that were compared with the actual service area containing 70% of the practice's patients. Accuracy was ascertained from overlap and the amount of estimated census tracts that were not part of the actual service area. Average overlap was 75%, but participants overestimated their service area by an average of 166 square miles. Service area overestimation impedes implementation of targeted community interventions by practices.

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.

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.

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.

Estimating the residency expansion required to avoid projected primary care physician shortages by 2035.

PURPOSE: The purpose of this study was to calculate the projected primary care physician shortage, determine the amount and composition of residency growth needed, and estimate the impact of retirement age and panel size changes. METHODS: We used the 2010 National Ambulatory Medical Care Survey to calculate utilization of ambulatory primary care services and the US Census Bureau to project demographic changes. To determine the baseline number of primary care physicians and the number retiring at 66 years, we used the 2014 American Medical Association Masterfile. Using specialty board and American Osteopathic Association figures, we estimated the annual production of primary care residents. To calculate shortages, we subtracted the accumulated primary care physician production from the accumulated number of primary care physicians needed for each year from 2015 to 2035. RESULTS: More than 44,000 primary care physicians will be needed by 2035. Current primary care production rates will be unable to meet demand, resulting in a shortage in excess of 33,000 primary care physicians. Given current production, an additional 1,700 primary care residency slots will be necessary by 2035. A 10% reduction in the ratio of population per primary care physician would require more than 3,000 additional slots by 2035, whereas changing the expected retirement age from 66 years to 64 years would require more than 2,400 additional slots. CONCLUSIONS: To eliminate projected shortages in 2035, primary care residency production must increase by 21% compared with current production. Delivery models that shift toward smaller ratios of population to primary care physicians may substantially increase the shortage.

A retrospective analysis of the relationship between medical student debt and primary care practice in the United States.

PURPOSE: We undertook a study to reexamine the relationship between educational debt and primary care practice, accounting for the potentially confounding effect of medical student socioeconomic status. METHODS: We performed retrospective multivariate analyses of data from 136,232 physicians who graduated from allopathic US medical schools between 1988 and 2000, obtained from the American Association of Medical Colleges Graduate Questionnaire, the American Medical Association Physician Masterfile, and other sources. Need-based loans were used as markers for socioeconomic status of physicians' families of origin. We examined 2 outcomes: primary care practice and family medicine practice in 2010. RESULTS: Physicians who graduated from public schools were most likely to practice primary care and family medicine at graduating educational debt levels of $50,000 to $100,000 (2010 dollars; P <.01). This relationship between debt and primary care practice persisted when physicians from different socioeconomic status groups, as approximated by loan type, were examined separately. At higher debt, graduates' odds of practicing primary care or family medicine declined. In contrast, private school graduates were not less likely to practice primary care or family medicine as debt levels increased. CONCLUSIONS: High educational debt deters graduates of public medical schools from choosing primary care, but does not appear to influence private school graduates in the same way. Students from relatively lower income families are more strongly influenced by debt. Reducing debt of selected medical students may be effective in promoting a larger primary care physician workforce.

Factors influencing family physicians' contribution to the child health care workforce.

PURPOSE: We wanted to explore demographic and geographic factors associated with family physicians' provision of care to children. METHODS: We analyzed the proportion of family physicians providing care to children using survey data collected by the American Board of Family Medicine from 2006 to 2009. Using a cross-sectional study design and logistic regression analysis, we examined the association of various physician demographic and geographic factors and providing care of children. RESULTS: Younger age, female sex, and rural location are positive predictors of family physicians providing care to children: odds ratio (OR) = 0.97 (95% CI, 0.97-0.98), 1.19 (1.12-1.25), and 1.50 (1.39-1.62), respectively. Family physicians practicing in a partnership are more likely to provide care to children than those in group practice: OR = 1.53 (95% CI, 1.40-1.68). Family physicians practicing in areas with higher density of children are more likely to provide care to children: OR = 1.04 (95% CI, 1.03-1.05), while those in high-poverty areas are less likely 0.10 (95% CI, 0.10-0.10). Family physicians located in areas with no pediatricians are more likely to provide care to children than those in areas with higher pediatrician density: OR = 1.80 (95% CI, 1.59-2.01). CONCLUSIONS: Various demographic and geographic factors influence the likelihood of family physicians providing care to children, findings that have important implications to policy efforts aimed at ensuring access to care for children.

Variation in Practice Patterns of Early- and Later-Career Family Physicians.

INTRODUCTION: Understanding how physicians' practice patterns change over a career is important for workforce and medical education planning. This study examined trends in self-reported practice activity among early- and later-career stage family physicians (FPs). METHODS: Data on early career FPs came from the American Board of Family Medicine's National Graduate Survey (NGS) and on later career FPs from its Continuous Certification Questionnaire (CCQ). Both cohorts could complete the Practice Demographic Survey (PDS) 3 years later. Longitudinal cohorts were from 2016 to 2019 and 2017 to 2020, respectively. All surveys included identical items on scope of practice, practice type, organization, and location. We characterized physicians as outpatient continuity only, outpatient and inpatient care (mixed practice), and no outpatient continuity (for example, hospitalist). We conducted repeated cross-sectional and longitudinal analysis of practice type. RESULTS: Our sample included 8,492 NGS and 30,491 CCQ FPs. In both groups, the vast majority provided outpatient continuity of care (77% to 81%). Approximately 25% of NGS had a mixed practice compared with approximately 16% of the CCQ group. The percent of FPs who had a mixed practice declined in both groups (34.21% to 27.10% and 23.88% to 19.33%). In both groups, physicians with higher odds of leaving mixed practice were in metropolitan counties or changed practice types. CONCLUSION: Although early-career FPs more frequently reported providing both inpatient and outpatient care and serving as hospitalists compared with later-career FPs, both groups had a decline in frequency of providing mixed practice. This change after only 3 years in practice has significant implications for patient care and medical education.

Setting the Target: Comparing Family Medicine Among US Allopathic Target Schools.

BACKGROUND AND OBJECTIVES: Despite the persistent primary care physician shortage over 2 decades of allopathic medical school expansion, some medical schools are absent a department of family medicine; these schools are designated as "target" schools. These absences are important because evidence has demonstrated the association between structured exposure to family medicine during medical school and the proportion of students who ultimately select a career in family medicine. In this study, we aimed to address part of this gap by defining and characterizing the current landscape of US allopathic target schools. METHODS: We identified allopathic target schools by reviewing all Liaison Committee of Medical Education (LCME) accredited institutions for the presence of a family medicine department. To compare these schools in terms of family medicine representation and outcomes, we curated descriptive data from publicly available websites, previously published family medicine match results, and school rankings for primary care. RESULTS: We identified 12 target schools (8.7% of all US allopathic accredited medical schools) with considerable heterogeneity in opportunities for family medicine engagement, leadership, and training. Target schools with greater family medicine representation had increased outcomes for family medicine workforce and primary care opportunities. CONCLUSION: With growing primary care workforce gaps, target schools have a responsibility to enhance family medicine presence and representation at their institutions. We provide recommendations at the institutional, specialty, and national level to increase family medicine representation at target schools, with the goal that all schools eventually establish a department of family medicine.

Physician-Level Continuity of Care and Patient Outcomes in All-Payer Claims Database.

INTRODUCTION: Being one of the few existing measures of primary care functions, physician-level continuity of care (Phy-CoC) is measured by the weighted average of patient continuity scores. Compared with the well-researched patient-level continuity, Phy-CoC is a new instrument with limited evidence from Medicare beneficiaries. This study aimed to expand the patient sample to include patients of all ages and all types of insurance and reassess the associations between full panel-based Phy-CoC scores and patient outcomes. METHODS: Cross-sectional analysis at patient-level using Virginia All-Payer Claims Database (VA-APCD). Phy-CoC scores were calculated by averaging patient's Bice-Boxerman Index scores and weighted by the total number of visits. Patient outcomes included total cost and preventable hospitalization. RESULTS: In a sample of 1.6 million Virginians, patients who lived in rural areas or had Medicare as primary insurance were more likely to be attributed to physicians with the highest Phy-CoC scores. Across all adult patient populations, we found that being attributed to physicians with higher Phy-CoC was associated with 7%-11.8% higher total costs, but was not associated with the odds of preventable hospitalization. Results from models with interactions revealed nuanced associations between Phy-CoC and total cost with patient's age and comorbidity, insurance payer, and the specialty of their physician. CONCLUSIONS: In this comprehensive examination of Phy-CoC using all populations from the VA-APCD, we found an overall positive association of higher full panel-based Phy-CoC with total cost, but a non-significant association with the risk of preventable hospitalization. Achieving higher full panel-based Phy-CoC may have unintended cost implications.

Prevalence and Predictors of Burnout Among Resident Family Physicians.

BACKGROUND AND OBJECTIVES: Resident burnout may affect career choices and empathy. We examined predictors of burnout among family medicine residents. METHODS: We used data from the 2019-2021 American Board of Family Medicine Initial Certification Questionnaire, which is required of graduating residents. Burnout was a binary variable defined as reporting callousness or emotional exhaustion once a week or more. We evaluated associations using bivariate and multilevel multivariable regression analyses. RESULTS: Among 11,570 residents, 36.4% (n=4,211) reported burnout. This prevalence did not significantly vary from 2019 to 2021 and was not significantly attributable to the residency program (ICC=0.07). Residents identifying as female reported higher rates of burnout (39.0% vs 33.4%, AOR=1.29 [95% CI 1.19-1.40]). Residents reporting Asian race (30.5%, AOR=0.78 [95% CI 0.70-0.86]) and Black race (32.3%, AOR=0.71 [95% CI 0.60-0.86]) reported lower odds of burnout than residents reporting White race (39.2%). We observed lower rates among international medical graduates (26.7% vs 40.3%, AOR=0.54 [95% CI 0.48-0.60]), those planning to provide outpatient continuity care (36.0% vs 38.7%, AOR=0.77 [95% CI 0.68-0.86]), and those at smaller programs (31.7% for <6 residents per class vs 36.3% for 6-10 per class vs 40.2% for >10 per class). Educational debt greater than $250,000 was associated with higher odds of burnout than no debt (AOR=1.29 [95% CI 1.15-1.45]). CONCLUSIONS: More than one-third of recent family medicine residents reported burnout. Odds of burnout varied significantly with resident and program characteristics.

The rise of electronic health record adoption among family physicians.

PURPOSE: Realizing the benefits of adopting electronic health records (EHRs) in large measure depends heavily on clinicians and providers' uptake and meaningful use of the technology. This study examines EHR adoption among family physicians using 2 different data sources, compares family physicians with other office-based medical specialists, assesses variation in EHR adoption among family physicians across states, and shows the possibility for data sharing among various medical boards and federal agencies in monitoring and guiding EHR adoption. METHOD: We undertook a secondary analysis of American Board of Family Medicine (ABFM) administrative data (2005-2011) and data from the National Ambulatory Medical Care Survey (NAMCS) (2001-2011). RESULTS: The EHR adoption rate by family physicians reached 68% nationally in 2011. NAMCS family physician adoption rates and ABFM adoption rates (2005-2011) were similar. Family physicians are adopting EHRs at a higher rate than other office-based physicians as a group; however, significant state-level variation exists, indicating geographical gaps in EHR adoption. CONCLUSION: Two independent data sets yielded convergent results, showing that adoption of EHRs by family physicians has doubled since 2005, exceeds other office-based physicians as a group, and is likely to surpass 80% by 2013. Adoption varies at a state level. Further monitoring of trends in EHR adoption and characterizing their capacities are important to achieve comprehensive data exchange necessary for better, affordable health care.

A needs-based method for estimating the behavioral health staff needs of community health centers.

BACKGROUND: Federally Qualified Health Centers are expanding to increase access for millions of more Americans with a goal of doubling capacity to serve 40 million people. Health centers provide a lot of behavioral health services but many have difficulty accessing mental health and substance use professionals for their patients. To meet the needs of the underserved and newly insured it is important to better estimate how many behavioral health professionals are needed. METHODS: Using health center staffing data and behavioral health service patterns from the 2010 Uniform Data System and the 2010 National Survey on Drug Use and Health, we estimated the number of patients likely to need behavioral health care by insurance type, the number of visits likely needed by health center patients annually, and the number of full time equivalent providers needed to serve them. RESULTS: More than 2.5 million patients, 12 or older, with mild or moderate mental illness, and more than 357,000 with substance abuse disorders, may have gone without needed behavioral health services in 2010. This level of need would have required more than 11,600 full time providers. This translates to approximately 0.9 licensed mental health provider FTE, 0.1 FTE psychiatrist, 0.4 FTE other mental health staff, and 0.3 FTE substance abuse provider per 2,500 patients. These estimates suggest that 90% of current centers could not access mental health services or provide substance abuse services to fully meet patients' needs in 2010. If needs are similar after health center expansion, more than 27,000 full time behavioral health providers will be needed to serve 40 million medical patients, and grantees will need to increase behavioral health staff more than four-fold. CONCLUSIONS: More behavioral health is seen in primary care than in any other setting, and health center clients have greater behavioral health needs than typical primary care patients. Most health centers needed additional behavioral health services in 2010, and this need will be magnified to serve 40 million patients. Further testing of these workforce models are needed, but the degree of current underservice suggests that we cannot wait to move on closing the gap.

Family physicians in the maternity care workforce: factors influencing declining trends.

Family physicians provide access to maternity care for a disproportionate share of rural and urban underserved communities. This paper aims to determine trends in maternity care provision by family physicians and the characteristics of family physicians that provide maternity care. We used American Board of Family Medicine survey data collected from every family physician during application for the Maintenance of Certification Examination to determine the percentage of family physicians that provided maternity care from 2000 to 2010. Using a cross-sectional study design, logistic regression analysis was performed to examine association between maternity care provision and various physician demographic and practice characteristics. Maternity care provision by family physicians declined from 23.3 % in 2000 to 9.7 % in 2010 (p < 0.0001). Family physicians who were female, younger and US medical graduates were more likely to practice maternity care. Practicing in a rural setting (OR = 2.2; 95 % CL 2.1-2.4), an educational setting (OR = 6.4; 95 % CL 5.7-7.1) and in either the Midwest (OR = 2.6; 95 % CL 2.3-2.9) or West (OR = 2.3; 95 % CL 2.1-2.6) were the strongest predictors of higher likelihood of providing maternity care. While family physicians continue to play an important role in providing maternity care in many parts of the United States, the steep decline in the percentage of family physicians providing maternity care is concerning. Formal collaborations with midwives and obstetrician-gynecologists, malpractice reform, payment changes and graduate medical education innovations are potential avenues to explore to ensure access to maternity care.

Trends in physician supply and population growth.

The physician workforce has steadily grown faster than the U.S. population over the past 30 years, context that is often absent in conversations anticipating physician scarcity. Policy makers addressing future physician shortages should also direct resources to ensure specialty and geographic distribution that best serves population health .

How Do Family Physicians Document Patients' Social Needs in Electronic Health Records?

Social needs are critical determinants of patient health, but their capture in clinical records began recently. A representative survey of family physicians showed that 61% of respondents document social needs using notes, with fewer using diagnosis codes or electronic forms. This preference for unstructured documentation may make it difficult to connect patients across organizations or for policymakers and planners to identify geographic variation in needs.

Interoperability among hospitals treating populations that have been marginalized.

OBJECTIVE: To test whether differences in hospital interoperability are related to the extent to which hospitals treat groups that have been economically and socially marginalized. DATA SOURCES AND STUDY SETTING: Data on 2393 non-federal acute care hospitals in the United States from the American Hospital Association Information Technology Supplement fielded in 2021, the 2019 Medicare Cost Report, and the 2019 Social Deprivation Index. STUDY DESIGN: Cross-sectional analysis. DATA COLLECTION/EXTRACTION METHODS: We identified five proxy measures related to marginalization and assessed the relationship between those measures and the likelihood that hospitals engaged in all four domains of interoperable information exchange and participated in national interoperability networks in cross-sectional analysis. PRINCIPAL FINDINGS: In unadjusted analysis, hospitals that treated patients from zip codes with high social deprivation were 33% less likely to engage in interoperable exchange (Relative Risk = 0.67, 95% CI: 0.58-0.76) and 24% less likely to participate in a national network than all other hospitals (RR = 0.76; 95% CI: 0.66-0.87). Critical Access Hospitals (CAH) were 24 percent less likely to engage in interoperable exchange (RR = 0.76; 95% CI: 0.69-0.83) but not less likely to participate in a national network (RR = 0.97; 95% CI: 0.88-1.06). No difference was detected for 2 measures (high Disproportionate Share Hospital percentage and Medicaid case mix) while 1 was associated with a greater likelihood to engage (high uncompensated care burden). The association between social deprivation and interoperable exchange persisted in an analysis examining metropolitan and rural areas separately and in adjusted analyses accounting for hospital characteristics. CONCLUSIONS: Hospitals that treat patients from areas with high social deprivation were less likely to engage in interoperable exchange than other hospitals, but other measures were not associated with lower interoperability. The use of area deprivation data may be important to monitor and address hospital clinical data interoperability disparities to avoid related health care disparities.