What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

Small Independent Primary Care Practices Serving Socially Vulnerable Urban Populations.

PURPOSE: This mixed methods study sought to describe the extent to which family physicians in urban communities serve socially vulnerable patients and to better understand their practices, their challenges, and the structural supports that could facilitate their patient care. METHODS: We conducted a quantitative analysis of questionnaire data from 100% of US physicians recertifying for family medicine from 2017 to 2020. We conducted qualitative analysis of in-depth interviews with 22 physician owners of urban, small, independent practices who reported that the majority of their patients were socially vulnerable. RESULTS: In 2020, in urban areas across the United States, 19.3% of family physicians served in independent practices with 1 to 5 clinicians, down from 22.6% in 2017. Nearly one-half of these physicians reported that >10% of their patients were socially vulnerable. Interviews with 22 physicians who reported that the majority of their patients were socially vulnerable revealed 5 themes: (1) substantial time spent addressing access issues and social determinants of health, (2) minimal support from health care entities, such as independent practice associations and health plans, and insufficient connection to community-based organizations, (3) myriad financial challenges, (4) serious concerns about the future, and (5) deep personal commitment to serving socially vulnerable patients in independent practice. CONCLUSIONS: Small independent practices serving vulnerable patients in urban communities are surviving because deeply committed physicians are making personal sacrifices. Health equity-focused policies could decrease the burden on these physicians and bolster independent practices so that socially vulnerable patients continue to have options when seeking primary care.

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.

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.

The Association of Mobility, Social Deprivation, and Pediatric Primary Care Outcomes in Community Health Centers.

OBJECTIVE: To examine how social deprivation and residential mobility are associated with primary care use in children seeking care at community health centers (CHCs) overall and stratified by race and ethnicity. STUDY DESIGN: We used electronic health record open cohort data from 152 896 children receiving care from 15 U S CHCs belonging to the OCHIN network. Patients were aged 3-17 years, with ≥2 primary care visits during 2012-2017 and had geocoded address data. We used negative binomial regression to calculate adjusted rates of primary care encounters and influenza vaccinations relative to neighborhood-level social deprivation. RESULTS: Higher rates of clinic utilization were observed for children who always lived in highly deprived neighborhoods (RR = 1.11, 95% CI = 1.05-1.17) and those who moved from low-to-high deprivation neighborhoods (RR = 1.05, 95% CI = 1.01-1.09) experienced higher rates of CHC encounters compared with children who always lived in the low-deprivation neighborhoods. This trend was similar for influenza vaccinations. When analyses were stratified by race and ethnicity, we found these relationships were similar for Latino children and non-Latino White children who always lived in highly deprived neighborhoods. Residential mobility was associated with lower rates of primary care. CONCLUSIONS: These findings suggest that children living in or moving to neighborhoods with high levels of social deprivation used more primary care CHC services than children who lived in areas with low deprivation, but moving itself was associated with less care. Clinician and delivery system awareness of patient mobility and its impacts are important to addressing equity in primary care.

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.

The Impact of Interpersonal Continuity of Primary Care on Health Care Costs and Use: A Critical Review.

PURPOSE: Interpersonal continuity has been shown to play an essential role in primary care's salutary effects. Amid 2 decades of rapid evolution in the health care payment model, we sought to summarize the range of peer-reviewed literature relating continuity to health care costs and use, information critical to assessing the need for continuity measurement in value-based payment design. METHODS: After comprehensively reviewing prior continuity literature, we used a combination of established medical subject headings (MeSH) and key words to search PubMed, Embase, and Scopus for articles published between 2002 and 2022 on "continuity of care" and "continuity of patient care," and payor-relevant outcomes, including cost of care, health care costs, cost of health care, total cost of care, utilization, ambulatory care-sensitive conditions, and hospitalizations for these conditions. We limited our search to primary care key words, MeSH terms, and other controlled vocabulary, including primary care, primary health care, family medicine, family practice, pediatrics, and internal medicine. RESULTS: Our search yielded 83 articles describing studies that were published between 2002 and 2022. Of these, 18 studies having a total of 18 unique outcomes examined the association between continuity and health care costs, and 79 studies having a total of 142 unique outcomes assessed the association between continuity and health care use. Interpersonal continuity was associated with significantly lower costs or more favorable use for 109 of the 160 outcomes. CONCLUSIONS: Interpersonal continuity today remains significantly associated with lower health care costs and more appropriate use. Further research is needed to disaggregate these associations at the clinician, team, practice, and system levels, but continuity assessment is clearly important to designing value-based payment for primary care.

Racial/Ethnic Minority Identifying Family Physicians Are More Likely to Work in Solo Practices.

While the overall proportion of family physicians who work in solo practices has been steadily declining, Black, Hispanic/Latino, and Asian family physicians are more likely to work in these settings. Given their association with high levels of continuity and improved health outcomes, and given patient preference for racial concordance with their physicians, policy makers and payors should consider how to support family physicians in solo practice in the interest of promoting access to and quality of care for ethnic/racial minorities.

Precision Ecologic Medicine: Tailoring Care to Mitigate Impacts of Climate Change.

As recent extreme weather events demonstrate, climate change presents unprecedented and increasing health risks, disproportionately so for disadvantaged communities in the U.S. already experiencing health disparities. As patients in these frontline communities live through extreme weather events, socioeconomic and health stressors are compounded; thus, their healthcare teams will need tools to provide precision ecologic medicine approaches to their care. Many primary care teams are taking actionable steps to bring community-level socioeconomic data ("community vital signs") into electronic medical records, to facilitate tailoring care based on a given patient's circumstances. This work can be extended to include environmental risk data, thus equipping healthcare teams with an awareness of clinical and community vital signs and making them better positioned to mitigate climate impacts on health. For example, if healthcare teams can easily identify patients who have multiple chronic conditions and live in an urban heat island, they can proactively arrange to "prescribe" an air conditioner, heat pump, and/or air purifier. Or, when a severe storm/heat event/poor air quality event is predicted, they can take preemptive steps to get help to patients at high medical and socioeconomic risk, rather than waiting for them to arrive in the emergency department. Advances in health information technologies now make it technically feasible to integrate a wealth of publicly-available community-level data into EMRs. Efforts to bring this contextual data into clinical settings must be accelerated to equip healthcare teams to provide precision ecologic medicine interventions to their patients.

Interpersonal Primary Care Continuity for Chronic Conditions Is Associated with Fewer Hospitalizations and Emergency Department Visits Among Medicaid Enrollees.

BACKGROUND: Interpersonal primary care continuity or chronic condition continuity (CCC) is associated with improved health outcomes. Ambulatory care-sensitive conditions (ACSC) are best managed in a primary care setting, and chronic ACSC (CACSC) require management over time. However, current measures do not measure continuity for specific conditions or the impact of continuity for chronic conditions on health outcomes. The purpose of this study was to design a novel measure of CCC for CACSC in primary care and determine its association with health care utilization. METHODS: We conducted a cross-sectional analysis of continuously enrolled, nondual eligible adult Medicaid enrollees with a diagnosis of a CACSC using 2009 Medicaid Analytic eXtract files from 26 states. We conducted adjusted and unadjusted logistic regression models of the relationship between patient continuity status and emergency department (ED) visits and hospitalizations. Models were adjusted for age, sex, race/ethnicity, comorbidity, and rurality. We defined CCC for CACSC as at least 2 outpatient visits with any primary care physician for a CACSC in the year, and (2) more than 50% of outpatient CACSC visits with a single PCP. RESULTS: There were 2,674,587 enrollees with CACSC and 36.3% had CCC for CACSC visits. In fully adjusted models, enrollees with CCC were 28% less likely to have ED visits compared with those without CCC (aOR = 0.71, 95% CI = 0.71 - 0.72) and were 67% less likely to have hospitalization than those without CCC (aOR = 0.33, 95% CI = 0.32-0.33). CONCLUSIONS: CCC for CACSCs was associated with fewer ED visits and hospitalizations in a nationally representative sample of Medicaid enrollees.

Disaggregating Latino nativity in equity research using electronic health records.

OBJECTIVE: To develop and validate prediction models for inference of Latino nativity to advance health equity research. DATA SOURCES/STUDY SETTING: This study used electronic health records (EHRs) from 19,985 Latino children with self-reported country of birth seeking care from January 1, 2012 to December 31, 2018 at 456 community health centers (CHCs) across 15 states along with census-tract geocoded neighborhood composition and surname data. STUDY DESIGN: We constructed and evaluated the performance of prediction models within a broad machine learning framework (Super Learner) for the estimation of Latino nativity. Outcomes included binary indicators denoting nativity (US vs. foreign-born) and Latino country of birth (Mexican, Cuban, Guatemalan). The performance of these models was compared using the area under the receiver operating characteristics curve (AUC) from an externally withheld patient sample. DATA COLLECTION/EXTRACTION METHODS: Census surname lists, census neighborhood composition, and Forebears administrative data were linked to EHR data. PRINCIPAL FINDINGS: Of the 19,985 Latino patients, 10.7% reported a non-US country of birth (5.1% Mexican, 4.7% Guatemalan, 0.8% Cuban). Overall, prediction models for nativity showed outstanding performance with external validation (US-born vs. foreign: AUC = 0.90; Mexican vs. non-Mexican: AUC = 0.89; Guatemalan vs. non-Guatemalan: AUC = 0.95; Cuban vs. non-Cuban: AUC = 0.99). CONCLUSIONS: Among challenges facing health equity researchers in health services is the absence of methods for data disaggregation, and the specific ability to determine Latino country of birth (nativity) to inform disparities. Recent interest in more robust health equity research has called attention to the importance of data disaggregation. In a multistate network of CHCs using multilevel inputs from EHR data linked to surname and community data, we developed and validated novel prediction models for the use of available EHR data to infer Latino nativity for health disparities research in primary care and health services research, which is a significant potential methodologic advance in studying this population.

Competencies for the Use of Artificial Intelligence in Primary Care.

The artificial intelligence (AI) revolution has arrived for the health care sector and is finally penetrating the far-reaching but perpetually underfinanced primary care platform. While AI has the potential to facilitate the achievement of the Quintuple Aim (better patient outcomes, population health, and health equity at lower costs while preserving clinician well-being), inattention to primary care training in the use of AI-based tools risks the opposite effects, imposing harm and exacerbating inequalities. The impact of AI-based tools on these aims will depend heavily on the decisions and skills of primary care clinicians; therefore, appropriate medical education and training will be crucial to maximize potential benefits and minimize harms. To facilitate this training, we propose 6 domains of competency for the effective deployment of AI-based tools in primary care: (1) foundational knowledge (what is this tool?), (2) critical appraisal (should I use this tool?), (3) medical decision making (when should I use this tool?), (4) technical use (how do I use this tool?), (5) patient communication (how should I communicate with patients regarding the use of this tool?), and (6) awareness of unintended consequences (what are the "side effects" of this tool?). Integrating these competencies will not be straightforward because of the breadth of knowledge already incorporated into family medicine training and the constantly changing technological landscape. Nonetheless, even incremental increases in AI-relevant training may be beneficial, and the sooner these challenges are tackled, the sooner the primary care workforce and those served by it will begin to reap the benefits.

Family Medicine Residencies: How Rural Training Exposure in GME Is Associated With Subsequent Rural Practice.

Background: Rural US populations face a chronic shortage of physicians and an increasing gap in life expectancy compared to urban US populations, creating a need to understand how to increase residency graduates' desire to practice in such areas. Objective: This study quantifies associations between the amount of rural training during family medicine (FM) residencies and subsequent rural work. Methods: American Medical Association (AMA) Masterfile, AMA graduate medical education (GME) supplement, American Board of Family Medicine certification, Accreditation Council for Graduate Medical Education (ACGME), and Centers for Medicare and Medicaid Services hospital costs data were merged and analyzed. Multiple logistic regression measured associations between rural training and rural or urban practice in 2018 by all 12 162 clinically active physicians who completed a US FM residency accredited by the ACGME between 2008 and 2012. Analyses adjusted for key potential confounders (age, sex, program size, region, and medical school location and type) and clustering by resident program. Results: Most (91%, 11 011 of 12 162) residents had no rural training. A minority (14%, 1721 of 12 162) practiced in a rural location in 2018. Residents with no rural training comprised 80% (1373 of 1721) of those in rural practice in 2018. Spending more than half of residency training months in rural areas was associated with substantially increased odds of rural practice (OR 5.3-6.3). Only 4% (424 of 12 162) of residents spent more than half their training in rural locations, and only 5% (26 of 436) of FM training programs had residents training mostly in rural settings or community-based clinics. Conclusions: There is a linear gradient between increasing levels of rural exposure in FM GME and subsequent rural work.

Family Physician Income Disparities by Race and Gender.

A race and gender salary gap has been well-documented throughout the U.S. economy, but little described in primary care. Using self-reported data on the most widely distributed primary care physician specialty, we reveal lower incomes and hourly wages among Black/African American and female family physicians. The clear gradient in family physician compensation by race and gender demands further study and action to better understand and address the underlying sources of these differences.

Physician- versus practice-level primary care continuity and association with outcomes in Medicare beneficiaries.

OBJECTIVE: To compare physician-level versus practice-level primary care continuity and their association with expenditure and acute care utilization among Medicare beneficiaries and evaluate whether continuity of outpatient primary care at either/both physician or/and practice level could be useful quality measures. DATA SOURCE: Medicare Fee-For-Service claims data for community dwelling beneficiaries without end-stage renal disease who were attributed to a national random sample of primary care practices billing Medicare (2011-2017). STUDY DESIGN: Retrospective secondary data analysis at per Medicare beneficiary per year level. We used multivariable linear regression with practice-level fixed effects to estimate continuity of care score at physician versus practice level and their associations with outcomes. DATA COLLECTION/EXTRACTION METHOD: We calculated clinician- and practice-level Bice-Boxerman continuity of care index scores, ranging from 0 to 1, using primary care outpatient claims. Medicare expenditures, hospital admissions, emergency department (ED) visits, and readmissions were obtained from the Medicare Beneficiary Summary File: Cost and Utilization Segment. Ambulatory care sensitive conditions (ACSC) were defined using diagnosis codes on inpatient claims. PRINCIPAL FINDINGS: We studied 2,359,400 beneficiaries who sought care from 13,926 physicians. Every 0.1 increase in physician continuity score was associated with a $151 reduction in expenditure per beneficiary per year (p < 0.01), and every 0.1 increase in practice continuity score was associated with $282 decrease (p < 0.01) per beneficiary per year. Both physician- and practice-level continuity were associated with lower Medicare expenditures among small, medium, and large practices. Both physician- and practice-level continuity were associated with lower probabilities of hospitalization, ED visit, admissions for ACSC, and readmission. CONCLUSIONS: Primary care continuity of care could serve as a potent value-based care quality metric. Physician-level continuity is a unique value center that cannot be supplanted by practice-level continuity.

Primary Care: The Actual Intelligence Required for Artificial Intelligence to Advance Health Care and Improve Health.

With conversational agents triaging symptoms, cameras aiding diagnoses, and remote sensors monitoring vital signs, the use of artificial intelligence (AI) outside of hospitals has the potential to improve health, according to a recently released report from the National Academy of Medicine. Despite this promise, the success of AI is not guaranteed, and stakeholders need to be involved with its development to ensure that the resulting tools can be easily used by clinicians, protect patient privacy, and enhance the value of the care delivered. A crucial stakeholder group missing from the conversation is primary care. As the nation's largest delivery platform, primary care will have a powerful impact on whether AI is adopted and subsequently exacerbates health disparities. To leverage these benefits, primary care needs to serve as a medical home for AI, broaden its teams and training, and build on government initiatives and funding.

Digital Health Interventions to Enhance Prevention in Primary Care: Scoping Review.

BACKGROUND: Disease prevention is a central aspect of primary care practice and is comprised of primary (eg, vaccinations), secondary (eg, screenings), tertiary (eg, chronic condition monitoring), and quaternary (eg, prevention of overmedicalization) levels. Despite rapid digital transformation of primary care practices, digital health interventions (DHIs) in preventive care have yet to be systematically evaluated. OBJECTIVE: This review aimed to identify and describe the scope and use of current DHIs for preventive care in primary care settings. METHODS: A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and Medical Subject Headings terms covering primary care professionals, prevention and care management, and digital health. A subgroup analysis identified relevant studies conducted in US primary care settings, excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (eg, health care performance and implementation science), and study quality as per Oxford levels of evidence were abstracted. RESULTS: The search yielded 5274 citations, of which 1060 full-text articles were identified. Following a subgroup analysis, 241 articles met the inclusion criteria. Studies primarily examined DHIs among health information technologies, including EHRs (166/241, 68.9%), clinical decision support (88/241, 36.5%), telehealth (88/241, 36.5%), and multiple technologies (154/241, 63.9%). DHIs were predominantly used for tertiary prevention (131/241, 54.4%). Of the core primary care functions, comprehensiveness was addressed most frequently (213/241, 88.4%). DHI users were providers (205/241, 85.1%), patients (111/241, 46.1%), or multiple types (89/241, 36.9%). Reported outcomes were primarily clinical (179/241, 70.1%), and statistically significant improvements were common (192/241, 79.7%). Results were summarized across the following 5 topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. The quality of the included studies was moderate to low. CONCLUSIONS: Preventive DHIs in primary care settings demonstrated meaningful improvements in both clinical and nonclinical outcomes, and across user types; however, adoption and implementation in the US were limited primarily to EHR platforms, and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluations of negative results, effects on health disparities, and many other gaps remain to be explored.

Practice Patterns of Family Physicians With and Without Addiction Medicine Board Certification.

BACKGROUND: The American Board of Medical Specialties recognized addiction medicine (ADM) as a subspecialty in 2016, which was timely given the recent rise in substance use disorder (SUD). The impact of this dual board opportunity on Family Medicine has not been described. Our study enumerates and characterizes physicians dually certified in Family Medicine and ADM. METHODS: We linked American Board of Medical Specialties data from March 2020 on physicians dually boarded in Family Medicine and ADM to responses on demographic and scope of practice questions in the American Board of Family Medicine (ABFM) National Graduate Survey and Family Medicine Certification Examination Registration Questionnaire. RESULTS: Of current ABFM Diplomates, 0.53% (492/93,269) are also boarded in ADM. Based on survey responses from a subset of dually certified physicians, those who are dually certified are more likely to practice in federally qualified health centers and to hold a faculty position. Dually certified physicians are more likely to provide HIV/AIDS and hepatitis C management and are as likely as non-dually certified physicians to provide newborn care, obstetric deliveries, inpatient adult medicine care, and intensive care. DISCUSSION: While only a small proportion of family physicians carry dual ADM board certification, those that do disproportionately serve vulnerable populations while retaining broad scope of care. Further work is needed to examine whether SUD treatment access could be addressed by implementing models that support dually certified physicians in consultative and educational efforts that would amplify their impact across the primary care workforce.