Perception of the compatibility of Quebec residency program characteristics with the advanced access model: a cross-sectional study.

BACKGROUND: The advanced access (AA) model is among the most recommended innovations for improving timely access in primary care (PC). AA is based on core pillars such as comprehensive planning for care needs and supply, regularly adjusting supply to demand, optimizing appointment systems, and interprofessional collaborative practices. Exposure of family medicine residents to AA within university-affiliated family medicine groups (U-FMGs) is a promising strategy to widen its dissemination and improve access. Using four AA pillars as a conceptual model, this study aimed to determine the theoretical compatibility of Quebec's university-affiliated clinics' residency programs with the key principles of AA. METHODS: A cross-sectional online survey was sent to the chief resident and academic director at each participating clinic. An overall response rate of 96% (44/46 U-FMGs) was obtained. RESULTS: No local residency program was deemed compatible with all four considered pillars. On planning for needs and supply, only one quarter of the programs were compatible with the principles of AA, owing to residents in out-of-clinic rotations often being unavailable for extended periods. On regularly adjusting supply to demand, 54% of the programs were compatible. Most (82%) programs' appointment systems were not very compatible with the AA principles, mostly because the proportion of the schedule reserved for urgent appointments was insufficient. Interprofessional collaboration opportunities in the first year of residency allowed 60% of the programs to be compatible with this pillar. CONCLUSIONS: Our study highlights the heterogeneity among local residency programs with respect to their theoretical compatibility with the key principles of AA. Future research to empirically test the hypotheses raised by this study is warranted.

Machine Learning to Identify Clusters in Family Medicine Diplomate Motivations and Their Relationship to Continuing Certification Exam Outcomes: Findings and Potential Future Implications.

BACKGROUND: The potential for machine learning (ML) to enhance the efficiency of medical specialty boards has not been explored. We applied unsupervised ML to identify archetypes among American Board of Family Medicine (ABFM) Diplomates regarding their practice characteristics and motivations for participating in continuing certification, then examined associations between motivation patterns and key recertification outcomes. METHODS: Diplomates responding to the 2017 to 2021 ABFM Family Medicine continuing certification examination surveys selected motivations for choosing to continue certification. We used Chi-squared tests to examine difference proportions of Diplomates failing their first recertification examination attempt who endorsed different motivations for maintaining certification. Unsupervised ML techniques were applied to generate clusters of physicians with similar practice characteristics and motivations for recertifying. Controlling for physician demographic variables, we used logistic regression to examine the effect of motivation clusters on recertification examination success and validated the ML clusters by comparison with a previously created classification schema developed by experts. RESULTS: ML clusters largely recapitulated the intrinsic/extrinsic framework devised by experts previously. However, the identified clusters achieved a more equal partitioning of Diplomates into homogenous groups. In both ML and human clusters, physicians with mainly extrinsic or mixed motivations had lower rates of examination failure than those who were intrinsically motivated. DISCUSSION: This study demonstrates the feasibility of using ML to supplement and enhance human interpretation of board certification data. We discuss implications of this demonstration study for the interaction between specialty boards and physician Diplomates.

The Scope of Multimorbidity in Family Medicine: Identifying Age Patterns Across the Lifespan.

INTRODUCTION: Multimorbidity rates are both increasing in prevalence across age ranges, and also increasing in diagnostic importance within and outside the family medicine clinic. Here we aim to describe the course of multimorbidity across the lifespan. METHODS: This was a retrospective cohort study across 211,953 patients from a large northeastern health care system. Past medical histories were collected in the form of ICD-10 diagnostic codes. Rates of multimorbidity were calculated from comorbid diagnoses defined from the ICD10 codes identified in the past medical histories. RESULTS: We identify 4 main age groups of diagnosis and multimorbidity. Ages 0 to 10 contain diagnoses which are infectious or respiratory, whereas ages 10 to 40 are related to mental health. From ages 40 to 70 there is an emergence of alcohol use disorders and cardiometabolic disorders. And ages 70 to 90 are predominantly long-term sequelae of the most common cardiometabolic disorders. The mortality of the whole population over the study period was 5.7%, whereas the multimorbidity with the highest mortality across the study period was Circulatory Disorders-Circulatory Disorders at 23.1%. CONCLUSION: The results from this study provide a comparison for the presence of multimorbidity within age cohorts longitudinally across the population. These patterns of comorbidity can assist in the allocation to practice resources that will best support the common conditions that patients need assistance with, especially as the patients transition between pediatric, adult, and geriatric care. Future work examining and comparing multimorbidity indices is warranted.

Research to Improve Clinical Care in Family Medicine: Big Data, Telehealth, Artificial Intelligence, and More.

This issue highlights changes in medical care delivery since the start of the COVID-19 pandemic and features research to advance the delivery of primary care. Several articles report on the effectiveness of telehealth, including its use for hospital follow-up, medication abortion, management of diabetes, and as a potential tool for reducing health disparities. Other articles detail innovations in clinical practice, from the use of artificial intelligence and machine learning to a validated simple risk score that can support outpatient triage decisions for patients with COVID-19. Notably one article reports the impact of a voluntary program using scribes in a large health system on physician documentation behaviors and performance. One article addresses the wage gap between early-career female and male family physicians. Several articles report on inappropriate testing for common health problems; are you following recommendations for ordering Pulmonary Function Tests, mt-sDNA for colon cancer screening, and HIV testing?

The Gender Wage Gap Among Early-Career Family Physicians.

PURPOSE: Numerous studies have documented salary differences between male and female physicians. For many specialties, this wage gap has been explored by controlling for measurable factors that influence pay such as productivity, work-life balance, and practice patterns. In family medicine where practice activities differ widely between physicians, it is important to understand what measurable factors may be contributing to the gender wage gap, so that employers and policymakers and can address unjust disparities. METHODS: We used data from the 2017 to 2020 American Board of Family Medicine (ABFM) National Graduate Survey (NGS) which is administered to family physicians 3 years after residency (n = 8608; response rate = 63.9%, 56.2% female). The survey collects clinical income and practice patterns. Multiple linear regression analysis was performed, which included variables on hours worked, degree type, principal professional activity, rural/urban, and region. RESULTS: Although early-career family physician incomes averaged $225,278, female respondents reported incomes that were $43,566 (17%) lower than those of male respondents (P = .001). Generally, female respondents tended toward lower-earning principal professional activities and US regions; worked fewer hours (2.9 per week); and tended to work more frequently in urban settings. However, in adjusted models, this gap in income only fell to $31,804 (13% lower than male respondents, P = .001). CONCLUSION: Even after controlling for measurable factors such as hours worked, degree type, principal professional activity, population density, and region, a significant wage gap persists. Interventions should be taken to eliminate gender bias in wage determinations for family physicians.

Family Medicine Clinician Screening and Barriers to Communication on Food Insecurity: A CERA General Membership Survey.

PURPOSE: Food insecurity (FI) is a hidden epidemic associated with worsening health outcomes affecting 33.8 million people in the US in 2021. Although studies demonstrate the importance of health care clinician assessment of a patient's food insecurity, little is known about whether Family Medicine clinicians (FMC) discuss FI with patients and what barriers influence their ability to communicate about FI. This study evaluated FM clinicians' food insecurity screening practices to evaluate screening disparities and identify barriers that influence the decision to communicate about FI. METHODS: Data were gathered and analyzed as part of the 2022 Council of Academic Family Medicine's Educational Research Alliance survey of Family Medicine general membership. RESULTS: The majority of respondents reported (66.9%) that their practice has a screening system for food insecurity, and most practices used a verbal screen with staff other than the clinician (41%) at specific visits (63.8%). Clinicians reported "rarely or never asking about FI" 40% of the time and only asking "always or frequently" 6.7% of the time. Inadequate time during appointments (44.5%) and other medical issues taking priority (29.4%) were identified as the most common barriers. The lack of resources available in the community was a significant barrier for clinicians who worked in rural areas. CONCLUSIONS: This survey provides insight into food insecurity screening disparities and identifies obstacles to FMC screening, such as time constraints, lack of resources, and knowledge of available resources. Understanding current communication practices could create opportunities for interventions to identify food insecurity and impact "Food as Medicine."

Data Disaggregation of Asian-American Family Physicians.

The singular label of "Asian" obscures socioeconomic differences between Asian ethnic groups that affect matriculation into the field of medicine. Using data from American Board of Family Medicine Examination candidates in 2023, we found that compared to the US population, among Asian-American family physicians, Indians were present at higher rates, while Chinese and Filipinos were underrepresented, suggesting the importance of continued disaggregation of Asian ethnicities in medicine.