Family Medicine最新文献

Background and objectives: In 2023, the Accreditation Council for Graduate Medical Education added participation within a "learning collaborative" or "learning network" (LN) as a requirement for family medicine residencies. The structure and scope of what makes an acceptable LN was only vaguely defined. The purpose of this study was to learn how many family medicine residencies associated with departments already belong to LNs, the purpose and funding of these existing LNs, and barriers to entering LNs.

Methods: An online survey was sent to family medicine department chairs through a Council of Academic Family Medicine Educational Research Alliance omnibus study from August to September 2023. Survey questions explored the purpose, structure, and funding of LNs that associated residency programs already belonged to as well as the chairs' beliefs and knowledge about LNs.

Results: Of the 227 chairs, 119 completed the survey (50.2%). About 53% reported that their department was part of an LN, with more than one-third belonging for 5 years or less; 47% had a low understanding of what an LN is; and 71% had little to no concern that collaborating in an LN would negatively affect residency recruitment. The purpose of most LNs was a mix of research, education, and clinical activities. Faculty's lack of knowledge about LNs and lack of time were the top barriers identified to joining an LN. Funding was varied, and departmental funding was positively associated with administrative control of the LN.

Conclusions: About half of the residency programs associated with family medicine departments already belong to LNs. Wide variation among existing LNs may lead to significantly disparate outcomes for residents and residencies as they navigate this new requirement.

Background and objectives: Integrated behavioral health (IBH) delivered in primary care is critical to addressing the growing behavioral health crisis in the United States. COVID-19 prompted changes to the core components of IBH, causing the model to shift. The specifics of how IBH teams adapted and what these adaptations mean for the future of IBH teams in primary care are uncertain.

Methods: We conducted individual interviews with IBH team members using a semistructured interview guide. A purposive convenience sample consisted of primary care clinicians (N=20) from nine states. We used qualitative thematic analysis to code and generate themes.

Results: Four themes emerged: (a) permanent changes to the physical structure of the team; (b) increased reliance on technology for team communication; (c) shift in team collaboration, often occurring asynchronously; and (d) telehealth embraced for IBH.

Conclusions: COVID-19 interrupted the originally designed IBH model of team-based care. Changes to the physical proximity of team members disrupted all other components of IBH, requiring adapted workflows, communication via digital channels, virtual team building, asynchronous care coordination, and remote service delivery. Long-term evaluation of these innovations is needed to examine whether shifts in core components impact model efficacy. Training family medicine, primary care, and behavioral health clinicians for these adapted models of IBH will be needed.

Background and objectives: Maternal morbidity and mortality disproportionally affect marginalized populations in both rural and urban settings. While the workforce of family physicians (FPs) who provide maternity care is declining, an enhanced obstetrics (OB) curriculum during residency training can help prepare future FPs to provide competent pregnancy care, particularly in marginalized communities.

Methods: We developed an innovative OB curriculum-PROMOTE: Primary care obstetrics and maternal outcomes training enhancement-in an urban underserved residency program in Pennsylvania that directly addressed barriers previously known to impact maternity care practice. We created a clinical competency assessment aligned with Accreditation Council for Graduate Medical Education requirements, and we reviewed resident feedback and logs throughout and upon completion of the track.

Results: After 3 years of implementation, 23 of 48 (48%) total residents entered and/or completed PROMOTE, compared to 17 of 45 (38%) total residents who chose the OB track in the 5 years prior to implementation. Postimplementation, 29.6% of total graduates practice inpatient obstetrics, compared to 26.6% prior to implementation. Twice annual competency evaluations were completed for all residents on the track. Our review of resident submitted feedback, logs, and competency assessments suggests that the curriculum has positively impacted their knowledge, skills, and clinical care provision.

Conclusions: PROMOTE's curricular innovation enhances obstetrical training by addressing known facilitators and barriers to practicing family medicine obstetrics. PROMOTE was implemented in an existing family medicine residency with an obstetrics track and could be adapted by other residency programs to enhance the future maternity care workforce.

Background and objectives: Artificial intelligence (AI), such as ChatGPT and Bard, has gained popularity as a tool in medical education. The use of AI in family medicine has not yet been assessed. The objective of this study is to compare the performance of three large language models (LLMs; ChatGPT 3.5, ChatGPT 4.0, and Google Bard) on the family medicine in-training exam (ITE).

Methods: The 193 multiple-choice questions of the 2022 ITE, written by the American Board of Family Medicine, were inputted in ChatGPT 3.5, ChatGPT 4.0, and Bard. The LLMs' performance was then scored and scaled.

Results: ChatGPT 4.0 scored 167/193 (86.5%) with a scaled score of 730 out of 800. According to the Bayesian score predictor, ChatGPT 4.0 has a 100% chance of passing the family medicine board exam. ChatGPT 3.5 scored 66.3%, translating to a scaled score of 400 and an 88% chance of passing the family medicine board exam. Bard scored 64.2%, with a scaled score of 380 and an 85% chance of passing the boards. Compared to the national average of postgraduate year 3 residents, only ChatGPT 4.0 surpassed the residents' mean of 68.4%.

Conclusions: ChatGPT 4.0 was the only LLM that outperformed the family medicine postgraduate year 3 residents' national averages on the 2022 ITE, providing robust explanations and demonstrating its potential use in delivering background information on common medical concepts that appear on board exams.