J Am Board Fam Med
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The COVID-19 pandemic disrupted how primary care patients with chronic pain received care. Our study sought to understand how long-term opioid therapy (LtOT) for chronic pain changed over the course of the pandemic overall and for different demographic subgroups. ⋯ The use of LtOT for chronic pain in primary care has increased from before to after the COVID-19 pandemic with racial/ethnic and geographic disparities. Future research is needed to understand these disparities in LtOT and their effect on patient outcomes.
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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. ⋯ 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.
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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. ⋯ 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.
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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.
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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. ⋯ 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.