J Am Board Fam Med
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Recently it was shown that the relative lack of diagnostic interventions conducted in women mediated the negative association between female sex and diagnosed disease. However, it remains unknown whether women and men receive disease diagnoses in an equal frequency after diagnostic interventions have been performed in general practice. ⋯ We used generalized linear mixed-effect models to assess the association between diagnostic interventions and disease diagnoses when patients presented with common somatic symptoms and studied whether the association differed between female and male patients. RESULTS: In 34,268 episodes of care (61.4% female) physical examinations and specialist referrals were associated with more disease diagnoses (OR = 2.32; 95% CI = 2.17-2.49 and OR = 1.38; 95% CI = 1.27-1.49, respectively), whereas laboratory diagnostics were associated with fewer disease diagnoses (OR = 0.50; 95% CI = 0.47-0.54). Significant interaction terms showed that women presenting with back pain, tiredness, arm and/or leg symptoms and tingling extremities were provided with fewer disease diagnoses after diagnostic interventions were performed than men. We found no significant interaction term that indicated that men were provided with fewer disease diagnoses after a diagnostic intervention than women. CONCLUSION: Especially when patients present with the mentioned symptoms, general practitioners should be aware that diagnostic interventions yield fewer disease diagnoses in female patients than in men. Yet, performing fewer diagnostic interventions in women with these symptoms will further exacerbate sex differences in disease diagnoses.
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Income inequality has been associated with multiple adverse health outcomes including diabetes and obesity, with this relationship potentially mediated by limited access to primary care. We explore the association between county-level economic inequality and the primary care physician (PCP) workforce in North Carolina. ⋯ Local increases in economic inequality are associated with local decreases in PCP workforce (per capita), particularly in family medicine. Although further research is needed to identify specific reasons for the decrease, medical schools in areas with high economic inequality should consider prioritizing training of physicians in family medicine and other primary care specialties to better serve community health care needs.
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Artificial intelligence (AI) in health care is the future that is already here. Despite its potential as a transformational force for primary care, most primary care providers (PCPs) do not know what it is, how it will impact them and their patients, and what its key limitations and ethical pitfalls are. ⋯ Primary care-as the dominant force at the base of the health care pyramid, with its unrivaled interconnectedness to every part of the health system and its deep relationship with patients and communities-is the most uniquely suited specialty to lead the health care AI revolution. PCPs can advance health care AI by partnering with technologists to ensure that AI use cases are relevant and human-centered, applying quality improvement methods to health care AI implementations, and advocating for inclusive and ethical AI that combats, rather than worsens, health inequities.
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Differential item functioning (DIF) procedures flag examination questions in which examinees from different subpopulations who are of equal ability do not have the same probability of answering it correctly. Few medical certification boards employ DIF procedures because they do not collect the needed data on the examinee's race or ethnicity. This article summarizes the American Board of Family Medicine's (ABFM) combined use of DIF procedures and an expert panel to review certification questions for bias. ⋯ Using DIF procedures and panel review can improve the quality of the board certification questions and demonstrate the organization's commitment to avoid racial or ethnic bias.