Annals of Family Medicine最新文献

This reflective essay recounts my transformative experience with Mrs G, a patient on the high-risk pregnancy ward, through which I explore the emotional complexities of obstetrics and gynecology. Despite her medical challenges, including polyhydramnios and a baby with potential Down syndrome, Mrs G maintained an incredibly positive outlook. Her daily resilience, combined with her husband's steadfast support, highlighted the emotional nuances of this field. Her son was born prematurely and required NICU care, but tragically passed away. This heartbreaking loss deeply affected me, emphasizing the emotional challenges faced by physicians. In the end, this experience reaffirmed my commitment to medicine, demonstrating that physicians, whether in obstetrics and gynecology or any other specialty, must navigate deeply emotional issues with empathy and dedication.

Purpose: Body mass index (BMI) is the current standard body composition measurement. We examined BMI vs body fat percentage (BF%) for 15-year mortality risk among adults aged 20-49 years.

Methods: In this nationally representative cohort of US adults in the National Health and Nutrition Examination Survey, Cox proportional hazard regression was computed for 15-year mortality risk. Healthy and overweight/obese BMI were 18.5-24.9 and ≥25 kg/m², respectively. The unhealthy BF% and waist circumference (WC) thresholds were ≥27% and ≥44%, and >40 and >35 inches, for men and women, respectively. The 15-year mortality risk for BMI, BF%, and WC was computed.

Results: Body fat percentage and WC are both significantly associated with 15-year, allcause mortality (BF%: unadjusted hazard ratio [HR] 2.01; 95% CI, 1.45-2.78; P < .001; adjusted HR 1.78; CI, 1.28-2.47; P < .001; WC: unadjusted HR 1.94; CI, 1.38-2.72; P < .001; adjusted HR 1.59; CI, 1.12-2.26; P = .009) and heart disease mortality (BF%: unadjusted HR 4.20; CI, 1.94-9.11; P < .001; adjusted HR 3.62; CI, 1.55-8.45; P = .003; WC: unadjusted HR 4.75; CI, 2.45-9.21; P < .001; adjusted HR 4.01; CI, 1.94-8.27; P < .001). Body mass index has no statistically significant relationship with all-cause mortality. Body mass index has a significant association with heart disease mortality in the unadjusted model but not in the adjusted model.

Conclusion: Body fat percentage is a better predictor of 15-year mortality risk in young adults than BMI. A move to using BF% rather than BMI may change how we measure body composition for risk stratification.

The transition to value-based care requires primary care physicians develop new capabilities in managing health services demand in ambulatory care settings. This narrative draws from clinical experience in a value-based, capitated system to examine how traditional fee-for-service incentives and changing patient expectations increase health care utilization. The essay introduces a practical "3D" framework-Delegate, Defer, Direct-for managing health services demand while maintaining quality care. Clinical examples demonstrate how primary care physicians can leverage team-based care models, implement evidence-based visit frequencies for chronic conditions, and develop systematic approaches to guide appropriate care-seeking behavior. Successfully managing demand requires physician practice innovation and patient partnership, supported by policy changes enabling team-based care delivery. As health care systems increasingly adopt value-based payment models as part of health care delivery reform, skillfully managing demand will become crucial for delivering high-quality, sustainable primary care.

Artificial intelligence and machine learning (AI/ML) in health care is accelerating at a breathtaking pace. As the largest health care delivery platform, primary care is where the power, opportunity, and future of AI/ML are most likely to be realized in the broadest and most ambitious scale. However, there is a relative lack of organized, open, large-scale primary care datasets to attract industry and academia in primary care-focused research and development. This article proposes a set of high-level considerations around the data transformation that is needed to enable the growth of AI/ML applications in primary care. These considerations call for automation of data collection, organization of fragmented data, identification of primary care-specific use cases, integration of AI/ML into human workflows, and surveillance for unintended consequences. By unlocking the power of its data, primary care can play a leading role in advancing health care AI/ML to support patients, clinicians, and the health of the nation.

Purpose: There is a critical need to treat opioid use disorder (OUD) in primary care. We describe the incidence of OUD medication treatment among primary care patients who reported opioid use and moderate or severe symptoms of substance use disorder (SUD), as defined by the Diagnostic and Statistical Manual of Mental Illnesses, Fifth Edition, Text Revision (DSM-5-TR), during routine care.

Method: This retrospective cohort study used electronic health record and insurance claims data from 33 primary care clinics in Washington that routinely screen for substance use and ask patients who report daily cannabis use or any past-year drug use to complete a DSM-5-TR Substance Use Symptom Checklist (Checklist). The sample included 1,502 adult primary care patients (from March 1, 2015 to January 1, 2023) who completed a Checklist, reported past-year opioid use, and had no recent OUD treatment. Primary outcomes were OUD medication treatment within 14 days of completing the Checklist (ie, initiation), and in the following 34 days (ie, engagement).

Results: Among 80 (5%) patients with moderate symptoms, 8 (10%) initiated and 6 (8%) remained engaged with medication treatment. These patients were significantly more likely to initiate (P < .001) and remain engaged (P = .003) compared with the 746 (50%) reporting no SUD symptoms. Among 542 (36%) patients with severe symptoms, 141 (26%) initiated and 108 (20%) engaged. These patients were also significantly more likely to initiate (P <.001) and remain engaged (P <.001) compared with those with no SUD symptoms (P = .003) or moderate SUD symptoms (P = .009).

Conclusion: Most primary care patients reporting opioid use and moderate or severe SUD symptoms did not initiate OUD treatment, but most who initiated remained engaged. Screening and assessment alone is insufficient to result in adequate OUD medication treatment.

Clinicians have a federally protected right to conscientiously refuse to provide treatment that conflicts with their core moral or religious values. The American Medical Association affirms that, among other obligations, a physician should give advance notification "before entering into a patient-physician relationship" by making "clear any specific interventions or services the physician cannot in good conscience provide" (Opinion 1.1.7). We apply this guidance to the rural health care context by considering whether giving notification of conscientious refusals is best done in advance of, or during, the clinical encounter. We conclude that giving advance notice should be the moral default in rural contexts, but giving notice during the clinical encounter can be justified where patients are especially dependent upon their primary care physician for their overall medical care.