Annals of Family Medicine最新文献

Purpose: Identifying cardiovascular disease before conception and in early pregnancy can better inform obstetric cardiovascular care. Our main objective was to evaluate the diagnostic performance of artificial intelligence (AI)-enabled digital tools for detecting left ventricular systolic dysfunction (LVSD) among women of reproductive age.

Methods: In a pilot cross-sectional study, we enrolled an initial cohort of 100 consecutive women aged 18-49 years who had a primary care physician and a scheduled echocardiography at Mayo Clinic Florida (Jacksonville) (cohort 1). Twelve-lead electrocardiography (ECG) and digital stethoscope recordings (single-lead ECG + phonocardiography) were performed on the date of echocardiography. We used deep learning to generate prediction probabilities for LVSD (defined as left ventricular ejection fraction <50%) for the 12-lead ECG (AI-ECG) and stethoscope (AI-stethoscope) recordings. In a second cohort of 100 participants, we enrolled consecutive women seen in primary care to estimate the prevalence of positive AI screening results when deployed for routine use (cohort 2).

Results: The median age of participants was 38.6 years (quartile 1: 30.3 years, quartile 3: 45.5 years), and 71.9% identified as part of the non-Hispanic White population. Among cohort 1, 5% had LVSD. The AI-ECG had an area under the curve of 0.94, and the AI-stethoscope (maximum prediction across all chest locations) had an area under the curve of 0.98. Among cohort 2, the prevalence of a positive AI screen was 1% and 3.2% for AI-ECG and the AI-stethoscope, respectively.

Conclusion: We found these AI tools to be effective for the detection of cardiomyopathy associated with LVSD among women of reproductive age. These tools could potentially be useful for preconception cardiovascular evaluations.

We, as the current and immediate-past NAPCRG Trainee Committee, share our perspectives as an international and diverse group of primary care research trainees. In this essay, we discuss the challenges and opportunities for achieving a more diverse, equitable, and inclusive primary care workforce by reflecting on 2 main challenges: (1) insufficient support for underrepresented identities in medicine, and (2) inadequate integration within existing primary care teams. Within each of these challenges, we pose potential opportunities for improvement using a trainee lens.

For the past 30 years, the Agency for Healthcare Research and Quality (AHRQ) has continuously supported primary care research, funding the first ECHO grant, pioneering patient-centered medical home models, and supporting primary care practice-based research networks. Until recently, these efforts were dispersed across AHRQ's centers and difficult to recognize as a unified portfolio of work. In 2022, the National Center for Excellence in Primary Care Research (NCEPCR) was funded to act as the home for primary care research at AHRQ. NCEPCR has recently developed a mission and vision and begun to coordinate primary care research efforts across AHRQ, curate and disseminate information and materials about primary care research, build a robust primary care research workforce, and convene key primary care partners. In the future, NCEPCR plans to continue to grow its work in each of these areas and expand its role as a national hub for primary care research.

Purpose: Understanding patients' perspectives and readiness regarding deprescribing-a concept broader than mere drug cessation, encompassing dynamic interaction between patients and health care professionals-is essential for developing feasible and effective deprescribing interventions. The goal of our study was to qualitatively explore the perspectives of older adults regarding proactive deprescribing, as well as its barriers and enablers.

Methods: We conducted semistructured interviews with 20 patients in Japan aged 65 years or older who were receiving 5 or more regular medications to explore their perceptions and experiences related to deprescribing. The interviews were transcribed and the data were thematically analyzed to identify major concepts.

Results: Placing a low value on medication was an important trigger of patients' proactive attitudes toward deprescribing. Patients were open to deprescribing conversations if they trusted the prescriber. Conversely, patients who had a positive perspective on medication or considered themselves incapable of participating in decision making preferred to defer to a physician. On the basis of medication valuation, decision-making preferences, and openness to deprescribing, we developed a new typology with 5 types of patients: indifferent (15% of study patients), satisfied and risk-averse (10%), compliant (30%), fearful but passive (20%), and proactive (25%).

Conclusions: Patients' attitudes toward deprescribing varied considerably according to their medication valuation, preference for involvement in decision making, and openness to deprescribing. Focusing on patients' proactiveness and understanding these barriers and enablers is essential for patient-centered decision making and for developing strategies to optimize the appropriateness of medication.

Purpose: This study aimed to assess how agile implementation-driven iterative processes and tailored workflows can facilitate the implementation of a digital cognitive assessment (DCA) tool for patients aged 65 years or older into primary care practices.

Methods: We used agile implementation principles to integrate a DCA tool into routine workflows across 7 primary care clinics. The intervention involved a structured selection process for identifying an appropriate DCA tool, stakeholder engagement through iterative sprints (structured, time-bound cycles), and development of tailored workflows to meet clinic-specific needs. A brain health navigator role was established to support patients with positive or borderline screenings, and assist primary care clinicians with follow-up assessment. We used the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework to evaluate the intervention's performance over a 12-month period.

Results: The intervention engaged 69 (63.8%) of 108 clinicians across the 7 clinics. DCA screening was completed in 1,808 (10.8%) of 16,708 eligible visits. We selected the Linus Health Core Cognitive Evaluation tool as our DCA tool based on stakeholder evaluations. Screening workflows were tailored to each clinic. The brain health navigator received 447 referrals for further assessment of a positive or borderline screening result. Four clinics fully adopted the intervention, achieving a DCA completion rate of at least 20%, and 5 clinics were still routinely using the DCA tool at 12 months.

Conclusions: Agile implementation effectively helped integrate the DCA tool into primary care workflows. Customized workflows, stakeholder engagement, and iterative improvements were crucial for adoption and sustainability. These insights can guide future efforts for early detection and management of cognitive impairment in primary care, ultimately improving patient outcomes and easing the burden on health care professionals.

Promoting adherence to medical recommendations remains one of the oldest yet most persistent challenges of modern clinical practice. Although increasingly sympathetic to structural forces that affect health behavior, standard models frequently conceptualize nonadherence as a phenomenon of patient behavior, a self-evident quality belonging to patients that is responsible for a myriad of undesired outcomes. We contend, however, that this approach not only fails to consider the role of the clinician in the concept's origins in clinical encounters, but also has facilitated the use of adherence terms (eg, nonadherent, noncompliant, treatment resistant) as pejorative social labels to the detriment of the physician-patient relationship. Used without care, such terminology can alter the meaning assigned to patients' behaviors so that structural barriers to care such as poverty and systemic racism are reframed as problems of poor attitude or effort. This article explores the functions of adherence terms as social labels by reviewing their underlying logic in clinical settings and outlining pitfalls in the pathologization of nonadherence in research and practice. We propose the concept of adherence labeling-the assessment, classification, and dissemination of clinicians' perceptions of patients' adherence through social labels-as an alternative model to understand how adherence terms may inadvertently obstruct the care of marginalized patients.

Purpose: Primary care is the foundation of any health care system. Primary care improves the health of communities and decreases health inequities. Yet, workforce shortages have worsened in the United States. Understanding characteristics of where workforce gaps are greatest is key to developing effective workforce solutions.

Methods: We used the 2019 Virginia All-Payers Claims Database to identify primary care physicians (PCPs) and the number of distinct patients seen by each physician. These data were used to identify PCP access by census tract measured using an enhanced 2-stage floating catchment method. Guided by the Andersen model of health care utilization, we identified predisposing, enabling, need, and structural community characteristics from public data sources. We assessed associations between PCP access and these characteristics using spatial autoregressive models with lagged independent variables. Analyses were performed from 2023 to 2024.

Results: In Virginia, 56.0% of census tracts had adequate PCP access. No association was found between PCP access and predisposing factors. Multiple enabling factors (ie, marital status, education, English language proficiency) were significantly associated with PCP access. Among need measures, PCP access was only positively associated with diagnosis of depression per 1,000 residents (0.01; P <.001). Structural factors had the greatest association with access. Neighborhoods with disproportionately greater numbers of Black residents had significantly greater PCP access compared with neighborhoods with disproportionately greater numbers of White residents (-0.35; P <.05). Rural communities had less PCP access than suburban neighborhoods (-0.53; P <.001).

Conclusions: There is an inadequate primary care workforce in Virginia. Structural factors, rather than predisposing, enabling, or need factors, contribute most to PCP access. Whereas Black segregated communities might have greater PCP access, rural communities are significantly disadvantaged.