Annals of Family Medicine最新文献

The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of chatbots driven by generative artificial intelligence when summarizing clinical evidence and providing health advice, referred to as chatbot health advice studies. CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting, and method in chatbot health advice studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary, modified, asynchronous Delphi consensus process of 531 stakeholders, 3 synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of chatbot health advice studies. These include title (subitem 1a), abstract/summary (subitem 1b), background (subitems 2a,b), model identifiers (subitems 3a,b), model details (subitems 4a-c), prompt engineering (subitems 5a,b), query strategy (subitems 6a-d), performance evaluation (subitems 7a,b), sample size (subitem 8), data analysis (subitem 9a), results (subitems 10a-c), discussion (subitems 11a-c), disclosures (subitem 12a), funding (subitem 12b), ethics (subitem 12c), protocol (subitem 12d), and data availability (subitem 12e). The CHART checklist and corresponding diagram of the method were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of chatbot health advice studies. KEY MESSAGES: CHART was developed by performing a systematic review, Delphi consensus of 531 international stakeholders, and several consensus meetings among an expert panel comprised of 48 membersThe CHART statement outlines 12 key reporting items for chatbot health advice studies in the form of a checklist and methodological diagramAll stakeholders including clinicians, researchers, and journal editors should encourage the transparent reporting of chatbot health advice studies.

Purpose: We aimed to evaluate an innovative strategy to collect family history (FH) and explore patients' views of this strategy.

Methods: We conducted a matched-pair effectiveness-implementation trial in family practices affiliated with the University of Toronto Practice-Based Research Network (UTOPIAN). The intervention group included family physicians (FPs) from randomly selected practices using electronic health records (EHRs) and an e-mailing platform, and randomly selected patients aged 30-69 years (4/FP/week) seen in clinic over a 6-month period. The matched control group included FPs (1:1) and patients (up to 5:1) from the UTOPIAN database. The intervention included patient and FP education, an e-mailed patient invitation to complete an FH questionnaire, automatic FH EHR upload, FP notification of completed FH questionnaire, and links to clinical support tools. Intervention patients were e-mailed a postvisit follow-up questionnaire. The assessed outcome was new documentation of FH in the EHR using mixed effects logistic regression and descriptive statistics for patient feedback.

Results: Fifteen FPs and 576 patients were recruited from 3 multidisciplinary team practices to the intervention group, matched to 15 FPs and 2,203 patients in the control group. Within 30 days of visit, a new FH was documented in the EHR for 93/576 (16.1%) of intervention patients compared with 5/2,203 (0.2%) control patients (adjusted OR = 94.2; 95% CI, 36.8-240.8). New cancer FH documentation was greater in the intervention group compared with the control group (7.8% vs 0.1%; P < .01). Of patients who reported discussing FH (n = 296), 24.5% reported screening test recommended, 7.5% referral to a nongenetics specialist, and 2.4% referral to a genetics specialist. Most patients (60.5%) found this FH strategy helpful.

Conclusions: This study showed improved collection/documentation of FH. Contributors to success of the intervention included being patient completed and seamless EHR integration with a reminder. This FH strategy needs tailoring to different contexts.

Purpose: Rates of participation in community-based diabetes prevention programs (DPPs) are low among patients with prediabetes. This may be due, in part, to low rates of referrals to these programs from health systems. One key opportunity to augment clinicians' referrals to and patients' participation in DPPs may be through electronic health record referrals (eReferrals).

Methods: We undertook a quality improvement initiative in a large, academic health system. Using the EpicCare Link feature of Epic (Epic Systems Corporation), we created an eReferral to local community-based DPPs. Eligibility criteria required that patients have an age of at least 18 years, a body mass index of at least 25 kg/m², and prediabetes or a history of gestational diabetes. We conducted a retrospective evaluation of implementation outcomes from June 1, 2021 to June 30, 2022 using the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) evaluation framework.

Results: During the evaluation period, 577 patients were referred to DPPs and 21% enrolled, defined as attending an information session and registering for a program. Thirty percent of 350 targeted primary care clinicians used the eReferral across 14 primary care clinics. Among all 124 referring clinicians, referral rates varied widely with a range of 1 to 46 referrals per clinician; 11% of referring clinicians contributed more than 50% of all referral orders. The large majority of referred patients (73% to 81%) met DPP eReferral eligibility criteria.

Conclusions: An eReferral is a promising, scalable strategy to connect patients with prediabetes to community DPPs. Additional strategies are needed to enhance clinicians' use of the eReferral and patients' subsequent program engagement to fully optimize the reach and effectiveness of DPPs.

Purpose: An interdisciplinary, team-based approach to delivering medical, behavioral, and supportive care can help pregnant people with substance use disorder (SUD) manage this chronic condition and care for themselves and their family. We identified the professionals, roles, and functions of these care teams.

Methods: We conducted a qualitative observational study of 7 organizations that implemented team-based SUD care for pregnant people. We observed clinical team operations and conducted semistructured interviews with leaders and care team professionals. We used an inductive and comparative approach to analyze data.

Results: The organizations varied in ownership, type (medical, SUD treatment, behavioral health) and rural-urban location. We identified 14 care team functions and organized them into 5 categories: medical care, behavioral health care, coordination and resources, support and engagement, and quality improvement leadership. Clinicians (family physicians, certified nurse midwives), registered nurses, medical assistants, licensed clinical social workers, certified alcohol and drug counselors, peer support professionals, and doulas carried out these functions. All teams provided care coordination, outreach and engagement, referral to specialists, transitional care, community resource connection, social and emotional support, and advocacy functions. Of the 4 nonmedical organizations, only 2-a behavioral health organization and an SUD treatment organization-carried out the medical care functions; one had a family physician on the team.

Conclusions: There are 14 functions that policy makers, payers, health care organization leaders, and individuals should expect from an interdisciplinary care team that is delivering whole-person SUD care to pregnant individuals. Investment in programs that train full-scope family medicine clinicians can strengthen care for people with SUD during pregnancy.

Purpose: Family physicians (FPs) are an important segment of the maternity workforce, particularly in rural areas. This research explores the geographic distribution of family physicians providing maternity care and identifies opportunities for family physicians to expand access to maternity care.

Methods: This cross-sectional study used a co-location mapping approach to identify 3 types of counties based on the following: (1) family physicians as the only clinician provider of maternity care along with at least 1 hospital providing obstetric care (FP with Hospital); (2) family physicians as the only clinician provider of maternity care with no hospital providing obstetric care (FP Only); (3) no clinician providers of maternity care but county has at least 1 hospital providing obstetric services (Only Hospital).

Results: Most of the 325 counties across the 3 types are rural and concentrated in the central United States, the upper Midwest, and in Mississippi. More than one-third of these counties are found in just 4 states-Texas, Iowa, Nebraska, and Kansas. Although there are not clear differences in the geographic distribution of FP Only and FP with Hospital counties, Only Hospital counties are located primarily in a few states, including Mississippi, Missouri, Oklahoma, and Texas, and have significantly higher percentages of Black populations.

Conclusions: Our findings demonstrate that while FPs are providing maternity care in rural areas across the United States, opportunities exist to expand their reach, particularly in Mississippi, Texas, and Oklahoma.