Faculty Bibliography

BACKGROUND:Currently, only about half of U.S. adults achieve current physical activity guidelines. Routine physical activity is not regularly assessed, nor are patients routinely counseled by their health care provider on achieving recommended levels. The three-question physical activity vital sign (PAVS) was developed to assess physical activity duration and intensity and identify adults not meeting physical activity guidelines. Clinical decision support provided via a best practice advisory in an electronic health record (EHR) system can be triggered as a prompt, reminding health care providers to implement the best practice intervention when appropriate. Remote patient monitoring of physical activity can provide objective data in the EHR. OBJECTIVES/OBJECTIVE:This study aimed to evaluate the feasibility and clinical utility of embedding the PAVS and a triggered best practice advisor into the EHR in an ambulatory preventive cardiology practice setting to alert providers to patients reporting low physical activity and prompt health care providers to counsel these patients as needed. METHODS:Three components based in the EHR were integrated for the purpose of this study: patients completed the PAVS through their electronic patient portal prior to an office visit; a best practice advisory was created to prompt providers to counsel patients who reported low levels of physical activity; and remote patient monitoring via Fitbit synced to the EHR provided objective physical activity data. The intervention was pilot-tested in the Epic EHR for 1 year (July 1, 2021-June 30, 2022). Qualitative feedback on the intervention from both providers and patients was obtained at the completion of the study. RESULTS:Monthly assessments of the use of the PAVS and best practice advisory and remote patient monitoring were completed. Patients' completion of the PAVS varied from 35% to 48% per month. The best practice advisory was signed by providers between 2% and 65% and was acknowledged by 2% to 22% per month. The majority (58%) of patients were able to sync a Fitbit device to their EHR for remote monitoring. DISCUSSION/CONCLUSIONS:Although uptake of each component needs improvement, this pilot demonstrated the feasibility of incorporating a PA promotion intervention into the EHR. Qualitative feedback provided guidance for future implementation.

In the United States, it is estimated that 0.3% of Americans aged 65 and older, or almost 172,000 individuals, identify as transgender. Aging comes with a unique set of challenges and experiences for this population, including health care disparities, mental health concerns, and social isolation. It is crucial for clinicians to use a patient-centered and trauma-informed care approach to address their specific needs and provide evidence-based quality health care, including preventive screenings, mental health support, and advocating for legal protections.

Precision education (PE) uses personalized educational interventions to empower trainees and improve learning outcomes. While PE has the potential to represent a paradigm shift in medical education, a theoretical foundation to guide the effective implementation of PE strategies has not yet been described. Here, the authors introduce a theoretical foundation for the implementation of PE, integrating key learning theories with the digital tools that allow them to be operationalized. Specifically, the authors describe how the master adaptive learner (MAL) model, transformative learning theory, and self-determination theory can be harnessed in conjunction with nudge strategies and audit and feedback dashboards to drive learning and meaningful behavior change. The authors also provide practical examples of these theories and tools in action by describing precision interventions already in use at one academic medical center, concretizing PE's potential in the current clinical environment. These examples illustrate how a firm theoretical grounding allows educators to most effectively tailor PE interventions to fit individual learners' needs and goals, facilitating efficient learning and, ultimately, improving patient and health system outcomes.

BACKGROUND:Electronic health record (EHR) tools can improve prescribing of guideline-recommended therapies for heart failure with reduced ejection fraction (HFrEF), but their effectiveness may vary by physician workload. OBJECTIVES/OBJECTIVE:This paper aims to assess whether physician workload modifies the effectiveness of EHR tools for HFrEF. METHODS:This was a prespecified subgroup analysis of the BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure) cluster-randomized trial, which compared effectiveness of an alert vs message vs usual care on prescribing of mineralocorticoid antagonists (MRAs). The trial included adults with HFrEF seen in cardiology offices who were eligible for and not prescribed MRAs. Visit volume was defined at the cardiologist-level as number of visits per 6-month study period (high = upper tertile vs non-high = remaining). Analysis at the patient-level used likelihood ratio test for interaction with log-binomial models. RESULTS:Among 2,211 patients seen by 174 cardiologists, 932 (42.2%) were seen by high-volume cardiologists (median: 1,853; Q1-Q3: 1,637-2,225 visits/6 mo; and median: 10; Q1-Q3: 9-12 visits/half-day). MRA was prescribed to 5.5% in the high-volume vs 14.8% in the non-high-volume groups in the usual care arm, 10.3% vs 19.6% in the message arm, and 31.2% vs 28.2% in the alert arm, respectively. Visit volume modified treatment effect (P for interaction = 0.02) such that the alert was more effective in the high-volume group (relative risk: 5.16; 95% CI: 2.57-10.4) than the non-high-volume group (relative risk: 1.93; 95% CI: 1.29-2.90). CONCLUSIONS:An EHR-embedded alert increased prescribing by >5-fold among patients seen by high-volume cardiologists. Our findings support use of EHR alerts, especially in busy practice settings. (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure [BETTER CARE-HF]; NCT05275920).

BACKGROUND:Over a third of US adults carry a diagnosis of prediabetes, 70% of whom may progress to type 2 diabetes mellitus ("diabetes"). Community health workers (CHWs) can help patients undertake healthy behavior to prevent diabetes. However, there is limited guidance to integrate CHWs in primary care, specifically to address CHWs' dual clinic-based and community-oriented role. OBJECTIVE:Using evidence from CHWs' adaptations of a diabetes-prevention intervention in safety-net hospitals in New York City, we examine the nature, intent, and possible consequences of CHWs' actions on program fidelity. We propose strategies for integrating CHWs in primary care. DESIGN/METHODS:Case study drawing on the Model for Adaptation Design and Impact (MADI) to analyze CHWs' actions during implementation of CHORD (Community Health Outreach to Reduce Diabetes), a cluster-randomized pragmatic trial (2017-2022) at Manhattan VA and Bellevue Hospital. PARTICIPANTS/METHODS:CHWs and clinicians in the CHORD study, with a focus in this analysis on CHWs. APPROACH/METHODS:Semi-structured interviews and focus group discussion with CHWs (n=4); semi-structured interviews with clinicians (n=17). Interpretivist approach to explain CHWs' adaptations using a mix of inductive and deductive analysis. KEY RESULTS/RESULTS:CHWs' adaptations extended the intervention in three ways: by extending social assistance, healthcare access, and operational tasks. The adaptations were intended to improve fit, reach, and retention, but likely had ripple effects on implementation outcomes. CHWs' focus on patients' complex social needs could divert them from judiciously managing their caseload. CONCLUSIONS:CHWs' community knowledge can support patient engagement, but overextension of social assistance may detract from protocolized health-coaching goals. CHW programs in primary care should explicitly delineate CHWs' non-health support to patients, include multiprofessional teams or partnerships with community-based organizations, establish formal communication between CHWs and clinicians, and institute mechanisms to review and iterate CHWs' work to resolve challenges in their community-oriented role.

Medications for opioid use disorder (MOUD) increase retention in care and decrease mortality during active treatment; however, information about the comparative effectiveness of different forms of MOUD is sparse. Observational comparative effectiveness studies are subject to many types of bias; a robust framework to minimize bias would improve the quality of comparative effectiveness evidence. This paper discusses the use of target trial emulation as a framework to conduct comparative effectiveness studies of MOUD with administrative data. Using examples from our planned research project comparing buprenorphine-naloxone and extended-release naltrexone with respect to the rates of MOUD discontinuation, we provide a primer on the challenges and approaches to employing target trial emulation in the study of MOUD.

The transitional period between hospital discharge and primary care follow-up is a vulnerable time for patients that can result in adverse health outcomes and preventable hospital readmissions. This is especially true for patients of safety-net hospitals (SNHs) who often struggle to secure primary care access when leaving the hospital due to social, economic and cultural barriers. In this study, we describe a resident-led postdischarge clinic that serves patients discharged from NYU Langone Hospital-Brooklyn, an urban safety-net academic hospital. In our multivariable analysis, there was no statistical difference in the readmission rate between those who completed the transitional care management and those who did not (OR 1.32 (0.75-2.36), p=0.336), but there was a statistically significant increase in primary care provider (PCP) engagement (OR 0.53 (0.45-0.62), p<0.001). Overall, this study describes a postdischarge clinic model embedded in a resident clinic in an urban SNH that is associated with increased PCP engagement, but no reduction in 30-day hospital readmissions.

INTRODUCTION/BACKGROUND:Concurrent electronic nicotine delivery system (ENDS) and cigarette (dual) use is harmful. Identifying longitudinal trajectories of ENDS and cigarette use among dual users can help to determine the public health impact of ENDS and inform tobacco control policies and interventions. OBJECTIVES/OBJECTIVE:(1) To identify independent and joint trajectories of ENDS and cigarette use among wave (W) 1 adult dual users across W1 to W5 of the Population Assessment of Tobacco and Health (PATH) Study; and (2) identify W1 predictors of ENDS and cigarette joint trajectory group membership. METHODS:We used group-based trajectory modelling to estimate independent and joint trajectories of ENDS and cigarette use from wave 1 (W1; 2013-2014) to wave 5 (W5; 2018-2019) among W1 adult established dual users of ENDS and cigarettes (n=545) from the PATH Study. We used multinomial logistic regression to identify W1 predictors of joint trajectories. RESULTS:Two ENDS (early quitters=66.0%, stable users=34.0%) and three cigarette (stable users=55.2%, gradual quitters=27.3%, early quitters=17.5%) trajectories of W1 were identified. In joint trajectory analysis, 41.6% of participants were early ENDS quitters and stable cigarette users; 14.8% early ENDS quitters and gradual cigarette quitters; 14.6% stable ENDS users and stable cigarette users; 11.2% stable ENDS users and gradual cigarette quitters; 10.3% early ENDS quitters and early cigarette quitters; and 7.4% stable ENDS users and early cigarette quitters. Cigarette and ENDS use frequency, nicotine dependence, cannabis use and other non-combusted tobacco product use predicted trajectory group membership (p values <0.05). CONCLUSIONS:Most dual users maintained long-term cigarette smoking or dual use, highlighting the need to address cessation of both products. Continued monitoring of trajectories and their predictors is needed, given ongoing changes to the ENDS marketplace.

BACKGROUND:Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE:This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS:We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS:Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS:ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.