Faculty Bibliography

INTRODUCTION/BACKGROUND:E-cigarettes (ECs) may be an effective harm reduction strategy for individuals with conditions like chronic obstructive pulmonary disease (COPD), asthma, coronary artery disease (CAD), and peripheral arterial disease (PAD) who smoke combustible cigarettes (CCs). Our aim was to examine how individuals with chronic conditions transition from CCs to ECs and its impact on health outcomes. METHODS:In a pilot randomized controlled trial (RCT), patients with COPD, asthma, CAD/PAD who currently smoke CCs and have not used nicotine replacement therapy (NRT) or ECs in the past 14 days were randomized to receive ECs or combination NRT with behavioral counselling. Disease symptoms, acceptability/satisfaction (TSQM-9) and feasibility, and cigarettes per day (CPD), and/or EC use were collected at baseline, 3-, and 6-months. Descriptive statistics and a linear regression were conducted to explore changes in CPD and chronic condition-specific assessments (CAT, SAQ-7, ACT) that assess COPD, asthma, and CAD/PAD symptom change. RESULTS:At 3-months, the EC group (n=63, mean CPD=9±11) reduced their CPD by 54% vs. 60% in the NRT group (n=58, mean CPD=7±6), p=0.56. At 6-months, 17.5% had switched completely to ECs while 23% quit smoking in the NRT arm. CAT scores showed a significant 6-point reduction in the EC arm (p=0.03). Participants scored an average of 69±27 for EC effectiveness, 87±23 for convenience, and 75±27 for overall satisfaction. CONCLUSIONS:This pilot study suggests that ECs may be a safer alternative for chronic condition patients using CCs and warrants further research on expected smoking cessation/reduction among individuals who use ECs. IMPLICATIONS/CONCLUSIONS:The findings from this pilot RCT hold significant implications with chronic conditions such as COPD, asthma, CAD and PAD who smoke CCs. The observed reduction in cigarettes per day and improvement in respiratory symptoms suggest that switching to ECs appears feasible and acceptable among those with chronic diseases. These results suggest that ECs may offer an alternative for individuals struggling to quit CC smoking through existing pharmacotherapies. This study supports further exploration of switching to ECs as a harm reduction strategy among CC users who have been unsuccessful at quitting by other means.

INTRODUCTION/BACKGROUND:Failure to document colonoscopy follow-up needs postpolypectomy can lead to delayed detection of colorectal cancer (CRC). Automating the update of a unified follow-up date in the electronic health record (EHR) may increase the number of patients with guideline-concordant CRC follow-up screening. METHODS:Prospective pre-post design study of an automated rules engine-based tool using colonoscopy pathology results to automate updates to documented CRC screening due dates was performed as an operational initiative, deployed enterprise-wide May 2023. Participants were aged 45-75 years who received a colonoscopy November 2022 to November 2023. Primary outcome measure is rate of updates to screening due dates and proportion with recommended follow-up < 10 years. Multivariable log-binomial regression was performed (relative risk, 95% confidence intervals). RESULTS:Study population included 9,824 standard care and 19,340 intervention patients. Patients had a mean age of 58.6 ± 8.6 years and were 53.4% female, 69.6% non-Hispanic White, 13.5% non-Hispanic Black, 6.5% Asian, and 4.6% Hispanic. Postintervention, 46.7% of follow-up recommendations were updated by the rules engine. The proportion of patients with a 10-year default follow-up frequency significantly decreased (88.7%-42.8%, P < 0.001). The mean follow-up frequency decreased by 1.9 years (9.3-7.4 years, P < 0.001). Overall likelihood of an updated follow-up date significantly increased (relative risk 5.62, 95% confidence intervals: 5.30-5.95, P < 0.001). DISCUSSION/CONCLUSIONS:An automated rules engine-based tool has the potential to increase the accuracy of colonoscopy follow-up dates recorded in patient EHR. The results emphasize the opportunity for more automated and integrated solutions for updating and maintaining EHR health maintenance activities.

OBJECTIVE/UNASSIGNED:Our objective was to determine the feasibility and preliminary efficacy of a behavioral nudge on adoption of a clinical decision support (CDS) tool. MATERIALS AND METHODS/UNASSIGNED:We conducted a pilot cluster nonrandomized controlled trial in 2 Emergency Departments (EDs) at a large academic healthcare system in the New York metropolitan area. We tested 2 versions of a CDS tool for pulmonary embolism (PE) risk assessment developed on a web-based electronic health record-agnostic platform. One version included behavioral nudges incorporated into the user interface. RESULTS/UNASSIGNED: < .001). DISCUSSION/UNASSIGNED:We demonstrated feasibility and preliminary efficacy of a PE risk prediction CDS tool developed using insights from behavioral science. The tool is well-positioned to be tested in a large randomized clinical trial. TRIAL REGISTRATION/UNASSIGNED:Clinicaltrials.gov (NCT05203185).

Prostate-specific antigen (PSA)-based prostate cancer screening is a preference-sensitive decision for which experts recommend a shared decision making (SDM) approach. This study aimed to examine PSA screening SDM in primary care. Methods included qualitative analysis of audio-recorded patient-provider interactions supplemented by quantitative description. Participants included 5 clinic providers and 13 patients who were: (1) 40-69 years old, (2) Black, (3) male, and (4) attending clinic for routine primary care. Main measures were SDM element themes and "observing patient involvement in decision making" (OPTION) scoring. Some discussions addressed advantages, disadvantages, and/or scientific uncertainty of screening, however, few patients received all SDM elements. Nearly all providers recommended screening, however, only 3 patients were directly asked about screening preferences. Few patients were asked about prostate cancer knowledge (2), urological symptoms (3), or family history (6). Most providers discussed disadvantages (80%) and advantages (80%) of PSA screening. Average OPTION score was 25/100 (range 0-67) per provider. Our study found limited SDM during PSA screening consultations. The counseling that did take place utilized components of SDM but inconsistently and incompletely. We must improve SDM for PSA screening for diverse patient populations to promote health equity. This study highlights the need to improve SDM for PSA screening.

INTRODUCTION/UNASSIGNED:Depressive symptoms are linked with pain, anxiety, and substance use. Research estimating whether a reduction in depressive symptoms is linked to subsequent reductions in pain and anxiety symptoms and substance use is limited. METHODS/UNASSIGNED:Using data from the Veterans Aging Cohort Study, a multisite observational study of U.S. veterans, the authors used a target trial emulation framework to compare individuals with elevated depressive symptoms (Patient Health Questionnaire-9 score ≥ 10) who experienced reductions in depressive symptoms (Patient Health Questionnaire-9 score < 10) with those whose symptoms persisted (Patient Health Questionnaire-9 score ≥ 10) at the next follow-up visit (on average, 1 year later). Using inverse probability of treatment weighting, the authors estimated ORs and 95% CIs for associations between depressive symptom reduction status and improvement on the following: anxiety symptoms, pain symptoms, unhealthy alcohol use, and use of tobacco, cannabis, cocaine, and/or illicit opioids. RESULTS/UNASSIGNED:Reductions in depressive symptoms were associated with reductions in pain symptoms (OR=1.43, 95% CI=1.01, 2.02), anxiety symptoms (OR=2.50, 95% CI=1.63, 3.83), and illicit opioid use (OR=2.07, 95% CI=1.13, 3.81). Depressive symptom reductions were not associated with reductions in unhealthy alcohol use (OR=0.85, 95% CI=0.48, 1.52) or use of tobacco (OR=1.49, 95% CI=0.89, 2.48), cannabis (OR=1.07, 95% CI=0.63, 1.83), or cocaine (OR=1.28, 95% CI=0.73, 2.24). CONCLUSIONS/UNASSIGNED:Reducing depressive symptoms may potentially reduce pain and anxiety symptoms and illicit opioid use. Future work should determine whether reductions achieved through antidepressant medications, behavioral therapy, or other means have comparable impact.

OBJECTIVE:Both diabetes and smoking significantly increase the risk of cardiovascular disease (CVD). Understanding whether a diagnosis of diabetes can be leveraged to promote smoking cessation is a gap in the literature. METHODS:We used data from the US National Health Interview Survey, 2006 to 2018, to investigate the relationship between self-report of diagnosis of diabetes and subsequent smoking abstinence among 142,884 respondents who reported regular smoking at baseline. Effect sizes were presented as hazard ratios (HRs) derived from multivariable Cox regression models adjusted for potential confounders using diabetes as a time-dependent covariate. Subgroup-specific estimates were obtained using interaction terms between diabetes and variables of interest. RESULTS:A self-reported diagnosis of diabetes was associated with smoking abstinence (HR: 1.21; 95% CI: 1.16 to 1.27). The strength of the association varied based on race (P for interaction: 0.004), where it was strongest in African Americans (HR: 1.44; 95% CI: 1.29 to 1.60); income (P for interaction <0.001), where it was strongest in those with a yearly income less than $35,000 (HR: 1.45; 95% CI: 1.36 to 1.53); and educational attainment (P for interaction <0.001), where it was strongest in those who did not attend college (HR: 1.48; 95% CI: 1.40 to 1.57). CONCLUSION/CONCLUSIONS:Among adults who smoke, a diagnosis of diabetes is significantly associated with subsequent smoking abstinence. The association is strongest in socially disadvantaged demographics, including African Americans, low-income individuals, and those who did not attend college.

INTRODUCTION/BACKGROUND:Six hospitals within the New York City public hospital system implemented the Consult for Addiction Treatment and Care in Hospitals (CATCH) program, an interprofessional addiction consult service. A stepped-wedge cluster randomized controlled trial tested the effectiveness of CATCH for increasing initiation and engagement in post-discharge medication for opioid use disorder (MOUD) treatment among hospital patients with opioid use disorder (OUD). The objective of this study was to identify facility characteristics that were associated with stronger performance of CATCH. METHODS:This study used a mixed methods multiple-case study design. The six hospitals in the CATCH evaluation were each assigned a case rating according to intervention reach. Reach was considered high if ≥50 % of hospitalized OUD patients received an MOUD order. Cross-case rating comparison identified attributes of high-performing hospitals and inductive and deductive approaches were used to identify themes. RESULTS:Higher-performing hospitals exhibited attributes that were generally absent in lower-performing hospitals, including (1) complete medical provider staffing; (2) designated office space and resources for CATCH; (3) existing integrated OUD treatment resources; and (4) limited overlap between the implementation period and COVID-19 pandemic. CONCLUSIONS:Hospitals with attributes indicative of awareness and integration of OUD services into general care were generally higher performing than hospitals that had siloed OUD treatment programs. Future implementations of addiction consult services may benefit from an increased focus on hospital- and community-level buy-in and efforts to integrate MOUD treatment into general care.

IMPORTANCE/UNASSIGNED:Increasing numbers of unaffected individuals could benefit from genetic evaluation for inherited cancer susceptibility. Automated conversational agents (ie, chatbots) are being developed for cancer genetics contexts; however, randomized comparisons with standard of care (SOC) are needed. OBJECTIVE/UNASSIGNED:To examine whether chatbot and SOC approaches are equivalent in completion of pretest cancer genetic services and genetic testing. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This equivalence trial (Broadening the Reach, Impact, and Delivery of Genetic Services [BRIDGE] randomized clinical trial) was conducted between August 15, 2020, and August 31, 2023, at 2 US health care systems (University of Utah Health and NYU Langone Health). Participants were aged 25 to 60 years, had had a primary care visit in the previous 3 years, were eligible for cancer genetic evaluation, were English or Spanish speaking, had no prior cancer diagnosis other than nonmelanoma skin cancer, had no prior cancer genetic counseling or testing, and had an electronic patient portal account. INTERVENTION/UNASSIGNED:Participants were randomized 1:1 at the patient level to the study groups at each site. In the chatbot intervention group, patients were invited in a patient portal outreach message to complete a pretest genetics education chat. In the enhanced SOC control group, patients were invited to complete an SOC pretest appointment with a certified genetic counselor. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Primary outcomes were completion of pretest cancer genetic services (ie, pretest genetics education chat or pretest genetic counseling appointment) and completion of genetic testing. Equivalence hypothesis testing was used to compare the study groups. RESULTS/UNASSIGNED:This study included 3073 patients (1554 in the chatbot group and 1519 in the enhanced SOC control group). Their mean (SD) age at outreach was 43.8 (9.9) years, and most (2233 of 3063 [72.9%]) were women. A total of 204 patients (7.3%) were Black, 317 (11.4%) were Latinx, and 2094 (75.0%) were White. The estimated percentage point difference for completion of pretest cancer genetic services between groups was 2.0 (95% CI, -1.1 to 5.0). The estimated percentage point difference for completion of genetic testing was -1.3 (95% CI, -3.7 to 1.1). Analyses suggested equivalence in the primary outcomes. CONCLUSIONS AND RELEVANCE/UNASSIGNED:The findings of the BRIDGE equivalence trial support the use of chatbot approaches to offer cancer genetic services. Chatbot tools can be a key component of sustainable and scalable population health management strategies to enhance access to cancer genetic services. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT03985852.

OBJECTIVES/OBJECTIVE:To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD. MATERIALS AND METHODS/METHODS:Combination symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring. RESULTS:ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability. DISCUSSION/CONCLUSIONS:The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias. CONCLUSION/CONCLUSIONS:These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care.

Health care delivery is undergoing an accelerated period of digital transformation, spurred in part by the COVID-19 pandemic and the use of "virtual-first" care delivery models such as telemedicine. Medical education has responded to this shift with calls for improved digital health training, but there is as yet no universal understanding of the needed competencies, domains, and best practices for teaching these skills. In this paper, we argue that a "digital determinants of health" (DDoH) framework for understanding the intersections of health outcomes, technology, and training is critical to the development of comprehensive digital health competencies in medical education. Much like current social determinants of health models, the DDoH framework can be integrated into undergraduate, graduate, and professional education to guide training interventions as well as competency development and evaluation. We provide possible approaches to integrating this framework into training programs and explore priorities for future research in digitally-competent medical education.