Faculty Bibliography

Manufacturing and use of pharmaceuticals is responsible for nearly 20% of US health care's greenhouse gas footprint. As highly effective weight-loss medications such as incretin mimetics become widely prescribed, it is important to understand environmental implications. More than 33.5 million Americans have tried incretin mimetic medications (IMMs), and nearly 30 million are projected to be consistent users by 2030. This article examines the broader potential implications of glucagon-like peptide-1 and other IMMs on climate change. We conducted a preliminary and speculative carbon footprint of IMMs using a life cycle assessment approach. Our findings suggest widespread IMM use could reduce greenhouse gas emissions by decreasing caloric consumption, food production, and health care activities, leading to a maximum estimated reduction of 760 kg CO2e/person/year. This reduction would be greater than the environmental benefits of switching to electric vehicles or adopting a vegetarian diet. This study highlights the need for more research into potential environmental benefits of IMMs.

BACKGROUND:De-implementation-reducing low-value or harmful care-is critical but difficult in clinical practice. Clinical decision support (CDS) "nudges" in electronic health records (EHRs) aim to promote guideline-concordant deprescribing, but effects are inconsistent. In a pragmatic randomized controlled trial across a large health system, we tested a suite of EHR-based CDS nudges to support Choosing Wisely-aligned deprescribing of glycemic medications in older adults with type 2 diabetes. Although a prior pilot showed modest improvement in guideline concordance (5.1%), the full trial found no significant changes in prescribing; this process evaluation examines clinicians' comments on alerts to explain why. METHODS:We conducted a mixed-methods process evaluation of comments within EHR-based alerts from a null-result RCT that promoted Choosing Wisely deprescribing for older adults with type 2 diabetes. Among 66,634 alerts firing across EHR encounters (December 2016-July 2023), providers commented on 764 (1.2%). Two researchers independently coded comments using reflexive thematic analysis, identifying four themes (three negative). Exploratory logistic and multinomial regressions examined predictors of commenting, valence, and themes among acknowledged firings, adjusting for patient, provider, and encounter factors. RESULTS:Thematic analysis of comments revealed three barriers to deprescribing: (1) disagreement with Choosing Wisely guidelines (308 comments, e.g., perceived low overtreatment risk); (2) workflow misalignment (203 comments, e.g., wrong provider responsibility); and (3) patient preferences (69 comments). Logistic regression showed multiple concurrent OPAs reduced action odds by 31.6% (OR 0.684, 95% CI 0.560-0.835); comments were 2.57 times more likely to be negative than positive (OR 2.565, 95% CI 1.637-4.018). Disparities in engagement were found, with female providers, patients, and socially vulnerable individuals less likely to comment. CONCLUSION/CONCLUSIONS:This process evaluation demonstrates scalable real-time feedback for clinical decision support refinement in de-implementation, with regressions identifying context-specific predictors. Provider disagreement, alert firings misaligned to workflows, and patient resistance hinder effectiveness. Future work should refine clinical decision support design to address complexity, enhance guideline explainability to build provider concordance, align with provider roles and workflows, and include patient-centered approaches. TRIAL REGISTRATION/BACKGROUND:The NYU School of Medicine Institutional Review Board (i17-01308) approved the trial, which has the clinicaltrials.gov ID NCT04181307 (https://clinicaltrials.gov/study/NCT04181307), with a first record date of November 26, 2019.

BACKGROUND/UNASSIGNED:The coronavirus disease 2019 (COVID-19) pandemic spurred a tremendous increase in the adoption and use of remote patient monitoring (RPM) for hypertension (HTN) management. However, limited evidence exists on the associations between frequency of utilization and uncontrolled blood pressure (BP). OBJECTIVES/UNASSIGNED:The present study comprehensively explores the associations between RPM use frequency and uncontrolled BP among a metropolitan-dwelling sample of hypertensive patients. METHODS/UNASSIGNED:Of 2,920 participants from a single urban health system, we employed a range of analytical perspectives to evaluate the RPM utilization-uncontrolled BP relationship across widely used engagement metrics: Frequency of BP transmission, digitally enabled clinician interactions, patient portal interactions, and a composite measure of utilization. Our dichotomized primary and secondary endpoints were BP >140/90 mm Hg and BP >130/80 mm Hg. RESULTS/UNASSIGNED:Fifty-nine percent of participants were females (59%), one-third (37%) were ≥65 years old, and Hispanic patients were most represented (39%). Our primary uncontrolled BP endpoint demonstrated strong adjusted associations with suboptimal RPM use across dichotomized measures: Low BP transmission (odds ratio [OR]: 2.02, 95% confidence interval [CI]: 1.41-2.96), low clinician interactions (OR: 1.83, 95% CI: 1.43-2.36), low patient portal interactions (OR: 1.83, 95% 1.46-2.30), and low overall engagement (OR: 3.50, 95% 2.77-4.46). Our causal evaluations mirrored these findings, showing moderate causal associations after comprehensive adjustment for confounding. Assessments using other data types, such as continuous and quartiles, showed significant associations and an apparent dose-response relationship, though not at a similar magnitude. CONCLUSION/UNASSIGNED:We observed strong associations between low RPM utilization and uncontrolled BP, with promising implications for patients with collectively high RPM use. These findings highlight the need to strengthen digital inclusion initiatives to improve RPM uptake and support existing efforts aimed at developing RPM clinical practice guidelines and expanding RPM reimbursement policies. Further research is warranted across diverse utilization components to better understand the linkages between engagement frequency and improved clinical outcomes.

BACKGROUND:Artificial intelligence (AI)-enabled "ambient" documentation may reduce clinician administrative burdens and improve care delivery, but implementation in clinical practice is complex. AIM/OBJECTIVE:To evaluate the implementation of commercially available ambient documentation tools in multi-specialty ambulatory clinical workflows at an academic medical center. SETTING/METHODS:A large urban academic health system in New York City. PARTICIPANTS/METHODS:Ninety-seven ambulatory clinicians across specialties. PROGRAM DESCRIPTION/METHODS:A multidisciplinary team conducted a 6-month proof-of-concept structured evaluation of two commercially available ambient documentation tools through initial vendor evaluations, technical review and integration with the electronic health record (EHR), clinician training and onboarding, implementation and technical support, and structured evaluation based on objective and key results (OKR) metrics. A single-group, pre-post evaluation of the impact of the tools on clinician EHR-based efficiency was conducted on a subset of participating clinicians. PROGRAM EVALUATION/RESULTS:Compared to the 3-month period immediately prior to initiating the ambient trial, clinicians experienced a 0.35-min-per-note and a 2.07-min-per-day reduction in documentation time. "Vendor B" showed higher utilization rates and superior user experience compared to "Vendor A." Implementation challenges included workflow integration, training resource requirements, data interoperability and analytics, and ongoing technical support needs. DISCUSSION/CONCLUSIONS:Ambient documentation shows promise in reducing documentation burden, but its success depends on technical stability and integration, product fit and support for clinicians, and adequate implementation resourcing. A multidisciplinary approach with clear metrics, strong vendor partnership and executive sponsorship, and ongoing technical support enables scalability.

OBJECTIVE/UNASSIGNED:To examine how exposure to advertising channels is associated with e-cigarette use among youth in the United States. METHODS/UNASSIGNED:This study included 161,700 middle and high school students aged 9-18 years from the 2014-2022 National Youth Tobacco Survey data. Multivariate logistic regressions estimated adjusted odds ratios (aORs) and 95 % confidence intervals (CIs) for the association between exposure to advertising channels and ever e-cigarette use. Annual percent change (APC) for ever e-cigarette use and exposure to each advertising channel were analyzed using Joinpoint regression. Data for 2014-2018 and 2019-2022 were analyzed separately due to the change in survey format and weighting procedure. RESULTS/UNASSIGNED:Ever e-cigarette use prevalence increased (APC = + 2.78: 95 % CI -13.65, 21.38) between 2014 and 2018, then declined (APC = -18.20 %: 95 % CI -27.11, 8.36) between 2019 and 2022. Exposure to retail (2014-2018 aOR = 1.36: 95 % CI 1.29, 1.43; 2019-2022 aOR = 1.46: 95 % CI 1.38, 1.54) and internet advertising (2014-2018 aOR = 1.43: 95 % CI 1.35, 1.51; 2019-2022 aOR = 1.19: 95 % CI 1.13, 1.25) were associated with higher odds of ever e-cigarette use. CONCLUSIONS/UNASSIGNED:Exposure to retail and internet marketing increased the likelihood of ever-use of e-cigarettes among youth in the US. Additional regulations for point-of-sale and internet advertisements should be implemented.

BACKGROUND/UNASSIGNED:Lung cancer remains the leading cause of cancer-related mortality worldwide, with low-dose computed tomography screening demonstrating an approximately 20% reduction in mortality among high-risk individuals. Despite this benefit, screening prevalence remains suboptimal, with often less than 20% of eligible individuals reported to be up to date on screening. Shared decision-making is essential for effective lung cancer screening (LCS) implementation, with decision aids shown to enhance patient knowledge and engagement. OBJECTIVE/UNASSIGNED:The aim of this study is to identify patient preferences, concerns, and design considerations through qualitative evaluation of MyLungHealth, a personalized patient-facing educational tool for LCS integrated with electronic health records, and to describe how these findings informed iterative design modifications. METHODS/UNASSIGNED:We employed qualitative research methods through focus groups (n=34) and individual interviews (n=18) with individuals who met screening eligibility criteria. Participants were recruited from the University of Utah Health and New York University Langone Health between May and December 2023. Feedback was analyzed using Braun and Clarke's thematic analysis principles. RESULTS/UNASSIGNED:Six themes were organized into three overarching domains. Domain A included interpretation and impact of personalized risk information: theme 1, difficulties interpreting risk information, and theme 2, varied impacts of risk information on motivation. Domain B included autonomy, privacy, and user interface preferences: theme 3, desire for autonomy and control over personal health data, and theme 4, preference for straightforward language and multiple information formats. Domain C included integration with clinical workflows and patient portal systems: theme 5, expectations for integration with health care provider workflows, and theme 6, mixed experiences with personal health record systems. These insights led to key design modifications, including simplified risk presentation, multimodal content delivery options (video and text), and implementation of electronic health record alerts for clinicians. CONCLUSIONS/UNASSIGNED:The user-centered design process for MyLungHealth revealed important considerations for developing effective patient education tools for LCS. The findings highlighted the need for simplified risk presentation, personalized information delivery, and integration with clinical workflows. These findings underscore the importance of balancing comprehensive risk communication with user accessibility.

INTRODUCTION/BACKGROUND:Linking electronic health record (EHR) use to care quality may offer insights into potential interventions improving guideline adherence and closing care gaps. We examine how EHR metadata can measure cognitive load in primary care providers during statin prescribing and identify cognitive load points in EHR workflows associated with guideline-concordant statin initiation. METHODS:We retrospectively extracted 2024 data from EHR primary care encounters from a large academic health system. We identified adult patients who met the criteria for statin initiation and calculated their atherosclerotic cardiovascular disease (ASCVD) risk scores. Cognitive load metrics were derived from EHR metadata. Logistic regressions evaluate associations between cognitive load and statin initiation, adjusting for patient covariates and provider fixed effects. Gradient-boosted forests and Shapley Additive explanations (SHAP) values were used to identify key EHR events and cognitive load patterns associated with statin initiation. RESULTS:Longer encounter duration was associated with increased likelihood of statin initiation, whereas more time spent per EHR event was associated with a decreased likelihood. Nonlinear associations were observed for loop count and distinct event count: predicted initiation probability decreased with increasing loop count to 93.9 loops, then increased beyond this threshold. For distinct events, initiation probability increased up to approximately 18 events and declined at higher counts. In a gradient-boosted decision tree model, average event time was the strongest predictor (72.2% relative contribution). Additional positive predictors included time spent reviewing lab results and on suggested medication order sets. Order list modification and looping back to it were negatively associated with statin initiation. DISCUSSION/CONCLUSIONS:EHR metadata can associate cognitive load with appropriate clinical behavior, revealing nonlinear associations between cognitive load and statin initiation rates. This work suggests opportunities to optimize EHR systems to reduce cognitive burden and support clinical decision-making. Connecting cognitive load to prescribing behavior generates hypotheses about how workflow adjustments and enhanced decision support might improve guideline adherence and patient care through prospective evaluation.

IMPORTANCE/UNASSIGNED:Artificial intelligence (AI)-enabled scribes have been proposed to reduce electronic health record (EHR) burden and improve clinician satisfaction. There is limited evidence about their associated results across multiple sites and relative benefits for different clinician groups. OBJECTIVE/UNASSIGNED:To assess the association of AI scribe adoption with changes in EHR time expenditure and visit volume and how associations vary by clinician characteristics. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Multisite, longitudinal cohort study of AI scribe adoption conducted at 5 US academic health care institutions that introduced AI scribes to their clinicians between June 2023 and August 2025. Participants were ambulatory clinicians. EXPOSURES/UNASSIGNED:AI scribe adoption, defined as receiving access to an AI scribe. This was determined by opt-in decisions by eligible physicians at 4 of the 5 sites. MAIN OUTCOME AND MEASURES/UNASSIGNED:Total time spent on the EHR, time spent on documentation, and time spent on the EHR outside scheduled hours or on unscheduled days, all normalized to 8 scheduled patient hours; weekly visit volume. RESULTS/UNASSIGNED:The sample comprised 8581 clinicians, including 1809 AI scribe adopters. Participants were 57.1% female and were split between primary care (24.4%), medical (62.4%), and surgical (13.2%) specialties. Most (74.1%) were attending physicians, with 18.1% advanced practice clinicians and 7.8% resident physicians. In a difference-in-differences analysis, AI scribe adoption was associated with 13.4 (95% CI, 9.1-17.7) fewer minutes of EHR time, 16.0 (95% CI, 13.7-18.3) fewer minutes of documentation time, and 0.49 (95% CI, 0.17-0.81) additional weekly visits delivered. Electronic health record time outside work hours did not change significantly. Changes associated with AI scribe adoption were greatest for primary care specialists, advanced practice clinicians, female clinicians, and clinicians who used AI scribes in 50% or more of visits. CONCLUSIONS AND RELEVANCE/UNASSIGNED:AI scribe adoption was associated with modest decreases in total EHR time and documentation time and with a modest increase in weekly visit volume.

BACKGROUND/UNASSIGNED:Clinical decision support (CDS) tools embedded in electronic health records in the form of integrated clinical prediction rules provide a potentially effective intervention to reduce inappropriate antibiotic prescribing for acute respiratory infections. However, their effectiveness has been limited by workflow barriers and low adoption by health care providers. Nurses are well positioned to implement evidence-based protocols using CDS tools. In a multicenter randomized controlled trial, a nurse-led implementation strategy for acute respiratory infection integrated clinical prediction rules was evaluated for use in primary care and urgent care settings. OBJECTIVE/UNASSIGNED:This study aimed to examine nurse and nurse leader perspectives on the sustainability of an electronic health record-integrated CDS tool for antibiotic stewardship and explored factors influencing its potential long-term integration into ambulatory nursing practice beyond the clinical trial. METHODS/UNASSIGNED:We interviewed 22 nurses and nurse leaders from 37 clinics across 3 academic medical centers that participated in the clinical trial. Two semistructured interview guides, one for nurses and one for nursing leadership, were developed to understand the barriers and facilitators to implementing a decision aid tool for nurses and to elicit challenges specific to nursing interactions with the CDS tool. Interviews were recorded and transcribed. Using thematic content analysis and iterative coding, our team collaboratively identified emerging themes related to sustainability and refined the results with consensus. RESULTS/UNASSIGNED:Five themes emerged: (1) importance of staffing stability and capacity, (2) impact of dedicated clinic resource availability, (3) variable nurse readiness with CDS-guided clinical care, (4) influence of openness to change and a nurse-supportive clinic culture, and (5) ongoing need for training and support. Specific recommendations for future actions were also noted. CONCLUSIONS/UNASSIGNED:Our findings revealed specific barriers and facilitators to the sustainability of a CDS tool from the nursing perspective that can inform further implementation of nurse-led delegation protocols in the ambulatory setting. Future solutions should consider mapping physical workflows, scheduling specific to nurse visits, continuing education, and treating cough and sore throat as 2 distinct processes.