Faculty Bibliography

BACKGROUND:About one-third of the computed tomography (CT) scans ordered yearly to evaluate for pulmonary embolism (PE) in emergency departments (ED) in the U.S. are avoidable. Clinical guidelines recommend the use of validated PE prediction rules which reduce CT scan ordering without an increase in missed PEs, but there is low provider adoption. Clinical decision support (CDS) that incorporates these rules along with nudges (subtle, non-coercive influences on decision-making) may improve provider adoption. In our pilot trial of a PE risk CDS tool with a nudge at order entry, adoption was significantly higher (39.1%) than for the tool without nudges (20.7%). The tool was developed on an EHR-agnostic web-based platform, designed for dissemination to work with any EHR. The objective of this study is to evaluate the tool with a nudge in a multi-site, randomized trial. METHODS:A hybrid type III, stepped-wedge, ED-level, cluster randomized trial will be conducted. Study settings include 12 EDs in three geographically diverse health care systems. The EDs will be randomized over 9 steps over 30 months, ensuring at least 3 months of intervention exposure for all EDs. We will randomize matched pairs of EDs based on patient volume in each stratum to early intervention or late intervention. All providers ordering CTs for the evaluation of PE in adult patients at the site EDs will be included. Our study will be guided by two complementary frameworks: Behavioral Change Wheel Framework and Proctor's Implementation Outcomes Framework. Nudge implementation strategies in the CDS user interface will be a peer comparison of providers' hit rates for CTs ordered for PE and salient messaging. We will use an EHR-agnostic, web-based platform to implement the tool. The primary outcome will be guideline-concordant CT ordering for PE. DISCUSSION/CONCLUSIONS:This trial will advance our understanding of the impact of behavioral strategies on improving provider adoption of CDS. Additionally, the trial will confirm the impact of guideline-concordant CT ordering on CT yield rates across a diverse patient population. The use of an EHR-agnostic platform helps maximize the dissemination potential of the core evidence-based practices it facilitates. TRIAL REGISTRATION/BACKGROUND:NCT07249385; Registered 11/18/2025.

IMPORTANCE/UNASSIGNED:Remote patient monitoring (RPM), including self-measured blood pressure monitoring with clinician review and telehealth-supported feedback, can support hypertension management. However, RPM use and related care continuity after switching from Medicare fee-for-service (FFS) to Medicare Advantage (MA) remain unclear. OBJECTIVE/UNASSIGNED:To compare RPM adoption, clinician continuity, and hypertension-related acute care utilization among beneficiaries who remained in Medicare FFS vs switched to MA plans, categorized as value-based contract (VBC) proxy or non-VBC. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This cohort study with an observational difference-in-differences design with propensity score matching used data from 2016 to 2022 Medicare enrollment, FFS claims, and MA encounter data. Beneficiaries were aged 65 years or older with prevalent diagnosed hypertension in 2018 and continuous enrollment in Parts A and B in 2018. Treated groups switched from FFS to MA in January 2019 and remained enrolled through 2022; comparators remained in FFS. Follow-up extended from January 1, 2019, through December 31, 2022. Data analysis was conducted from April to July 2025. EXPOSURE/UNASSIGNED:Switching from Medicare FFS to MA-VBC proxy or MA non-VBC in 2019. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was annual RPM adoption during hypertension-related visits; secondary outcomes included clinician loss without replacement, clinician switching or substitution, and hypertension-related emergency department (ED) visits and hospitalizations. RESULTS/UNASSIGNED:Matched samples included 281 620 beneficiaries, with 46 833 MA-VBC proxy plan switchers and 46 833 FFS comparators (27 920 [59.6%] aged 71 years or older and 27 685 female [59.1%] in each group) and 93 977 MA non-VBC switchers and 93 977 FFS comparators (67 188 [71.5%] aged 71 years or older; 53 122 female [56.5%] in each group). Common comorbidities included diabetes, chronic kidney disease, and heart failure. Switching to MA was associated with lower RPM adoption in 2022 (MA-VBC proxy: odds ratio [OR], 0.55; 95% CI, 0.42-0.72; -0.63 percentage points; non-VBC: OR, 0.73; 95% CI, 0.54-0.99; -0.52 percentage points), greater clinician loss without replacement (MA-VBC proxy: OR, 1.27; 95% CI, 1.23-1.32; 3.41 percentage points; MA non-VBC: OR, 1.09; 95% CI, 1.06-1.12; 0.83 percentage points), and higher hypertension-related hospitalizations (MA-VBC proxy: OR, 1.75; 95% CI, 1.48-2.06; MA non-VBC: OR, 1.94; 95% CI 1.71-2.19; 1.56 percentage points in both comparisons). Event-study analyses showed postswitch divergence through 2022. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this cohort study of older Medicare beneficiaries with hypertension, switching from FFS to MA was associated with lower RPM adoption, greater clinician discontinuity, and higher hypertension-related acute care use. These findings suggest that continuity safeguards and clearer payment or quality incentives during MA transitions may support remote monitoring and clinician follow-up for hypertension.

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: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.

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/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.