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.

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.

BACKGROUND:Nonadherence to antihypertensive medications is common. Mobile health (mHealth)-based behavioral economic interventions may improve adherence, but remain largely untested, especially in vulnerable populations. OBJECTIVE:The study sought to test whether an mHealth incentive lottery would lower systolic blood pressure (SBP) and improve adherence. METHODS:BETTER-BP (Behavioral Economics Trial To Enhance Regulation of Blood Pressure) was a randomized trial conducted in 3 safety-net clinics in New York City. Eligible participants were adults with hypertension prescribed at least 1 antihypertensive medication, with SBP >140 mm Hg, and poor self-reported adherence. In the intervention arm, an incentive lottery was administered via SMS messaging. All participants received passive adherence monitoring. The intervention lasted 6 months, with continued monitoring until 12 months. The primary clinical endpoint was change in SBP at 6 months. The primary process endpoint was adequate antihypertensive medication adherence (≥80% days adherent) from baseline to 6 months. RESULTS:Four-hundred participants (265 intervention:135 control) were enrolled with median age 57 years, 60.5% women, 61.5% Hispanic, and 20.3% non-Hispanic Black. Over 70% had Medicaid or no insurance. At 6 months, intervention arm participants were twice as likely to achieve adequate adherence (71% vs 34%; adjusted risk ratio: 2.04; 95% CI: 1.58-2.63), but there was no significant change in mean SBP (-6.7 mm Hg intervention vs -5.8 mm Hg control; P = 0.62). From 6 to 12 months, adherence was similar (31% intervention vs 26% control; adjusted risk ratio: 1.17; 95% CI: 0.83-1.65). CONCLUSIONS:In a diverse safety-net population, the BETTER-BP intervention doubled the rate of adequate antihypertensive medication adherence but did not reduce SBP at 6 months.

The integration of generative AI (GenAI) in patient communication presents benefits and challenges. This retrospective observational study analyzed EHR audit logs to assess how 75 healthcare professionals (HCPs) utilized AI-generated drafts for patient messages from October 2023 to August 2024 at a large health system in New York City. Overall utilization was low (19.4%), though prompt refinements improved usage (from 12% to 20%), particularly among physicians. GenAI drafts were generated for all messages, including 80% that received no response, adding to the review burden and potentially undermining efficiency. Text analysis showed HCPs preferred concise, information-rich drafts, with role-based differences-physicians favored shorter drafts, while clinical support staff preferred more empathetic responses. AI-generated drafts reduced message turnaround time by 6.76% despite a marginal increase in required steps (InBasket actions). These findings highlight the need for targeted GenAI deployment strategies, better aligned with clinician workflows and optimized draft generation for improved efficiency.

Health care has become increasingly digitized. Given that under-invested health systems and patient populations frequently have worse access to the newest innovations, there is concern that this digitalization may exacerbate preexisting health inequities. This article discusses the multiple ways that digital health may increase health inequities. Using case studies presented by digital health leaders in different roles and settings, it provides examples of how health systems can adopt and implement innovative tools to deliver care while centering health equity. The case studies highlight five guidelines that health-care systems should consider as they weigh the equity implications of adopting any digital tool: auditing benefits, institutional incentives, elevating frontline and patient perspectives, long-term community engagement, and protecting data.

BACKGROUND:Health equity is receiving increased attention in medical education. However, guidance is often lacking on how to integrate health equity into routine medical education. Journal club presents an opportunity to deepen medical educators' and learners' understanding of health equity principles and use it as a lens through which to critically appraise the literature. AIM/OBJECTIVE:We present a health equity framework, iteratively co-created by faculty and learners, that can be applied in a journal club setting. SETTING/METHODS:Academic medical center in New York City, USA. PARTICIPANTS/METHODS:Faculty, residency program directors, medical students, and residents. PROGRAM DESCRIPTION/METHODS:Authors developed the health equity journal club framework during a medical student selective course. Learner and faculty applied the framework to journal club articles; their feedback informed revisions. Framework domains included authorship, ethics, methodology, language, peer review, and references. PROGRAM EVALUATION/RESULTS:Learner evaluations were overall positive, and 86% (n = 13) of responding residency program directors (n = 15) across 15 departments who were surveyed plan to use the framework moving forward. DISCUSSION/CONCLUSIONS:A health equity journal club framework applied to critical appraisal of the literature may facilitate health equity as a routine part of medical education. Co-creating the framework proved vital to inclusion of learner voices.

BACKGROUND/UNASSIGNED:Usability testing is valuable for assessing a new tool or system's usefulness and ease-of-use. Several established methods of usability testing exist, including think-aloud testing. Although usability testing has been shown to be crucial for successful clinical decision support (CDS) tool development, it is often difficult to conduct across multisite development projects due to its time- and labor-intensiveness, cost, and the skills required to conduct the testing. OBJECTIVE/UNASSIGNED:Our objective was to develop a new method of usability testing that would enable efficient acquisition and dissemination of results among multiple sites. We sought to address the existing barriers to successfully completing usability testing during CDS tool development. METHODS/UNASSIGNED:We combined individual think-aloud testing and focus groups into one session and performed sessions serially across 4 sites (snowball group usability testing) to assess the usability of two CDS tools designed for use by nurses in primary and urgent care settings. We recorded each session and took notes in a standardized format. Each site shared feedback from their individual sessions with the other sites in the study so that they could incorporate that feedback into their tools prior to their own testing sessions. RESULTS/UNASSIGNED:The group testing and snowballing components of our new usability testing method proved to be highly beneficial. We identified 3 main benefits of snowball group usability testing. First, by interviewing several participants in a single session rather than individuals over the course of weeks, each site was able to quickly obtain their usability feedback. Second, combining the individualized think-aloud component with a focus group component in the same session helped study teams to more easily notice similarities in feedback among participants and to discuss and act upon suggestions efficiently. Third, conducting usability testing in series across sites allowed study teams to incorporate feedback based on previous sites' sessions prior to conducting their own testing. CONCLUSIONS/UNASSIGNED:Snowball group usability testing provides an efficient method of obtaining multisite feedback on newly developed tools and systems, while addressing barriers typically associated with traditional usability testing methods. This method can be applied to test a wide variety of tools, including CDS tools, prior to launch so that they can be efficiently optimized.

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).

BACKGROUND:During the COVID-19 pandemic, acute respiratory infection (ARI) antibiotic prescribing in ambulatory care markedly decreased. It is unclear if antibiotic prescription rates will remain lowered. METHODS:We used trend analyses of antibiotics prescribed during and after the first wave of COVID-19 to determine whether ARI antibiotic prescribing rates in ambulatory care have remained suppressed compared to pre-COVID-19 levels. Retrospective data was used from patients with ARI or UTI diagnosis code(s) for their encounter from 298 primary care and 66 urgent care practices within four academic health systems in New York, Wisconsin, and Utah between January 2017 and June 2022. The primary measures included antibiotic prescriptions per 100 non-COVID ARI encounters, encounter volume, prescribing trends, and change from expected trend. RESULTS:At baseline, during and after the first wave, the overall ARI antibiotic prescribing rates were 54.7, 38.5, and 54.7 prescriptions per 100 encounters, respectively. ARI antibiotic prescription rates saw a statistically significant decline after COVID-19 onset (step change -15.2, 95% CI: -19.6 to -4.8). During the first wave, encounter volume decreased 29.4% and, after the first wave, remained decreased by 188%. After the first wave, ARI antibiotic prescription rates were no longer significantly suppressed from baseline (step change 0.01, 95% CI: -6.3 to 6.2). There was no significant difference between UTI antibiotic prescription rates at baseline versus the end of the observation period. CONCLUSIONS:The decline in ARI antibiotic prescribing observed after the onset of COVID-19 was temporary, not mirrored in UTI antibiotic prescribing, and does not represent a long-term change in clinician prescribing behaviors. During a period of heightened awareness of a viral cause of ARI, a substantial and clinically meaningful decrease in clinician antibiotic prescribing was observed. Future efforts in antibiotic stewardship may benefit from continued study of factors leading to this reduction and rebound in prescribing rates.