Faculty Bibliography

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.

IMPORTANCE/UNASSIGNED:Lung cancer screening (LCS) with low-dose computed tomography (CT) remains underused in the US, partly because of incomplete smoking history documentation in electronic health records (EHRs) and limited time for shared decision-making in primary care. OBJECTIVE/UNASSIGNED:To determine whether a patient-facing, EHR-integrated tool combined with clinician-facing clinical decision support improves the identification of LCS-eligible patients and the ordering of low-dose CT compared with clinician-facing tools alone. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This pragmatic, unstratified, randomized clinical trial with parallel groups was conducted from March 29, 2024, to March 28, 2025, at primary care clinics at University of Utah Health and New York University Langone Health. Adults aged 50 to 79 years with a documented smoking history, an active patient portal account, and a primary care visit in the preceding year were included. Study 1 enrolled patients with uncertain LCS eligibility (10 to 19 pack-years, unknown pack-years, or missing quit date); study 2 enrolled patients with documented eligibility (20 or more pack-years and currently smoking or quit smoking within 15 years). INTERVENTIONS/UNASSIGNED:The control included the clinician-facing Decision Precision+ tool (preventive care reminders and a shared decision-making tool). The intervention included the Decision Precision+ tool as well as the MyLungHealth tool, which collected detailed smoking history (study 1) and delivered personalized education and risk/benefit information (studies 1 and 2) via the patient portal in English and Spanish. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcomes were the proportion of patients newly identified as eligible for LCS (study 1) and low-dose CT ordering rates (study 2) over 12 months. Analyses used intention-to-treat mixed-effects logistic regression. RESULTS/UNASSIGNED:There were 31 303 randomized participants, including 26 729 in study 1 (13 144 [49.2%] female; 13 580 [50.8%] male; median [IQR] age, 62 [55-69] years) and 4574 in study 2 (2230 [48.8%] female; 2344 [51.2%] male; median [IQR] age, 63 [56-69] years). In study 1, the MyLungHealth tool increased new LCS eligibility identification (635 of 13 412 [4.7%] vs 308 of 13 317 [2.3%]; adjusted odds ratio, 2.19; 95% CI, 1.99-2.42; P < .001). In study 2, low-dose CT ordering was higher in the intervention arm (474 of 2312 [20.5%] vs 434 of 2262 [19.2%]; adjusted odds ratio, 1.16; 95% CI, 1.04-1.30; P = .008). CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this randomized clinical trial, integrating a patient-centered tool into primary care EHR workflows increased the identification of patients eligible for LCS and the ordering of low-dose CTs. The relative increases in these primary outcomes were substantial, but absolute increases were more modest. Research on more intensive interventions is warranted to evaluate their ability to further improve LCS screening. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT06338592.

ObjectivesRemote patient monitoring (RPM), combining home blood pressure measurements with telehealth services, effectively manages hypertension. Successful implementation of RPM programs at scale requires understanding program costs and financial sustainability. We evaluated the financial performance of an RPM program.MethodsConducted from March to June 2024 in the Cardiology Division at New York University Langone Health, the study used field observation, surveys, and micro-costing methods. A costing tool was developed to quantify program costs in 2024 US dollars, including personnel, equipment, and supplies. RPM-related services reimbursement rates were estimated using Medicare billing information. The return-on-investment (ROI) ratio was calculated by dividing net return (profit) by the RPM program costs. Sensitivity analyses assessed the impact of varying parameters on the ROI of RPM.ResultsThe average RPM program cost was estimated at $330 per patient (range: $208-$452). Major expenses included data review by staff ($172 per patient), blood pressure devices ($48 per patient), and phone communications ($36 per patient). ROI varied based on patient compliance with home blood pressure monitoring (≥16 days per month), with an average estimate of 22.2% (range: -11.1%-93.3%) per patient at a 55% compliance rate. The ROI was most sensitive to changes in data-review costs, insurance reimbursement rates, patient compliance, device setup, and communication costs.ConclusionsThe RPM program achieved a positive ROI from the perspective of a clinical division in a large healthcare system. Successful implementation and financial sustainability of RPM require efforts to reduce human resource costs and enhance patient engagement.

INTRODUCTION/BACKGROUND:Colorectal cancer (CRC) screening rates among patients receiving care at multiple federally qualified health care centers (FQHCs) in New York city are low. Proactive outreach through mailed fecal immunochemical tests (FIT), reminders and navigation are evidence based interventions to improve CRC screening rates but remain untested in this study population. OBJECTIVE:To evaluate the effectiveness, implementation, and cost-effectiveness of a multilevel proactive outreach strategy to improve CRC screening rates among underserved adults in Brooklyn, New York. METHODS:This is a randomized controlled trial across five FQHCs serving predominantly Black and low-income populations. Adults aged 45-75 who are overdue for CRC screening are randomized to usual care or a multi-level proactive intervention. The intervention includes mailed education and FIT kits, patient navigation, and support for colonoscopy scheduling and follow-up. The primary outcome is CRC screening completion (FIT or colonoscopy) within six months. Secondary outcomes include colonoscopy follow-up after a positive FIT, implementation barriers and facilitators, and cost-effectiveness. RESULTS:A total of 1379 participants have been enrolled through May 2025. DISCUSSION/CONCLUSIONS:This trial addresses a critical gap in CRC prevention by testing a scalable, multilevel outreach model tailored to underserved populations. Findings will inform future strategies to enhance screening rates while reducing screening disparities through sustainable FQHC-based programs.

BACKGROUND:Medication nonadherence is a common issue among patients with hypertension. Healthcare professionals often overlook medication nonadherence due to limited tools, time constraints, and competing demands. Integrating pharmacy medication fill data into electronic health records (EHRs) presents an opportunity to enhance medication adherence measurement and monitoring in real-time. This study identified facilitators and barriers to addressing adherence to antihypertensive medications by Medical Assistants (MAs), Registered Nurses (RNs), and Primary Care Providers (PCPs) in primary care settings. METHODS:We conducted a qualitative study with, 15 healthcare professionals (5 MAs, 5 RNs, and 5 PCPs) caring for patients with hypertension. Semi-structured interviews, guided by the Consolidated Framework for Implementation Research (CFIR), explored barriers and facilitators related to screening and addressing medication non-adherence during primary care clinical encounters. Thematic analysis and deductive coding were used to analyze the data. RESULTS:Four major themes emerged: motivation, work infrastructure, capability, and opportunity. MAs and PCPs were motivated to discuss medication adherence and build relationships. Capability varied; RNs were confident in their counseling skills based on their training and patient trust, and PCPs described adherence counseling as part of their role, particularly through motivational interviewing. Work infrastructure presented structural hurdles due to RN workflow limitations and MA role constraints. Opportunity to address non-adherence were constrained by tight schedules and competing clinical demands during brief visits. CONCLUSIONS:RNs and PCPs felt capable in their ability to address medication adherence but cited time and competing demands as significant barriers; conversely, MAs reported motivation but were limited by their role. These findings suggest opportunities for effective management of medication adherence in practice settings through a more coordinated strategy across multiple healthcare professionals. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov; NCT05349422; https://clinicaltrials.gov/ct2/show/NCT05349422.

BACKGROUND/UNASSIGNED:With rising patient volumes and a focus on quality, our health system had the objective to create a more efficient way to ensure accurate documentation of colorectal cancer (CRC) screening intervals from inbound colonoscopy reports to ensure timely follow-up. We developed an integrated end-to-end workflow solution using machine learning (ML) and robotic process automation (RPA) to extract and update electronic health record (EHR) follow-up dates from unstructured data. OBJECTIVE/UNASSIGNED:This study aimed to automate data extraction from external, free-text colonoscopy reports to identify and document recommended follow-up dates for CRC screening in structured EHR fields. METHODS/UNASSIGNED:As proof of concept, we outline the process development, validity, and implementation of an approach that integrates available tools to automate data retrieval and entry within the EHR of a large academic health system. The health system uses Epic Systems as its EHR platform, and the ML model used was trained on health system patient colonoscopy reports. This proof-of-concept process study consisted of six stages: (1) identification of gaps in documenting recommendations for follow-up CRC screening from external colonoscopy reports, (2) defining process objectives, (3) identification of technologies, (4) creation of process architecture, (5) process validation, and (6) health system-wide implementation. A chart review was performed to validate process outcomes and estimate impact. RESULTS/UNASSIGNED:We developed an automated process with 3 primary steps leveraging ML and RPA to create a fully orchestrated workflow to update CRC screening recall dates based on colonoscopy reports received from external sources. Process validity was assessed with 690 scanned colonoscopy reports. During process validation, the overall automated process achieved an accuracy of 80.7% (557/690, 95% CI 77.8%-83.7%) for correctly identifying the presence or absence of a valid follow-up date and a follow-up date false negative identification rate of 32.9% (130/395, 95% CI 29.4%-36.4%). From the organization-wide implementation to go-live until December 31, 2024, the system processed 16,563 external colonoscopy reports. Of these, 35.3% (5841/16,563) had a follow-up date meeting the relevant ML model threshold and thus were identified as ready for RPA processing. CONCLUSIONS/UNASSIGNED:Implementation of an automated workflow to extract and update CRC screening follow-up dates from colonoscopy reports is feasible and has the potential to improve accuracy in patient recall while reducing documentation burden. By standardizing data ingestion, extending this approach to various unstructured data types can address deficiencies in structured EHR documentation and solve for a lack of data integration and reporting for quality measures. Automated workflows leveraging ML and RPA offer practical solutions to overcome interoperability challenges and the use of unstructured data within health care systems.

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.