Faculty Bibliography

BACKGROUND/UNASSIGNED:Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and basal insulin both lower blood sugar, but while insulin puts people at risk of hypoglycemia and weight gain, GLP-1 RAs do not. In addition, GLP-1 RAs have added cardiometabolic and renal benefits. For these reasons, when possible, many primary care providers prefer their patients with well-controlled type 2 diabetes to be transitioned from basal insulin to a GLP-1 RA. This transition process can be labor intensive, requiring multiple dosing adjustments and a watchful eye for hypoglycemia and hyperglycemia. The Mobile Insulin Titration Intervention (MITI)-GLP1 program uses SMS text messaging-based technology to support a streamlined and supervised transition process from basal insulin to a GLP-1 RA. This program takes place at a multilingual safety-net clinic. OBJECTIVE/UNASSIGNED:Our objectives were to assess program feasibility and acceptability to determine whether the intervention was doable, practical, and worthy of further investigation via a larger controlled trial. Preliminary clinical outcomes are also discussed in this paper. METHODS/UNASSIGNED:Patients were enrolled on a secure web platform that sent them a daily SMS text message asking the following: "What was your fasting blood sugar this morning?" Each weekday, texted responses containing patients' fasting blood sugar levels were checked for alarm values, and once weekly, patients were called and advised on whether and how to lower their basal insulin and increase their GLP-1 RA dose. The program was co-run by general internal medicine physicians and nurses and continued until the patient had their insulin stopped completely and/or their GLP-1 RA dose reached the maximum, or 16 weeks elapsed. All enrolled patients were included in the analyses. RESULTS/UNASSIGNED:A total of 72 patients completed the pilot program. Feasibility and acceptability were high. Of 3671 SMS text messages sent by the program, 3520 (95.89%) received a response from patients. Of 719 cumulative weeks in which Thursday titration phone calls were attempted, successful connections with patients were made in 649 (90.26%) instances. Preliminary clinical outcomes were promising. Insulin doses were meaningfully reduced (55/72, 76.39% had their basal insulin reduced by at least 50%; 45/72, 62.5% had their insulin stopped completely). GLP-1 RA doses were meaningfully increased (64/72, 88.89% had their GLP-1 RA dose increased by ≥1 level; 45/72, 62.5% were discharged on the maximum dose of their GLP-1 RA). There was minimal hypoglycemia (5/3520, 0.14% of the SMS text messages reported a value of <80 mg/dL) and hyperglycemia (1/3520, 0.03% of the SMS text messages reported a value of >400 mg/dL). CONCLUSIONS/UNASSIGNED:A general internal medicine-run MITI-GLP1 pilot program using SMS text messaging and interdisciplinary teamwork between internists and nurses is a feasible and acceptable intervention for safely and effectively transitioning people with well-controlled type 2 diabetes away from basal insulin and toward a GLP-1 RA.

BACKGROUND:Reductions in blood pressure (BP) control among patients with hypertension were observed early in the COVID-19 pandemic. The degree to which BP control may have returned to prepandemic levels is unknown. METHODS:Individuals aged 18 to 85 years with hypertension from 17 health systems participating in the National Patient-Centered Clinical Research Network were identified using electronic health record data collected as part of routine care. BP control (percentage of patients whose most recent BP measurement was <140/<90 mm Hg) was estimated in a series of 12-month rolling measurement periods from 2017 through 2022 (January 1, 2017 through December 31, 2017; April 1, 2017 through March 31, 2018; … January 1, 2022 through December 31, 2022). Differences in average BP control between 2022 (January 1, 2022 through December 31, 2022) and 2019 (January 1, 2019 through December 31, 2019) were estimated overall (adjusted for age, sex, and race and ethnicity) and by race and ethnicity (adjusted for age and sex). RESULTS:Our sample included 1 193 314 persons with hypertension in 2019 (48.9% aged 65-85 years, 52.9% men, 66.2% non-Hispanic White) and 1 499 418 individuals in 2022 (50.6% aged 65-85 years, 47.1% men, 62.7% non-Hispanic White). The weighted average BP control dropped from 65.3% in 2019 to 61.8% in 2020 and then partially recovered to 62.6% in 2022 (adjusted mean difference, -2.6 percentage points [95% CI, -5.0 to -0.2]). Non-Hispanic Asian individuals experienced the largest temporal drop in BP control, declining from 68.4% in 2019 to 63.9% in 2022. CONCLUSIONS:BP control was disrupted during the COVID-19 pandemic and had not fully rebounded to prepandemic levels by the end of 2022. Continued surveillance is needed to determine whether the decline in BP control will persist and will result in future adverse cardiovascular events.

BACKGROUND AND AIM/UNASSIGNED:Extended-release injectable naltrexone (XR-Naltrexone) is an effective treatment for opioid use disorder (OUD); however, initiation can be challenging as it requires an opioid-free period. This exploratory analysis examines patient characteristics associated with successful initiation of XR-Naltrexone in the National Drug Abuse Treatment Clinical Trials Network (CTN-0051) Extended-Release Naltrexone versus Buprenorphine for Opioid Treatment (X:BOT) trial. METHODS/UNASSIGNED:Patient demographics and clinical variables associated with successful XR-Naltrexone initiation were examined among 283 participants with OUD randomized to XR-Naltrexone in the X:BOT trial. Variables included severity of opioid use, characteristics of opioid and other substance use, treatment history, psychiatric history, baseline depression, and pain. Logistic regression models were used to estimate the effect of variables on the odds of induction success. RESULTS/UNASSIGNED:204 (72%) of 283 participants randomized to receive XR-Naltrexone completed successful induction. Housing status and pain were significantly associated with XR-Naltrexone induction status. Reported homelessness was significantly associated with higher odds of successful XR-Naltrexone induction (OR: 2.31; 95% CI: 1.12, 4.76). Individuals that reported moderate or extreme pain on the EuroQoL had half the odds of successful induction compared to those without pain (OR: 0.49; 95% CI: 0.27, 0.89). CONCLUSIONS/UNASSIGNED:Among patients with OUD initiating treatment on inpatient units, homelessness was associated with greater likelihood of successfully initiating XR-Naltrexone, while chronic pain was associated with lower likelihood of XR-Naltrexone initiation. Future research on XR-Naltrexone initiation should consider tailoring treatment based on housing status and other social determinants, and evaluation and management of pain.

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.

We evaluated the effectiveness of a community research partnership focused on improving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing for New York City residents during the pandemic. We employed interviews, a focus group, and a survey to evaluate partnership characteristics, engagement, and future collaboration. Qualitative analysis revealed five core themes: committee identity, collective goals, information sharing, adaptability, and trust. The findings highlight the importance of flexibility, shared goals, diverse representation, open communication, and trust in effective multisector community health partnerships. (Am J Public Health. 2026;116(4):431-436. https://doi.org/10.2105/AJPH.2025.308358).

IMPORTANCE/UNASSIGNED:Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer deaths in the US. Although early detection improves survival, the rarity of the disease has rendered population screening a difficult approach. OBJECTIVE/UNASSIGNED:To develop and validate a parsimonious, interpretable, and generalizable model predicting incident PDAC-termed PRIME (PDAC Risk Model for Earlier Detection)-using routinely available electronic health record (EHR) data. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This cohort study used the Optum Labs Data Warehouse, a longitudinal, deidentified US EHR and claims database. Adults 40 years or older with an outpatient clinical encounter between 2016 and 2018 were included. Participants from 23 health systems (n = 4 859 833) comprised the training cohort; 31 additional systems (n = 5 619 091) served as validation. International validation was conducted in the UK Biobank (n = 498 754). Data analysis occurred July 2025 to January 2026. EXPOSURES/UNASSIGNED:Demographics, diagnosis codes, and routinely measured laboratory values were evaluated. Elastic-net regularization with 10-fold cross-validation selected the predictor set. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Incident PDAC was identified by International Classification of Diseases, Ninth and Tenth Revisions (ICD-9/10) codes. Model performance was assessed using time-dependent area under the curve (AUC) and calibration metrics. RESULTS/UNASSIGNED:Overall, the study included more than 11 million adults (2.1% Asian individuals, 8.4% Black individuals, 4.3% Hispanic/Latino individuals, 82.7% White individuals, and 2.4% other race/ethnicity by EHR reporting). In the training cohort (mean [SD] age, 60.4 [11] years), 14 405 individuals were diagnosed with PDAC (incidence 55 per 100 000 person-years) over a mean (SD) of 5.4 (2.5) years; in the validation cohort, 11 693 individuals were diagnosed with PDAC (54 per 100 000 person-years) over a mean (SD) of 3.9 (2.5) years. PRIME retained 19 predictors including history of pancreatitis, gastrointestinal disorders, prior cancers, type 2 diabetes, elevated aspartate aminotransferase levels, smoking, non-type-O blood, and male sex. Discrimination was strong at the 36-month time horizon (AUC = 0.75 in both the training and validation cohorts) with good calibration. In the validation cohort, patients in the top 1% of predicted risk had substantially higher PDAC risk (HR, 7.63; 95% CI, 6.85-8.49) compared with average-risk patients. In the UK Biobank, PRIME achieved a 36-month AUC of 0.71 with good calibration. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this validation cohort study, PRIME was a transparent EHR-based model that effectively stratified PDAC risk across diverse US health systems and generalized internationally. Prospective studies should evaluate for EHR-guided PDAC case-finding and integration with blood-based early-detection assays.