Faculty Bibliography

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:Moral distress, which occurs when the ethically correct action cannot be taken because of internal or external constraints, is associated with depression, burnout, and the desire to leave the healthcare profession among healthcare workers. This study compares internal medicine (IM) residents’ experiences of moral distress while caring for patients with COVID-19 in the year prior to and during the first year of the COVID-19 pandemic. METHODS:This is a mixed methods prospective observational cohort study that enrolled IM residents on a rolling basis beginning December 2018. Moral distress was evaluated via the validated Moral Distress Score-Revised (MDS-R) and Measure of Moral Distress for Healthcare Professionals (MDD-HP) and open-ended questions every 4-months via online surveys and through five resident focus groups. The moral distress scores (MDS) before and during the COVID-19 pandemic were compared using paired t-tests. Transcripts and free text were independently coded by investigators and analyzed by major themes and sub-themes. RESULTS: < .05). Qualitive findings included the exacerbation of existing moral distress and the emergence of new drivers of moral distress, including personal protective equipment, visitor policies, lack of moral framework, and tension between protecting one’s own health and caring for others. CONCLUSIONS:The results of this preliminary analysis suggest that the COVID-19 pandemic exacerbated pre-existing experiences of moral distress and brought to light new and different morally distressing situations for trainees. This analysis of the impact of the pandemic is valuable not only for identifying leverage points for intervention, but also for informing future crisis preparedness and cultivating moral resilience in trainees and the healthcare workforce. SUPPLEMENTARY INFORMATION:The online version contains supplementary material available at 10.1186/s12910-025-01274-6.

BACKGROUND:Glaucoma Research Foundation's third Catalyst for a Cure team (CFC3) was established in 2019 to uncover new therapies for glaucoma, a leading cause of blindness. In the 2021 meeting "Solving Neurodegeneration," (detailed in Mol Neurodegeneration 17(1), 2022) the team examined the failures of investigational monotherapies, issues with translatability, and other significant challenges faced when working with neurodegenerative disease models. They emphasized the need for novel, humanized models and proposed identifying commonalities across neurodegenerative diseases to support the creation of pan-neurodegenerative disease therapies. Since then, the fourth Catalyst for a Cure team (CFC4) was formed to explore commonalities between glaucoma and other neurodegenerative diseases. This review summarizes outcomes from the 2023 "Solving Neurodegeneration 2" meeting, a forum for CFC3 and CFC4 to share updates, problem solve, plan future research collaborations, and identify areas of unmet need or opportunity in glaucoma and the broader field of neurodegenerative disease research. MAIN BODY/METHODS:We summarize the recent progress in the field of neurodegenerative disease research and present the newest challenges and opportunities moving forward. While translatability and disease complexity continue to pose major challenges, important progress has been made in identifying neuroprotective targets and understanding neuron-glia-vascular cell interactions. New challenges involve improving our understanding of the disease microenvironment and timeline, identifying the optimal approach(es) to neuronal replacement, and finding the best drug combinations and synergies for neuroprotection. We propose solutions to common research questions, provide prescriptive recommendations for future studies, and detail methodologies, strategies, and approaches for addressing major challenges at the forefront of neurodegenerative disease research. CONCLUSIONS:This review is intended to serve as a research framework, offering recommendations and approaches to validating neuroprotective targets, investigating rare cell types, performing cell-specific functional characterizations, leveraging novel adaptations of scRNAseq, and performing single-cell sorting and sequencing across neurodegenerative diseases and disease models. We focus on modeling neurodegeneration using glaucoma and other neurodegenerative pathologies to investigate the temporal and spatial dynamics of neurodegenerative disease pathogenesis, suggesting researchers aim to identify pan-neurodegenerative drug targets and drug combinations leverageable across neurodegenerative diseases.

Background Overuse and misuse of antibiotics is an urgent healthcare problem and one of the key factors in antibiotic resistance. Validated clinical prediction rules have shown effectiveness in guiding providers to an appropriate diagnosis and identifying when antibiotics are the recommended choice for treatment. Objective We aimed to study the relative ability of registered nurses using clinical prediction rules to guide the management of acute respiratory infections in a simulated environment compared to practicing primary care physicians. Design We evaluated a case-based simulation of the diagnosis and treatment for acute respiratory infections using clinical prediction rules. As a secondary outcome, we examined nursing self-efficacy by administering a survey before and after case evaluations. Participants Participants included 40 registered nurses from three academic medical centers and five primary care physicians as comparators. Participants evaluated six simulated case studies, three for patients presenting with cough symptoms and three for sore throat. Key Results Compared to physicians, nurses determined risk and treatment for simulated sore throat cases using clinical prediction rules with nurses having 100% accuracy in low-risk sore throat cases versus 80% for physicians. We found great variability in the accuracy of the risk level and appropriate treatment for cough cases. Nurses reported slight increases in self-efficacy from baseline to post-case evaluation suggesting further information is needed to understand correlation. Conclusions Clinical prediction rules used by nurses in sore throat management workflows can guide accurate diagnosis and treatment in simulated cases, while cough management requires further exploration. Our results support the future implementation of automated prediction rules in a clinical decision support tool and a thorough examination of their effect on clinical practice and patient outcomes.

BACKGROUND:Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. OBJECTIVE:We examined users' interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users' clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. METHODS:We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. RESULTS:Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. CONCLUSIONS:Findings highlight the chatbot's effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot's scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

IMPORTANCE/UNASSIGNED:Medication nonadherence is present in nearly half of patients with hypertension but is underrecognized in clinical care. Data linkages between electronic health records and pharmacies have created opportunities for scalable assessment of medication adherence at the point of care. OBJECTIVE/UNASSIGNED:To test the effectiveness of a multicomponent intervention that identified patients with uncontrolled hypertension and medication nonadherence using linked electronic health record-pharmacy data combined with team-based care to address adherence barriers. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:TEAMLET (Leveraging Electronic Health Record Technology and Team Care to Address Medication Adherence) was a pragmatic, 2-arm, cluster randomized clinical trial conducted between October 2022 and November 2024 in 10 primary care sites in New York. The study included adults with uncontrolled hypertension and low medication adherence, defined as proportion of days covered (PDC) less than 80%. Data analysis was performed from November 2024 to January 2025. INTERVENTION/UNASSIGNED:The intervention consisted of the following: (1) automated identification of patients with medication nonadherence at the time of the visit; (2) prompting of medical assistants to screen for barriers to adherence; (3) clinical decision support alerting the primary care physicians and nurse practitioners to barriers to adherence; and (4) adherence discussion between the primary care physician or nurse practitioner and the patient. The comparator was usual care. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was change in PDC from baseline to 12 months. RESULTS/UNASSIGNED:Among 1726 patients (mean [SD] age, 67.2 [13.9] years; 887 [51.4%] female), the mean (SD) baseline PDC was 33.2% (30.5%) overall (32.4% [30.4%] in the intervention group and 34.0% [30.6%] in the control group). The mean (SD) PDC at 12 months was 51.1% (39.5%) for the intervention group and 53.1% (39.6%) for the control group. No difference was found in the change in PDC from baseline to 12 months between the intervention and control groups (mean [SD] absolute change in PDC, 18.5 [41.1] vs 18.2 [40.9] percentage points, respectively; adjusted difference, -0.15 percentage point; 95% CI, -4.06 to 3.76 percentage points). Change in systolic blood pressure and patients who became adherent (PDC ≥80%) at 12 months were also similar between groups. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this pragmatic trial, an intervention that combined team-based primary care with automated identification of patients with antihypertensive medication nonadherence did not lead to improvements in adherence or blood pressure. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT05349422.

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.

Uncontrolled hypertension is common and frequently related to inadequate adherence to prescribed medications, resulting in suboptimal blood pressure control and increased healthcare utilization. Although healthcare providers have the opportunity to improve medication adherence, they may lack the tools to address adherence at the point of care. This study aims to assess the usability of a digital tool designed to improve medication adherence and blood pressure control among patients with hypertension who are not adherent to therapy. By evaluating usability, the study seeks to refine the tool's design, underscore the role of technology in managing hypertension, and provide insights to inform clinical decisions.We performed qualitative usability testing of an electronic health record (EHR)-integrated intervention with medical assistants (MAs) and primary care providers (PCPs) from a large integrated health system. Usability was assessed with these end-users using the "think aloud" and "near live" approaches. This evaluation was guided by two frameworks: the End-User Computing Satisfaction Index (EUCSI) and the Technology Acceptance Model (TAM). Interviews were analyzed using a thematic analysis approach.Thematic saturation was reached after usability testing was performed with 10 participants, comprising 5 PCPs and 5 MAs. The study identified several strengths within the content, format, ease of use, timeliness, accuracy, and usefulness of the tool, including the user-friendly content presentation, the usefulness of adherence information, and timely alerts that fit into the workflow. Challenges centered around alert visibility and specificity of information.Leveraging the two conceptual frameworks (TAM and EUCSI) to test the usability of the medication adherence tool was helpful. The tool's several strengths and opportunities for improvement were found. The resulting suggestions will be used to support the enhancement of the design for optimal implementation in a clinical trial.