Faculty Bibliography

BACKGROUND:Regular physical activity (PA) is a component of cardiovascular health and is associated with a lower risk of cardiovascular disease (CVD). However, only about half of US adults achieved the current PA recommendations. OBJECTIVE:The study purpose was to implement PA counseling using a clinical decision support tool in a preventive cardiology clinic and to assess changes in CVD risk factors in a sample of patients enrolled over 12 weeks of PA monitoring. METHODS:This intervention, piloted for 1 year, had 3 components embedded in the electronic health record: assessment of patients' PA, an electronic prompt for providers to counsel patients reporting low PA, and patient monitoring using a Fitbit. Cardiovascular disease risk factors included PA (self-report and Fitbit), body mass index, blood pressure, lipids, and cardiorespiratory fitness assessed with the 6-minute walk test. Depression and quality of life were also assessed. Paired t tests assessed changes in CVD risk. RESULTS:The sample who enrolled in the remote patient monitoring (n = 59) were primarily female (51%), White adults (76%) with a mean age of 61.13 ± 11.6 years. Self-reported PA significantly improved over 12 weeks ( P = .005), but not Fitbit steps ( P = .07). There was a significant improvement in cardiorespiratory fitness (469 ± 108 vs 494 ± 132 m, P = .0034), and 23 participants (42%) improved at least 25 m, signifying a clinically meaningful improvement. Only 4 participants were lost to follow-up over 12 weeks of monitoring. CONCLUSIONS:Patients may need more frequent reminders to be active after an initial counseling session, perhaps getting automated messages based on their step counts syncing to their electronic health record.

We have entered a new age of health informatics—applied health informatics—where digital health innovation cannot be pursued without considering operational needs. In this new digital health era, creating an integrated applied health informatics system will be essential for health systems to achieve informatics healthcare goals. Integration of information technology (IT) and health informatics does not naturally occur without a deliberate and intentional shift towards unification. Recognizing this, NYU Langone Health’s (NYULH) Medical Center IT (MCIT) has taken proactive measures to vertically integrate academic informatics and operational IT through the establishment of the MCIT Department of Health Informatics (DHI). The creation of the NYULH DHI showcases the drivers, challenges, and ultimate successes of our enterprise effort to align academic health informatics with IT; providing a model for the creation of the applied health informatics programs required for academic health systems to thrive in the increasingly digitized healthcare landscape.

IMPORTANCE/UNASSIGNED:Virtual patient-physician communications have increased since 2020 and negatively impacted primary care physician (PCP) well-being. Generative artificial intelligence (GenAI) drafts of patient messages could potentially reduce health care professional (HCP) workload and improve communication quality, but only if the drafts are considered useful. OBJECTIVES/UNASSIGNED:To assess PCPs' perceptions of GenAI drafts and to examine linguistic characteristics associated with equity and perceived empathy. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This cross-sectional quality improvement study tested the hypothesis that PCPs' ratings of GenAI drafts (created using the electronic health record [EHR] standard prompts) would be equivalent to HCP-generated responses on 3 dimensions. The study was conducted at NYU Langone Health using private patient-HCP communications at 3 internal medicine practices piloting GenAI. EXPOSURES/UNASSIGNED:Randomly assigned patient messages coupled with either an HCP message or the draft GenAI response. MAIN OUTCOMES AND MEASURES/UNASSIGNED:PCPs rated responses' information content quality (eg, relevance), using a Likert scale, communication quality (eg, verbosity), using a Likert scale, and whether they would use the draft or start anew (usable vs unusable). Branching logic further probed for empathy, personalization, and professionalism of responses. Computational linguistics methods assessed content differences in HCP vs GenAI responses, focusing on equity and empathy. RESULTS/UNASSIGNED:A total of 16 PCPs (8 [50.0%] female) reviewed 344 messages (175 GenAI drafted; 169 HCP drafted). Both GenAI and HCP responses were rated favorably. GenAI responses were rated higher for communication style than HCP responses (mean [SD], 3.70 [1.15] vs 3.38 [1.20]; P = .01, U = 12 568.5) but were similar to HCPs on information content (mean [SD], 3.53 [1.26] vs 3.41 [1.27]; P = .37; U = 13 981.0) and usable draft proportion (mean [SD], 0.69 [0.48] vs 0.65 [0.47], P = .49, t = -0.6842). Usable GenAI responses were considered more empathetic than usable HCP responses (32 of 86 [37.2%] vs 13 of 79 [16.5%]; difference, 125.5%), possibly attributable to more subjective (mean [SD], 0.54 [0.16] vs 0.31 [0.23]; P < .001; difference, 74.2%) and positive (mean [SD] polarity, 0.21 [0.14] vs 0.13 [0.25]; P = .02; difference, 61.5%) language; they were also numerically longer (mean [SD] word count, 90.5 [32.0] vs 65.4 [62.6]; difference, 38.4%), but the difference was not statistically significant (P = .07) and more linguistically complex (mean [SD] score, 125.2 [47.8] vs 95.4 [58.8]; P = .002; difference, 31.2%). CONCLUSIONS/UNASSIGNED:In this cross-sectional study of PCP perceptions of an EHR-integrated GenAI chatbot, GenAI was found to communicate information better and with more empathy than HCPs, highlighting its potential to enhance patient-HCP communication. However, GenAI drafts were less readable than HCPs', a significant concern for patients with low health or English literacy.

INTRODUCTION/UNASSIGNED:Shared decision-making (SDM) is a method of care by which patients and clinicians work together to co-create a plan of care. Electronic health record (EHR) integration of SDM tools may increase adoption of SDM. We conducted a "lightweight" integration of a freely available electronic SDM tool, CV Prevention Choice, within the EHRs of three healthcare systems. Here, we report how the healthcare systems collaborated to achieve integration. METHODS/UNASSIGNED:This work was conducted as part of a stepped wedge randomized pragmatic trial. CV Prevention Choice was developed using guidelines for HTML5-based web applications. Healthcare systems integrated the tool in their EHR using documentation the study team developed and refined with lessons learned after each system integrated the electronic SDM tool into their EHR. CV Prevention Choice integration populates the tool with individual patient data locally without sending protected health information between the EHR and the web. Data abstraction and secure transfer systems were developed to manage data collection to assess tool implementation and effectiveness outcomes. RESULTS/UNASSIGNED:Time to integrate CV Prevention Choice in the EHR was 12.1 weeks for the first system, 10.4 weeks for the second, and 9.7 weeks for the third. One system required two 1-hour meetings with study team members and two healthcare systems required a single 1-hour meeting. Healthcare system information technology teams collaborated by sharing information and offering improvements to documentation. Challenges included tracking CV Prevention Choice use for reporting and capture of combination medications. Data abstraction required refinements to address differences in how each healthcare system captured data elements. CONCLUSION/UNASSIGNED:Targeted documentation on tool features and resource mapping supported collaboration of IT teams across healthcare systems, enabling them to integrate a web-based SDM tool with little additional research team effort or oversight. Their collaboration helped overcome difficulties integrating the web application and address challenges to data harmonization for trial outcome analyses.

BACKGROUND:Up to 50% of antibiotic prescriptions for upper respiratory infections (URIs) are inappropriate. Clinical decision support (CDS) systems to mitigate unnecessary antibiotic prescriptions have been implemented into electronic health records, but their use by providers has been limited. OBJECTIVE:As a delegation protocol, we adapted a validated electronic health record-integrated clinical prediction rule (iCPR) CDS-based intervention for registered nurses (RNs), consisting of triage to identify patients with low-acuity URI followed by CDS-guided RN visits. It was implemented in February 2022 as a randomized controlled stepped-wedge trial in 43 primary and urgent care practices within 4 academic health systems in New York, Wisconsin, and Utah. While issues were pragmatically addressed as they arose, a systematic assessment of the barriers to implementation is needed to better understand and address these barriers. METHODS:We performed a retrospective case study, collecting quantitative and qualitative data regarding clinical workflows and triage-template use from expert interviews, study surveys, routine check-ins with practice personnel, and chart reviews over the first year of implementation of the iCPR intervention. Guided by the updated CFIR (Consolidated Framework for Implementation Research), we characterized the initial barriers to implementing a URI iCPR intervention for RNs in ambulatory care. CFIR constructs were coded as missing, neutral, weak, or strong implementation factors. RESULTS:Barriers were identified within all implementation domains. The strongest barriers were found in the outer setting, with those factors trickling down to impact the inner setting. Local conditions driven by COVID-19 served as one of the strongest barriers, impacting attitudes among practice staff and ultimately contributing to a work infrastructure characterized by staff changes, RN shortages and turnover, and competing responsibilities. Policies and laws regarding scope of practice of RNs varied by state and institutional application of those laws, with some allowing more clinical autonomy for RNs. This necessitated different study procedures at each study site to meet practice requirements, increasing innovation complexity. Similarly, institutional policies led to varying levels of compatibility with existing triage, rooming, and documentation workflows. These workflow conflicts were compounded by limited available resources, as well as an implementation climate of optional participation, few participation incentives, and thus low relative priority compared to other clinical duties. CONCLUSIONS:Both between and within health care systems, significant variability existed in workflows for patient intake and triage. Even in a relatively straightforward clinical workflow, workflow and cultural differences appreciably impacted intervention adoption. Takeaways from this study can be applied to other RN delegation protocol implementations of new and innovative CDS tools within existing workflows to support integration and improve uptake. When implementing a system-wide clinical care intervention, considerations must be made for variability in culture and workflows at the state, health system, practice, and individual levels. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04255303; https://clinicaltrials.gov/ct2/show/NCT04255303.

Digital health implementations and investments continue to expand. As the reliance on digital health increases, it is imperative to implement technologies with inclusive and accessible approaches. A conceptual model can be used to guide equity-focused digital health implementations to improve suitability and uptake in diverse populations. The objective of this study is expand an implementation model with recommendations on the equitable implementation of new digital health technologies. The Digital Health Equity-Focused Implementation Research (DH-EquIR) conceptual model was developed based on a rigorous review of digital health implementation and health equity literature. The Equity-Focused Implementation Research for Health Programs (EquIR) model was used as a starting point and merged with digital equity and digital health implementation models. Existing theoretical frameworks and models were appraised as well as individual equity-sensitive implementation studies. Patient and program-related concepts related to digital equity, digital health implementation, and assessment of social/digital determinants of health were included. Sixty-two articles were analyzed to inform the adaption of the EquIR model for digital health. These articles included digital health equity models and frameworks, digital health implementation models and frameworks, research articles, guidelines, and concept analyses. Concepts were organized into EquIR conceptual groupings, including population health status, planning the program, designing the program, implementing the program, and equity-focused implementation outcomes. The adapted DH-EquIR conceptual model diagram was created as well as detailed tables displaying related equity concepts, evidence gaps in source articles, and analysis of existing equity-related models and tools. The DH-EquIR model serves to guide digital health developers and implementation specialists to promote the inclusion of health-equity planning in every phase of implementation. In addition, it can assist researchers and product developers to avoid repeating the mistakes that have led to inequities in the implementation of digital health across populations.

BACKGROUND:Currently, only about half of U.S. adults achieve current physical activity guidelines. Routine physical activity is not regularly assessed, nor are patients routinely counseled by their health care provider on achieving recommended levels. The three-question physical activity vital sign (PAVS) was developed to assess physical activity duration and intensity and identify adults not meeting physical activity guidelines. Clinical decision support provided via a best practice advisory in an electronic health record (EHR) system can be triggered as a prompt, reminding health care providers to implement the best practice intervention when appropriate. Remote patient monitoring of physical activity can provide objective data in the EHR. OBJECTIVES/OBJECTIVE:This study aimed to evaluate the feasibility and clinical utility of embedding the PAVS and a triggered best practice advisor into the EHR in an ambulatory preventive cardiology practice setting to alert providers to patients reporting low physical activity and prompt health care providers to counsel these patients as needed. METHODS:Three components based in the EHR were integrated for the purpose of this study: patients completed the PAVS through their electronic patient portal prior to an office visit; a best practice advisory was created to prompt providers to counsel patients who reported low levels of physical activity; and remote patient monitoring via Fitbit synced to the EHR provided objective physical activity data. The intervention was pilot-tested in the Epic EHR for 1 year (July 1, 2021-June 30, 2022). Qualitative feedback on the intervention from both providers and patients was obtained at the completion of the study. RESULTS:Monthly assessments of the use of the PAVS and best practice advisory and remote patient monitoring were completed. Patients' completion of the PAVS varied from 35% to 48% per month. The best practice advisory was signed by providers between 2% and 65% and was acknowledged by 2% to 22% per month. The majority (58%) of patients were able to sync a Fitbit device to their EHR for remote monitoring. DISCUSSION/CONCLUSIONS:Although uptake of each component needs improvement, this pilot demonstrated the feasibility of incorporating a PA promotion intervention into the EHR. Qualitative feedback provided guidance for future implementation.

BACKGROUND:Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE:This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS:We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS:Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS:ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.