Faculty Bibliography

BACKGROUND:The integration of behavioral economics (BE) principles and electronic health records (EHRs) using clinical decision support (CDS) tools is a novel approach to improving health outcomes. Meanwhile, the American Geriatrics Society has created the Choosing Wisely (CW) initiative to promote less aggressive glycemic targets and reduction in pharmacologic therapy in older adults with type 2 diabetes mellitus. To date, few studies have shown the effectiveness of combined BE and EHR approaches for managing chronic conditions, and none have addressed guideline-driven deprescribing specifically in type 2 diabetes. We previously conducted a pilot study aimed at promoting appropriate CW guideline adherence using BE nudges and EHRs embedded within CDS tools at 5 clinics within the New York University Langone Health (NYULH) system. The BE-EHR module intervention was tested for usability, adoption, and early effectiveness. Preliminary results suggested a modest improvement of 5.1% in CW compliance. OBJECTIVE:This paper presents the protocol for a study that will investigate the effectiveness of a BE-EHR module intervention that leverages BE nudges with EHR technology and CDS tools to reduce overtreatment of type 2 diabetes in adults aged 76 years and older, per the CW guideline. METHODS:A pragmatic, investigator-blind, cluster randomized controlled trial was designed to evaluate the BE-EHR module. A total of 66 NYULH clinics will be randomized 1:1 to receive for 18 months either (1) a 6-component BE-EHR module intervention + standard care within the NYULH EHR, or (2) standard care only. The intervention will be administered to clinicians during any patient encounter (eg, in person, telemedicine, medication refill, etc). The primary outcome will be patient-level CW compliance. Secondary outcomes will measure the frequency of intervention component firings within the NYULH EHR, and provider utilization and interaction with the BE-EHR module components. RESULTS:Study recruitment commenced on December 7, 2020, with the activation of all 6 BE-EHR components in the NYULH EHR. CONCLUSIONS:This study will test the effectiveness of a previously developed, iteratively refined, user-tested, and pilot-tested BE-EHR module aimed at providing appropriate diabetes care to elderly adults, compared to usual care via a cluster randomized controlled trial. This innovative research will be the first pragmatic randomized controlled trial to use BE principles embedded within the EHR and delivered using CDS tools to specifically promote CW guideline adherence in type 2 diabetes. The study will also collect valuable information on clinician workflow and interaction with the BE-EHR module, guiding future research in optimizing the timely delivery of BE nudges within CDS tools. This work will address the effectiveness of BE-inspired interventions in diabetes and chronic disease management. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04181307; https://clinicaltrials.gov/ct2/show/NCT04181307. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/28723.

The aims were to implement physical activity (PA) screening as part of the electronic kiosk check-in process in an adult preventive cardiology clinic and assess factors related to patients' self-reported PA. The 3-question physical activity vital sign (PAVS) was embedded in the Epic electronic medical record and included how many days, minutes and intensity (light, moderate, vigorous) of PA patients conducted on average. This is a data analysis of PAVS data over a 60-day period. We conducted multivariable logistic regression to identify factors associated with not meeting current PA recommendations. Over 60 days, a total of 1322 patients checked into the clinic using the kiosk and 72% (n = 951) completed the PAVS at the kiosk. The majority of those patients were male (58%) and White (71%) with a mean age of 64 ± 15 years. Of the 951 patients completing the PAVS, 10% reported no PA, 55% reported some PA, and 35% reported achieving at least 150 min moderate or 75 min vigorous PA/week. In the logistic model, females (AOR = 1.4, 95%CI: 1.002-1.8, p = .049) vs. males, being Black (AOR = 2.0, 95%CI: 1.04-3.7, p = .038) or 'Other' race (AOR = 1.5, 95%CI: 1.02-2.3, p = .035) vs. White, unknown or other types of relationships (AOR = 0.0.26, 95%CI: 0.10-0.68, p = .006) vs. being married/partnered, and those who were retired (AOR = 1.9, 95% CI: 1.4-2.8, p < .001) or unemployed (AOR = 2.2, 95%CI: 1.3-3.7, p = .002) vs. full-time workers were associated with not achieving recommended levels of PA. The PAVS is a feasible electronic tool for quickly assessing PA and may prompt providers to counsel on this CVD risk factor.

BACKGROUND:Telemedicine in obstetrics has mostly been described in the rural areas that have limited access to subspecialties. During the COVID-19 pandemic, health systems rapidly expanded telemedicine services for urgent and nonurgent healthcare delivery, even in urban settings. The New York University health system implemented a prompt systemwide expansion of video-enabled telemedicine visits, increasing telemedicine to >8000 visits daily within 6 weeks of the beginning of the pandemic. There are limited studies that explore patient and provider satisfaction of telemedicine visits in obstetrical patients during the COVID-19 epidemic, particularly in the United States. OBJECTIVE:This study aimed to evaluate both the patients' and the providers' satisfaction with the administration of maternal-fetal medicine services through telemedicine and to identify the factors that drive the patients' desire for future obstetrical telemedicine services. STUDY DESIGN/METHODS:A cross-sectional survey was administered to patients who completed a telemedicine video visit with the Division of Maternal-Fetal Medicine at the New York University Langone Hospital-Long Island from March 19, 2020, to May 26, 2020. A 10-question survey assessing the patients' digital experience and desire for future use was either administered by telephone or self-administered by the patients via a link after obtaining verbal consent. The survey responses were scored from 1-strongly disagree to 5-strongly agree. We analyzed the demographics and survey responses of the patients who agreed to vs those who answered neutral or disagree to the question "I would like telehealth to be an option for future obstetric visits." The providers also answered a similar 10-question survey. The median scores were compared using appropriate tests. A P value of <.05 was considered significant. RESULTS:A total of 253 patients participated in 433 telemedicine visits, and 165 patients completed the survey, resulting in a 65% survey response rate. Overall, there were high rates of patient satisfaction in all areas assessed. Those who desired future telemedicine had significantly greater agreeability that they were able to see and hear their provider easily (5 [4.5, 5] vs 5 [4, 5]; P=.014) and that the lack of physical activity was not an issue (5 [4, 5] vs 5 [4, 5]; P=.032). They were also more likely to agree that the telemedicine visits were as good as in-person visits (4 [3, 5] vs 3 [2, 3]; P<.001) and that telehealth made it easier for them to see doctors or specialists (5 [4, 5] vs 3 [2, 3]; P<.001). The patients seeking consults for poor obstetrical history were more likely to desire future telemedicine compared with other visit types (19 (90%) vs 2 (10%); P=.05). Provider survey responses also demonstrated high levels of satisfaction, with 83% agreeing that they would like telemedicine to be an option for future obstetrical visits. CONCLUSION/CONCLUSIONS:We demonstrated that maternal-fetal medicine obstetrical patients and providers were highly satisfied with the implementation of telemedicine during the initial wave of the COVID-19 pandemic and a majority of them desire telemedicine as an option for future visits. A patient's desire for future telemedicine visits was significantly affected by their digital experience, the perception of a lack of need for physical contact, perceived time saved on travel, and access to healthcare providers. Health systems need to continue to improve healthcare delivery and invest in innovative solutions to conduct physical examinations remotely.

BACKGROUND:Advances in genetics and sequencing technologies are enabling the identification of more individuals with inherited cancer susceptibility who could benefit from tailored screening and prevention recommendations. While cancer family history information is used in primary care settings to identify unaffected patients who could benefit from a cancer genetics evaluation, this information is underutilized. System-level population health management strategies are needed to assist health care systems in identifying patients who may benefit from genetic services. In addition, because of the limited number of trained genetics specialists and increasing patient volume, the development of innovative and sustainable approaches to delivering cancer genetic services is essential. METHODS:We are conducting a randomized controlled trial, entitled Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE), to address these needs. The trial is comparing uptake of genetic counseling, uptake of genetic testing, and patient adherence to management recommendations for automated, patient-directed versus enhanced standard of care cancer genetics services delivery models. An algorithm-based system that utilizes structured cancer family history data available in the electronic health record (EHR) is used to identify unaffected patients who receive primary care at the study sites and meet current guidelines for cancer genetic testing. We are enrolling eligible patients at two healthcare systems (University of Utah Health and New York University Langone Health) through outreach to a randomly selected sample of 2780 eligible patients in the two sites, with 1:1 randomization to the genetic services delivery arms within sites. Study outcomes are assessed through genetics clinic records, EHR, and two follow-up questionnaires at 4 weeks and 12 months after last genetic counseling contactpre-test genetic counseling. DISCUSSION/CONCLUSIONS:BRIDGE is being conducted in two healthcare systems with different clinical structures and patient populations. Innovative aspects of the trial include a randomized comparison of a chatbot-based genetic services delivery model to standard of care, as well as identification of at-risk individuals through a sustainable EHR-based system. The findings from the BRIDGE trial will advance the state of the science in identification of unaffected patients with inherited cancer susceptibility and delivery of genetic services to those patients. TRIAL REGISTRATION/BACKGROUND:BRIDGE is registered as NCT03985852 . The trial was registered on June 6, 2019 at clinicaltrials.gov .

BACKGROUND:Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. OBJECTIVE:This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. METHODS:A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. RESULTS:To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. CONCLUSIONS:These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. TRIAL REGISTRATION/BACKGROUND:Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191.

The transformation of patient-clinician relationships with AI-based medical advice is discussed. many new tools are based on entirely new "˜black-box"™ AI-based technologies, whose inner workings are likely not fully understood by patients or clinicians. Most patients with Type 1 diabetes now use continuous glucose monitors and insulin pumps to tightly manage their disease. Their clinicians carefully review the data streams from both devices to recommend dosage adjustments. Recently new automated recommender systems to monitor and analyze food intake, insulin doses, physical activity, and other factors influencing glucose levels, and provide data-intensive, AI-based recommendations on how to titrate the regimen, are in different stages of FDA approval using "˜black box"™ technology, which is an alluring proposition for a clinical scenario that requires identification of meaningful patterns in complex and voluminous data.

BACKGROUND:Digital diabetes prevention programs (dDPPs) are effective behavior change tools to prevent disease progression in patients at risk for diabetes. At present, these programs are poorly integrated into existing health information technology infrastructure and clinical workflows, resulting in barriers to provider-level knowledge of, interaction with, and support of patients who use dDPPs. Tools that can facilitate patient-provider interaction around dDPPs may contribute to improved patient engagement and adherence to these programs and improved health outcomes. OBJECTIVE:This study aims to use a rigorous, user-centered design (UCD) methodology to develop a theory-driven system that supports patient engagement with dDPPs and their primary care providers with their care. METHODS:at 6 and 12 months. Secondary outcomes will be patient engagement (use and activity) in the dDPP. The mediator variables (self-efficacy, digital health literacy, and patient-provider relationship) will be measured. RESULTS:The project was initiated in 2018 and funded in September 2019. Enrollment and data collection for phase 1 began in September 2019 under an Institutional Review Board quality improvement waiver granted in July 2019. As of December 2020, 27 patients have been enrolled and first results are expected to be submitted for publication in early 2021. The study received Institutional Review Board approval for phases 2 and 3 in December 2020, and phase 2 enrollment is expected to begin in early 2021. CONCLUSIONS:Our findings will provide guidance for the design and development of technology to integrate dDPP platforms into existing clinical workflows. This will facilitate patient engagement in digital behavior change interventions and provider engagement in patients' use of dDPPs. Integrated clinical tools that can facilitate patient-provider interaction around dDPPs may contribute to improved patient adherence to these programs and improved health outcomes by addressing barriers faced by both patients and providers. Further evaluation with pilot testing and a clinical trial will assess the effectiveness and implementation of these tools. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/26750.

BACKGROUND:The transformation of health care during COVID-19, with the rapid expansion of telemedicine visits, presents new challenges to chronic care and preventive health providers. Clinical decision support (CDS) is critically important to chronic care providers, and CDS malfunction is common during times of change. It is essential to regularly reassess an organization's ambulatory CDS program to maintain care quality. This is especially true after an immense change, like the COVID-19 telemedicine expansion. OBJECTIVE:Our objective is to reassess the ambulatory CDS program at a large academic medical center in light of telemedicine's expansion in response to the COVID-19 pandemic. METHODS:Our clinical informatics team devised a practical framework for an intrapandemic ambulatory CDS assessment focused on the impact of the telemedicine expansion. This assessment began with a quantitative analysis comparing CDS alert performance in the context of in-person and telemedicine visits. Board-certified physician informaticists then completed a formal workflow review of alerts with inferior performance in telemedicine visits. Informaticists then reported on themes and optimization opportunities through the existing CDS governance structure. RESULTS:Our assessment revealed that 10 of our top 40 alerts by volume were not firing as expected in telemedicine visits. In 3 of the top 5 alerts, providers were significantly less likely to take action in telemedicine when compared to office visits. Cumulatively, alerts in telemedicine encounters had an action taken rate of 5.3% (3257/64,938) compared to 8.3% (19,427/233,636) for office visits. Observations from a clinical informaticist workflow review included the following: (1) Telemedicine visits have different workflows than office visits. Some alerts developed for the office were not appearing at the optimal time in the telemedicine workflow. (2) Missing clinical data is a common reason for the decreased alert firing seen in telemedicine visits. (3) Remote patient monitoring and patient-reported clinical data entered through the portal could replace data collection usually completed in the office by a medical assistant or registered nurse. CONCLUSIONS:In a large academic medical center at the pandemic epicenter, an intrapandemic ambulatory CDS assessment revealed clinically significant CDS malfunctions that highlight the importance of reassessing ambulatory CDS performance after the telemedicine expansion.

OBJECTIVE:Through the coronavirus disease 2019 (COVID-19) pandemic, telemedicine became a necessary entry point into the process of diagnosis, triage and treatment. Racial and ethnic disparities in health care have been well documented in COVID-19 with respect to risk of infection and in-hospital outcomes once admitted, and here we assess disparities in those who access healthcare via telemedicine for COVID-19 . MATERIALS AND METHODS/METHODS:Electronic health record data of patients at New York University Langone Health between March 19th and April 30, 2020 were used to conduct descriptive and multilevel regression analyses with respect to visit type (telemedicine or in-person), suspected COVID diagnosis and COVID test results. RESULTS:Controlling for individual and community-level attributes, Black patients had 0.6 times the adjusted odds (95%CI:0.58-0.63) of accessing care through telemedicine compared to white patients, though they are increasingly accessing telemedicine for urgent care, driven by a younger and female population. COVID diagnoses were significantly more likely for Black versus white telemedicine patients. DISCUSSION/CONCLUSIONS:There are disparities for Black patients accessing telemedicine, however increased uptake by young, female Black patients. Mean income and decreased mean household size of Zip code were also significantly related to telemedicine use. CONCLUSION/CONCLUSIONS:Telemedicine access disparities reflect those in in-person healthcare access. Roots of disparate use are complex and reflect individual, community, and structural factors, including their intersection; many of which are due to systemic racism. Evidence regarding disparities that manifest through telemedicine can be used to inform tool design and systemic efforts to promote digital health equity.