Faculty Bibliography

STATEMENT OF PROBLEM OR QUESTION (ONE SENTENCE): In the transition away from traditional doctors' offices, how can we optimize shared multi-use clinical spaces to serve clinicians' needs LEARNING OBJECTIVES 1: Identify ways in which a practice that relies upon shared clinical spaces can remain familiar and effective for clinical work. LEARNING OBJECTIVES 2: Determine how might technology help clinicians develop a sense of belonging, professional pride, and patient rapport in multi-use spaces by allowing them to display personal information and patient education materials related to their practice. DESCRIPTION OF PROGRAM/INTERVENTION, INCLUDING ORGANIZATIONAL CONTEXT (E.G. INPATIENT VS. OUTPATIENT, PRACTICE OR COMMUNITY CHARACTERISTICS): The traditional doctor's office is being rapidly replaced by multi-use clinical environments that combine exam rooms with shared touchdown spaces, promoting efficient use of space & team-based care approach while utilizing network technologies. While potentially efficient & lower-cost, there's a need to assess the impact of these configurations on clinician workflows, professional identity & explore opportunities to improve their build and aesthetics. We conducted need assessment interviews with 9 clinicians, health technologists, 2 operational leaders, shadowed 3 clinicians & conducted 4 site visits across various clinical practices. We then issued a 10-question survey and conducted 2 HCD workshops with 12 clinicians to understand the new conditions of clinical work, their impact on clinicians' professional & personal identity, practice habits, to identify areas for potential optimization to improve clinical workflow & experience. Workshops were divided in three phases: explore, ideate and create. MEASURES OF SUCCESS (DISCUSS QUALITATIVE AND/OR QUANTITATIVEMETRICSWHICHWILL BE USEDTOEVALUATE PROGRAM/INTERVENTION): We report qualitative success metrics used to evaluate the results of the HCD workshops: 1. Understanding of what shared multi-use work spaces mean to participating clinicians. 2. Identified needs, potential concerns and pain points of clinicians and stakeholders. 3. Group generation of potential solutions without bias towards feasibility. 4. Described solutions using quick prototyping tools. FINDINGS TO DATE (IT IS NOT SUFFICIENT TO STATE FINDINGS WILL BE DISCUSSED): Clinicians identified the lack of customization and capability for sharing information about their areas of expertise and tailored patient education materials as the most significant problem, and had privacy concerns about sharing personal information on a digital display. Potential solutions include customizable content display controlled by patients that fosters engagement, exploring education materials, patient testimonials, information about the care team and wait time as well as patient-specific information, such as labs and imaging. KEY LESSONS FOR DISSEMINATION (WHAT CAN OTHERS TAKE AWAY FOR IMPLEMENTATION TO THEIR PRACTICE OR COMMUNITY): The use of the HCD principles helped us better understand the challenges of multi-use spaces for clinicians, and identify potential technology solutions for data sharing, patient education, personalization, and efficiencies. It is crucial to design these spaces and choose appropriate technology solutions that will help reduce patients' anxiety by ensuring privacy, comfort, thorough understanding of care plans and boost collaborative care decision making between clinicians and patients

BACKGROUND:Clinical decision support (CDS) is a promising tool for reducing antibiotic prescribing for acute respiratory infections (ARIs). OBJECTIVE:To assess the impact of previously effective CDS on antibiotic-prescribing rates for ARIs when adapted and implemented in diverse primary care settings. DESIGN/METHODS:Cluster randomized clinical trial (RCT) implementing a CDS tool designed to guide evidence-based evaluation and treatment of streptococcal pharyngitis and pneumonia. SETTING/METHODS:Two large academic health system primary care networks with a mix of providers. PARTICIPANTS/METHODS:All primary care practices within each health system were invited. All providers within participating clinic were considered a participant. Practices were randomized selection to a control or intervention group. INTERVENTIONS/METHODS:Intervention practice providers had access to an integrated clinical prediction rule (iCPR) system designed to determine the risk of bacterial infection from reason for visit of sore throat, cough, or upper respiratory infection and guide evidence-based evaluation and treatment. MAIN OUTCOME(S)/UNASSIGNED:Change in overall antibiotic prescription rates. MEASURE(S)/UNASSIGNED:Frequency, rates, and type of antibiotics prescribed in intervention and controls groups. RESULTS:33 primary care practices participated with 541 providers and 100,573 patient visits. Intervention providers completed the tool in 6.9% of eligible visits. Antibiotics were prescribed in 35% and 36% of intervention and control visits, respectively, showing no statistically significant difference. There were also no differences in rates of orders for rapid streptococcal tests (RR, 0.94; P = 0.11) or chest X-rays (RR, 1.01; P = 0.999) between groups. CONCLUSIONS:The iCPR tool was not effective in reducing antibiotic prescription rates for upper respiratory infections in diverse primary care settings. This has implications for the generalizability of CDS tools as they are adapted to heterogeneous clinical contexts. TRIAL REGISTRATION/BACKGROUND:Clinicaltrials.gov (NCT02534987). Registered August 26, 2015 at https://clinicaltrials.gov.

BACKGROUND:Intensive glycemic control is of unclear benefit and carries increased risk for older adults with diabetes. The American Geriatrics Society's (AGS) Choosing Wisely (CW) guideline promotes less aggressive glycemic targets and reduction in pharmacologic therapy for older adults with type II diabetes. Meanwhile, behavioral economic (BE) approaches offer promise in influencing hard-to-change behavior, and previous studies have shown the benefits of using electronic health record (EHR) technology to encourage guideline adherence. OBJECTIVE:This study aimed to develop and pilot test an intervention that leverages BE with EHR technology to promote appropriate diabetes management in older adults. DESIGN/METHODS:A pilot study within the New York University Langone Health (NYULH) EHR and Epic system to deliver BE-inspired nudges at five NYULH clinics at varying time points from July 12, 2018, through October 31, 2019. PARTICIPANTS/METHODS:Clinicians across five practices in the NYULH system whose patients were older adults (age 76 and older) with type II diabetes. INTERVENTIONS/METHODS:A BE-EHR module comprising six nudges was developed through a series of design workshops, interviews, user-testing sessions, and clinic visits. BE principles utilized in the nudges include framing, social norming, accountable justification, defaults, affirmation, and gamification. MAIN MEASURES/METHODS:Patient-level CW compliance. KEY RESULTS/RESULTS:CW compliance increased 5.1% from a 16-week interval at baseline to a 16-week interval post intervention. From February 14 to June 5, 2018 (prior to the first nudge launch in Vanguard clinics), CW compliance for 1278 patients was mean (95% CI)-16.1% (14.1%, 18.1%). From July 3 to October 22, 2019 (after BE-EHR module launch at all five clinics), CW compliance for 680 patients was 21.2% (18.1%, 24.3%). CONCLUSIONS:The BE-EHR module shows promise for promoting the AGS CW guideline and improving diabetes management in older adults. A randomized controlled trial will commence to test the effectiveness of the intervention across 66 NYULH clinics. NIH TRIAL REGISTRY NUMBER/UNASSIGNED:NCT03409523.

Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.

BACKGROUND:Patient-reported outcomes (PROs) are increasingly being used in the management of type 2 diabetes (T2D) to integrate data from patients' perspective into clinical care. To date, the majority of PRO tools have lacked patient and provider involvement in their development, thus failing to meet the unique needs of end users, and lack the technical infrastructure to be integrated into the clinic workflow. OBJECTIVE:This study aims to apply a systematic, user-centered design approach to develop i-Matter (investigating a mobile health [mHealth] texting tool for embedding patient-reported data into diabetes management), a theory-driven, mobile PRO system for patients with T2D and their primary care providers. METHODS:i-Matter combines text messaging with dynamic data visualizations that can be integrated into electronic health records (EHRs) and personalized patient reports. To build i-Matter, we conducted semistructured group and individual interviews with patients with T2D and providers, a design thinking workshop to refine initial ideas and design the prototype, and user testing sessions of prototypes using a rapid-cycle design (ie, design-test-modify-retest). RESULTS:Using an iterative user-centered process resulted in the identification of 6 PRO messages that were relevant to patients and providers: medication adherence, dietary behaviors, physical activity, sleep quality, quality of life, and healthy living goals. In user testing, patients recommended improvements to the wording and timing of the PRO text messages to increase clarity and response rates. Patients also recommended including motivational text messages to help sustain engagement with the program. The personalized report was regarded as a key tool for diabetes self-management by patients and providers because it aided in the identification of longitudinal patterns in the PRO data, which increased patient awareness of their need to adopt healthier behaviors. Patients recommended adding individualized tips to the journal on how they can improve their behaviors. Providers preferred having a separate tab built into the EHR that included the personalized report and highlighted key trends in patients' PRO data over the past 3 months. CONCLUSIONS:PRO tools that capture patients' well-being and the behavioral aspects of T2D management are important to patients and providers. A clinical trial will test the efficacy of i-Matter in 282 patients with uncontrolled T2D. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT03652389; https://clinicaltrials.gov/ct2/show/NCT03652389.

This study provides data on the feasibility and impact of video-enabled telemedicine use among patients and providers and its impact on urgent and non-urgent health care delivery from one large health system (NYU Langone Health) at the epicenter of the COVID-19 outbreak in the United States. Between March 2nd and April 14th 2020, telemedicine visits increased from 369.1 daily to 866.8 daily (135% increase) in urgent care after the system-wide expansion of virtual health visits in response to COVID-19, and from 94.7 daily to 4209.3 (4345% increase) in non-urgent care post expansion. Of all virtual visits post expansion, 56.2% and 17.6% urgent and non-urgent visits, respectively, were COVID-19-related. Telemedicine usage was highest by patients aged 20-44, particularly for urgent care. The COVID-19 pandemic has driven rapid expansion of telemedicine use for urgent care and non-urgent care visits beyond baseline periods. This reflects an important change in telemedicine that other institutions facing the COVID-19 pandemic should anticipate.

Background: Gastric cancer is the third most common cause of cancer death worldwide. In the US, gastric cancer incidence for Chinese Americans is nearly twice that for non-Hispanic whites. Cancer is the leading cause of death among Chinese New Yorkers who experience higher mortality for gastric cancer than other New Yorkers overall. The bacterium Helicobacter pylori (H. pylori) is the strongest risk factor for gastric cancer, and eradication of H. pylori through triple antibiotic therapy is the most effective prevention strategy for gastric cancer. Despite the elevated burden, there are no culturally and linguistically tailored evidence-based intervention strategies to address H. pylori medication adherence and gastric cancer prevention for Chinese Americans in NYC, a largely foreign-born (72%), limited English proficient (61%), and low-income (21% living in poverty) population.
Objective(s): The study objective was to develop and pilot a community health worker (CHW)-delivered linguistically and culturally adapted gastric cancer prevention intervention to improve H. pylori treatment adherence and address modifiable cancer prevention risk factors, including improved nutrition for low-income, LEP, Chinese American immigrants.
Method(s): We used a mixed methods and community-engaged research approach to develop and pilot the intervention curriculum and materials. Methods included: 1) a comprehensive scoping review of the peer-reviewed and grey literature on gastric cancer prevention programs and strategies targeting Chinese Americans; 2) 15 key informant interviews with gatekeepers and stakeholders serving the New York Chinese immigrant community to assess the knowledge and perception of H. pylori infection and gastric cancer among Chinese New Yorkers; and 3) pilot implementation of the collaboratively developed intervention with H. pylori-infected LEP Chinese immigrant participants (n=7).
Result(s): Study process findings and pilot results will be presented. Preliminary results indicate high patient- and community-level need and acceptability for the intervention. Baseline and 1-month post-treatment outcomes and survey data, qualitative data analysis of the CHW session notes, and key informant interviews will be presented.
Conclusion(s): Findings suggest that a CHW-delivered culturally adapted gastric cancer prevention intervention can result in meaningful health information and treatment adherence for at-risk, low-income Chinese immigrant communities. Study findings are being applied to inform a randomized controlled trial being implemented in safety net hospital settings

BACKGROUND:The seminal Dietary Approaches to Stopping Hypertension (DASH) study demonstrated the effectiveness of diet to control hypertension; however, the effective implementation and dissemination of its principles have been limited. OBJECTIVE:This study aimed to determine the feasibility and effectiveness of a DASH mobile health intervention. We hypothesized that combining Bluetooth-enabled data collection, social networks, and a human coach with a smartphone DASH app (DASH Mobile) would be an effective medium for the delivery of the DASH program. METHODS:We conducted a single-arm pilot study from August 2015 through August 2016, using a pre-post evaluation design to evaluate the feasibility and preliminary effectiveness of a smartphone version of DASH that incorporated a human health coach. Participants were recruited both online and offline. RESULTS:A total of 17 patients participated in this study; they had a mean age of 59 years (SD 6) and 10 (60%) were women. Participants were engaged with the app; in the 120 days of the study, the mean number of logged blood pressure measurements was 63 (SD 46), the mean number of recorded weight measurements was 52 (SD 45), and participants recorded a mean of 55 step counts (SD 36). Coaching phone calls had a high completion rate (74/102, 73%). The mean number of servings documented per patient for the dietary assessment was 709 (SD 541), and patients set a mean number of 5 (SD 2) goals. Mean systolic and diastolic blood pressure, heart rate, weight, body mass index, and step count did not significantly change over time (P>.10 for all parameters). CONCLUSIONS:In this pilot study, we found that participants were engaged with an interactive mobile app that promoted healthy behaviors to treat hypertension. We did not find a difference in the physiological outcomes, but were underpowered to identify such changes.

BACKGROUND:Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)-integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician's interaction with these alerts in general. OBJECTIVE:This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts. METHODS:We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician's interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients. RESULTS:During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20). CONCLUSIONS:All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.