Faculty Bibliography

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.

BACKGROUND:The emergency department is a critical juncture in the trajectory of care of patients with serious, life-limiting illness. Implementation of a clinical decision support (CDS) tool automates identification of older adults who may benefit from palliative care instead of relying upon providers to identify such patients, thus improving quality of care by assisting providers with adhering to guidelines. The Primary Palliative Care for Emergency Medicine (PRIM-ER) study aims to optimize the use of the electronic health record by creating a CDS tool to identify high risk patients most likely to benefit from primary palliative care and provide point-of-care clinical recommendations. METHODS:A clinical decision support tool entitled Emergency Department Supportive Care Clinical Decision Support (Support-ED) was developed as part of an institutionally-sponsored value based medicine initiative at the Ronald O. Perelman Department of Emergency Medicine at NYU Langone Health. A multidisciplinary approach was used to develop Support-ED including: a scoping review of ED palliative care screening tools; launch of a workgroup to identify patient screening criteria and appropriate referral services; initial design and usability testing via the standard System Usability Scale questionnaire, education of the ED workforce on the Support-ED background, purpose and use, and; creation of a dashboard for monitoring and feedback. RESULTS:The scoping review identified the Palliative Care and Rapid Emergency Screening (P-CaRES) survey as a validated instrument in which to adapt and apply for the creation of the CDS tool. The multidisciplinary workshops identified two primary objectives of the CDS: to identify patients with indicators of serious life limiting illness, and to assist with referrals to services such as palliative care or social work. Additionally, the iterative design process yielded three specific patient scenarios that trigger a clinical alert to fire, including: 1) when an advance care planning document was present, 2) when a patient had a previous disposition to hospice, and 3) when historical and/or current clinical data points identify a serious life-limiting illness without an advance care planning document present. Monitoring and feedback indicated a need for several modifications to improve CDS functionality. CONCLUSIONS:CDS can be an effective tool in the implementation of primary palliative care quality improvement best practices. Health systems should thoughtfully consider tailoring their CDSs in order to adapt to their unique workflows and environments. The findings of this research can assist health systems in effectively integrating a primary palliative care CDS system seamlessly into their processes of care. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov Identifier: NCT03424109. Registered 6 February 2018, Grant Number: AT009844-01.

Objectives: We will: 1) describe the WonderLab, a digital health initiative within the New York University Langone Health Department of Child and Adolescent Psychiatry; 2) introduce When to Wonder: Picky Eating, which is the WonderLab's first early childhood mental health digital study; and 3) present preliminary data from this study. Our first objective is to demonstrate how smartphone-based tools developed to assess children in their homes and the use of advanced data analytics can transform how, when, and where we assess young children's development and mental health. Our second objective is to share how our multidisciplinary team and agile development methodology enable us to build and launch a consumer-facing pediatric health app within an academic medical center.
Method(s): The WonderLab creates scalable mobile digital health tools to collect multimodal data in children's homes at the individual, family, and population levels. In December 2018, we released When to Wonder: Picky Eating, a national study with consent, enrollment, study activities, and feedback fully integrated in iOS and Android apps that parents download from the app stores. When to Wonder: Picky Eating focuses on the emotions and behaviors related to picky eating in children under the age of 7 years. Data sources include parent-report, video, audio, and an active task that children and parents play independently to quantify children's food preferences.
Result(s): We will present preliminary data from When to Wonder: Picky Eating to characterize normative and clinically significant emotions and behaviors related to picky eating. We will also share data on recruitment and engagement using social media, app performance, and "lessons learned" about digital pediatric health.
Conclusion(s): We create clinically and scientifically valid digital tools that parents and children want to use. We integrate clinical, scientific, engineering, design, data science, and bioethics expertise with collaborative user engagement and a "build, measure, learn" agile development culture. Our app-based study demonstrates how to build digital health tools that collect and analyze population-level and individual-level, multimodal data about children and families in the home. These new tools and approaches have the potential to transform our engagement with families and our delivery of care. EA, EC, MED
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BACKGROUND:Effective implementation of technologies into clinical workflow is hampered by lack of integration into daily activities. Normalisation process theory (NPT) can be used to describe the kinds of 'work' necessary to implement and embed complex new practices. We determined the suitability of NPT to assess the facilitators, barriers and 'work' of implementation of two clinical decision support (CDS) tools across diverse care settings. METHODS:We conducted baseline and 6-month follow-up quantitative surveys of clinic leadership at two academic institutions' primary care clinics randomised to the intervention arm of a larger study. The survey was adapted from the NPT toolkit, analysing four implementation domains: sense-making, participation, action, monitoring. Domains were summarised among completed responses (n=60) and examined by role, institution, and time. RESULTS:The median score for each NPT domain was the same across roles and institutions at baseline, and decreased at 6 months. At 6 months, clinic managers' participation domain (p=0.003), and all domains for medical directors (p<0.003) declined. At 6 months, the action domain decreased among Utah respondents (p=0.03), and all domains decreased among Wisconsin respondents (p≤0.008). CONCLUSIONS:This study employed NPT to longitudinally assess the implementation barriers of new CDS. The consistency of results across participant roles suggests similarities in the work each role took on during implementation. The decline in engagement over time suggests the need for more frequent contact to maintain momentum. Using NPT to evaluate this implementation provides insight into domains which can be addressed with participants to improve success of new electronic health record technologies. TRIAL REGISTRATION NUMBER/BACKGROUND:NCT02534987.

BACKGROUND:Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers. OBJECTIVE:This study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations. METHODS:We used a scoping process to survey existing efforts through Epic's Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms. RESULTS:Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock. CONCLUSIONS:The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.

Changing physician behaviors is difficult. Electronic health record (EHR) clinical decision support (CDS) offers an opportunity to promote guideline adherence. Behavioral economics (BE) has shown success as an approach to supporting evidence-based decision-making with little additional cognitive burden. We applied a user-centered approach to incorporate BE "nudges" into a CDS module in two "vanguard" sites utilizing: (1) semi-structured interviews with key informants (n = 8); (2) a design thinking workshop; and (3) semi-structured group interviews with clinicians. In the 133 day development phase at two clinics, the navigator section fired 299 times for 27 unique clinicians. The inbasket refill alert fired 124 times for 22 clinicians. Fifteen prescriptions for metformin were written by 11 clinicians. Our user-centered approach yielded a BE-driven CDS module with relatively high utilization by clinicians. Next steps include the addition of two modules and continued tracking of utilization, and assessment of clinical impact of the module.