Faculty Bibliography

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.

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: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.

INTRODUCTION/BACKGROUND:Emergency departments (ED) care for society's most vulnerable older adults who present with exacerbations of chronic disease at the end of life, yet the clinical paradigm focuses on treatment of acute pathologies. Palliative care interventions in the ED capture high-risk patients at a time of crisis and can dramatically improve patient-centred outcomes. This study aims to implement and evaluate Primary Palliative Care for Emergency Medicine (PRIM-ER) on ED disposition, healthcare utilisation and survival in older adults with serious illness. METHODS AND ANALYSIS/UNASSIGNED:This is the protocol for a pragmatic, cluster-randomised stepped wedge trial to test the effectiveness of PRIM-ER in 35 EDs across the USA. The intervention includes four core components: (1) evidence-based, multidisciplinary primary palliative care education; (2) simulation-based workshops; (3) clinical decision support; and (4) audit and feedback. The study is divided into two phases: a pilot phase, to ensure feasibility in two sites, and an implementation and evaluation phase, where we implement the intervention and test the effectiveness in 33 EDs over 2 years. Using Centers for Medicare and Medicaid Services (CMS) data, we will assess the primary outcomes in approximately 300 000 patients: ED disposition to an acute care setting, healthcare utilisation in the 6 months following the ED visit and survival following the index ED visit. Analysis will also determine the site, provider and patient-level characteristics that are associated with variation in impact of PRIM-ER. ETHICS AND DISSEMINATION/UNASSIGNED:Institutional Review Board approval was obtained at New York University School of Medicine to evaluate the CMS data. Oversight will also be provided by the National Institutes of Health, an Independent Monitoring Committee and a Clinical Informatics Advisory Board. Trial results will be submitted for publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER/BACKGROUND:NCT03424109; Pre-results.

Background: Clinical decision support (CDS) tools which incorporate clinical prediction rules (CPRs) have the potential to successfully deliver accurate information and guide decision-making at the point of care. Our previously validated integrated clinical prediction rule (iCPR) was designed to guide evidence-based treatment within an electronic health record for streptococcal pharyngitis and pneumonia based on chief complaints of sore throat, cough or upper respiratory infection. In initial testing at a single site, it resulted in high provider tool adoption (58%) and decreased antibiotic prescribing rates (35%) for acute respiratory infections. Our objective for this study was to assess the impact of this tool when adapted and implemented in diverse primary care settings.
Method(s): This was a randomized controlled trial including 33 primary care practices at two large academic health systems in Wisconsin and Utah. Between October 2015 and June 2018 providers in the intervention group were prompted to complete either Centor Score or Heckerling Rule for Pneumonia based onthe chief complaint of the patient encounter. EHR data on provider and patient demographics, tool use rates, and antibiotic order rates from 541 providers and 100,573 monitored patient encounters were collected for analysis. Risk ratios, CIs, and P values are calculated from a generalized estimating equation log-binomial model adjusting for clustering of orders or visits by provider and using robust standard error estimators.
Result(s): The tool was triggered 42,126 times among 214 intervention providers and was completed in 6.9% of eligible visits. The intervention and control groups prescribed antibiotics in 35% and 36% of visits respectively and were not significantly different. There were no differences in rates for rapid streptococcal test or chest X-ray orders between groups (Strep: relative risk, 1.0; P=.11; Pneumonia: relative risk, 1.8; P=.64).
Conclusion(s): In diverse primary care settings, the tool was not effective at reducing unnecessary antibiotic prescription and diagnostic testing. This outcome was possibly driven by low overall use of CDS tools highlighting the growing impact of " alert fatigue" and the need for new approaches to enhance provider engagement with CDS tools. New strategies for reducing the persistently high rates of inappropriate antibiotic prescribing for acute respiratory infections are needed. Novel approaches in future studies are necessary for reducing barriers to CDS tools in order to increase use and engagement

Statement of Problem Or Question (One Sentence): Underutilization of clinical prediction rules and poor uptake of provider-oriented clinical decision support (CDS) has contributed to overuse of antibiotics for sore throat. Objectives of Program/Intervention (No More Than Three Objectives): 1. Adapt CDS for registered nurses (RNs) to evaluate and treat patients with sore throat 2. Demonstrate the feasibility of RN visits using CDS to evaluate and treat patients with sore throat Description of Program/Intervention, Including Organizational Context (E.G. Inpatient Vs. Outpatient, Practice or Community Characteristics): We performed a 12-week pilot study to evaluate the feasibility of RN visits using an integrated clinical prediction rule (iCPR) tool to determine patient risk for strep throat and provide appropriate treatment at a family medicine clinic in a Midwest academic healthcare system. iCPR, originally developed for use by primary care physicians (PCPS), includes a risk calculator using Centor strep throat criteria and ordersets based on patient's risk for strep throat: education for low-risk, testing for intermediate-risk, and testing or antibiotics for high-risk. To adapt the process for RN visits, we developed triage protocols so appropriate patients received nurse visits, very low risk received education and more complex patients received provider visits. No major changes were made to the risk calculator or ordersets. Four RNs, with 2-24 years of experience, received a 10-minute online training session on sore throat evaluation followed by a 45-minute in-person training on physical examination and iCPR use. RNs triaged patients by phone and conducted RN visits using iCPR and following orderset recommendations. RNs could transition to a PCP visit if they were uncomfortable evaluating the patient. Measures of Success (DISCUSS QUALITATIVE AND/OR QUANTITATIVE METRICS WHICH WILL BE USED TO EVALUATE PROGRAM/INTERVENTION): Electronic health record data was used to determine the number of nurse visits, frequency of tool use and antibiotic and diagnostic test ordering. RNs completed a self-efficacy survey prior to training and 8-weeks after implementation. At 12 weeks, we interviewed RNs to understand barriers and facilitators to using the tool. Findings To Date (It Is Not Sufficient To State Findings Will Be Discussed): 162 triage calls for sore throat resulted in 77(48%) patients with RN-only visits, 45(28%) with provider visits, 38(23%) with no visit. Only 2 RN visits (< 3%) converted to provider visit due to patient complexity. RNs completed the risk calculator for 99% of visits and followed recommendations in all cases except for ordering antibiotics in 1 high-risk patient with a negative rapid strep. RN confidence in their ability to evaluate and treat a patient with sore throat was 85 (SD 5.8) (0 cannot do at all; 100 highly certain I can do) prior to training and 97.5 (SD 5.0) at 8-weeks. RNs felt the tool decreased provider visits and strep testing in patients. RN's also felt that the tool increased patient and RN satisfaction. Key Lessons For Dissemination (What Can Others Take Away For Implementation To Their Practice Or Community?): This pilot study demonstrates that RNs can use CDS to appropriately triage, evaluate and treat acute low-complexity sore throat patients. Implementation of an RN-driven iCPR tool shows promise to reduce inappropriate antibiotic prescribing and represents a potential model for expanding RN practice using CDS

Background: Older adults with diabetes continue to be overtreated despite current guidelines recommending less aggressive target A1c levels based on life expectancy. The suboptimal management of this vulnerable population could be due to physicians having conflicting beliefs regarding this guideline or simply lacking awareness, and changing these behaviors is challenging. Clinical decision support (CDS) within the electronic health record (EHR) has the potential to address this issue, but effectiveness is undermined by alert fatigue and poor workflow integration. Incorporating behavioral economics into CDS tools is an innovative approach to improve adherence to these guidelines while reducing physician burden, and offers the promise of improving care in this population.
Method(s): We applied a systematic, user-centered approach to incorporate behavioral economic " nudges" into a CDS module and performed user testing in six pilot primary care practices in a large academic medical center. To build the nudges, we conducted: (1) semi-structured interviews with key informants (n=8); (2) a two-hour design thinking workshop to derive and refine initial module ideas; and (3) semi-structured group interviews at each site with clinic leaders and clinicians to elicit feedback on the module components. Clinicians were observed using the module in practice; detailed field notes were collected and summarized by module idea and usability theme for rapid iteration and refinement. Frequency of firing and user action taken were assessed in the first month of implementation via EHR reporting to confirm that module components and reporting were working as expected, and to assess utilization.
Result(s): Insights from key stakeholder and clinician group interviews identified the refill protocol, inbasket lab result, and medication preference list as candidate EHR CDS targets for the module. A new EHR navigator section notification and peer comparison message, derived from the design workshop, were also prototyped and produced. User feedback from site visits confirmed compatibility with clinical workflows, and contributed to refinement of design and content. The initial prototypes were first piloted at two sites, refined, and then activated at an additional four additional sites. Preliminary Results for the six clinics indicate that over approximately 31 weeks: 1) the navigator alert fired 1047 times for 53 unique clinicians, and 2) the refill protocol alert fired 421 times for 53 unique clinicians. Reports for the other " nudges" are in development.
Conclusion(s): Integrating behavioral economic nudges into the EHR is a promising approach to enhancing guideline awareness and adherence for older adults with diabetes. This novel pilot will demonstrate the initial feasibility and preliminary efficacy of this strategy and determine if a full-scale effectiveness trial is warranted

BACKGROUND:Clinical decision support (CDS) has been shown to improve compliance with evidence-based care, but its impact is often diminished because of issues such as poor usability, insufficient integration into workflow, and alert fatigue. Noninterruptive CDS may be less subject to alert fatigue, but there has been little assessment of its usability. OBJECTIVE:This study aimed to study the usability of interruptive and noninterruptive versions of a CDS. METHODS:We conducted a usability study of a CDS tool that recommended prescribing an angiotensin-converting enzyme inhibitor for inpatients with heart failure. We developed 2 versions of the CDS: an interruptive alert triggered at order entry and a noninterruptive alert listed in the sidebar of the electronic health record screen. Inpatient providers were recruited and randomly assigned to use the interruptive alert followed by the noninterruptive alert or vice versa in a laboratory setting. We asked providers to "think aloud" while using the CDS and then conducted a brief semistructured interview about usability. We used a constant comparative analysis informed by the CDS Five Rights framework to analyze usability testing. RESULTS:A total of 12 providers participated in usability testing. Providers noted that the interruptive alert was readily noticed but generally impeded workflow. The noninterruptive alert was felt to be less annoying but had lower visibility, which might reduce engagement. Provider role seemed to influence preferences; for instance, some providers who had more global responsibility for patients seemed to prefer the noninterruptive alert, whereas more task-oriented providers generally preferred the interruptive alert. CONCLUSIONS:Providers expressed trade-offs between impeding workflow and improving visibility with interruptive and noninterruptive versions of a CDS. In addition, 2 potential approaches to effective CDS may include targeting alerts by provider role or supplementing a noninterruptive alert with an occasional, well-timed interruptive alert.