Healthcare Innovation Bridging Research, Informatics & Design Lab Projects | NYU Langone Health

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HiBRID Lab Healthcare Innovation Bridging Research, Informatics & Design Lab Projects

Healthcare Innovation Bridging Research, Informatics & Design Lab Projects

The Healthcare Innovation Bridging Research, Informatics, and Design (HiBRID) Lab collaborates with researchers across NYU Langone on a variety of research and operational projects. We aim to improve healthcare delivery and patient care by supporting user-centered design in digital health applications and electronic health record (EHR) innovations.

Our unique interdisciplinary research group currently manages a portfolio of seven federally sponsored grants and multiple enterprise digital innovation initiatives. We represent core members of critical digital innovation teams at NYU Langone including Clinical Decision Support, FuturePractice, Patient Digital Experience, and Clinical Digital Experience.

Learn more about our current projects below.

Digital Diabetes Prevention Integration Tool (Noom-dDPP)

This study, Evaluation of an Automated Physician-Directed Messaging on Patient Engagement in the Digital Diabetes Prevention Program, aims to understand the effectiveness of a personalized SMS integration tool (PAMS) on patient engagement within a digital diabetes prevention program (dDPP) through measurement of weight, A1c, and Noom use. The project leverages a National Institutes of Health (NIH)–funded prototype integration tool with the goal of conducting a large-scale hybrid effectiveness and implementation study.

By Tracking Weight, A1c, and Noom Use, A Personalized SMS Integration Tool Enhances the Effectiveness of using a Third-Party Digital Diabetes Prevention Program
A personalized, automated messaging support system, PAMS delivers tailored, theoretically-grounded behavior change content to patients who are using a third-party dDPP application for diabetes prevention. PAMS also integrates dDPP activity data into the EHR for clinician review.

Effects of Nurse-Driven Integrated Clinical Prediction Tool on Antibiotic Prescribing (iCPR3)

This study, Decreasing Antibiotic Prescribing in Acute Respiratory Infections Through Nurse-Driven Clinical Decision Support, evaluates the effects of a novel integrated clinical decision prediction tool, iCPR3, on antibiotic prescription patterns of nurses for acute respiratory infections (ARIs). The intervention is an EHR-integrated risk calculator and order set to help guide appropriate, evidence-based antibiotic prescriptions for patients presenting with ARI symptoms.

Team Care to Address Medication Adherence Leveraging EHR Technology (TEAMLET)

This project, Addressing Antihypertensive Medication Adherence Through EHR-enabled Teamlets in Primary Care, involves pragmatic, type I hybrid comparative effectiveness–implementation study. TEAMLET aims to evaluate if an Epic-based intervention (pharmacy fill data, EHR-based clinical decision support, and health coaching by primary care providers) can improve medication adherence and blood pressure control in the primary care setting.

Remote Patient Monitoring Playbook

This project includes characterizing baseline remote patient monitoring (RPM) workflows and pain points across different specialties, designing and testing solutions to address these problems in tandem with the HiBRID Lab, NYU Langone’s Medical Center Information Technology (MCIT), and clinical stakeholders, and creating an internal “playbook” that outlines a comprehensive strategy for using RPM across NYU Langone clinics.

Lung Cancer Screening Clinical Decision Support Tool for Shared Decision-Making

This project aims to improve lung cancer screening at a large scale through clinical decision support (CDS) using shared decision-making (SDM) with the University of Utah. In order to achieve our goal, we will be utilizing a Decision Precision+ app using the “SMART on FHIR” interoperability standard for lung cancer screening.

Nudging Provider Adoption of Clinical Decision Support

This clinical trial aims to use the principles of behavioral economics (such as the “nudge”) in the development of a pulmonary embolism risk prediction clinical decision support tool to improve provider adoption and hence increase the adoption of clinical decision support tools which will improve quality of care and decrease morbidity, mortality, and healthcare waste.

Advancing the Development of Collaborative Research Networks to Enhance Aging Research Value

This research study is funded by the National Institute on Aging (NIA) and aims to determine factors contributing to the development of successful research collaboration networks to support increased interdisciplinary collaboration and diversity in aging research.

National Science Foundation Digital Worker

This research is funded by the National Science Foundation (NSF) and studies how to best bring inclusive tech into clinical settings, empower healthcare workers to take advantage of data-driven research, and improve health outcomes for patients. This study collaborates with NYU Tandon School of Engineering and NYU Stern School of Business.

National Science Foundation Future of Work

This research is funded by the NSF and studies cases of future expert work in the age of “black box,” data-intensive, and algorithmically augmented healthcare. This study collaborates with NYU Tandon School of Engineering.

Daily SMS ePROs to Improve Diabetes Control (i-Matter)

This project, i-Matter: Investigating an mHealth Texting Tool for Embedding Patient-Reported Data into Diabetes Management, evaluates the effect of a technology-based patient-reported outcome tool called i-Matter for care of patients with uncontrolled type 2 diabetes. i-Matter is an interactive journaling system that enables patients to rate, record, and view the progression of their patient-reported outcomes (PROs) along their treatment journey.

The iMatter Journal Report Comprises Bar Charts and Graphs Tracking Health Metrics to Help Patients Better Manager Type 2 Diabetes
The monthly i-Matter journal report includes key insights and evidence-based recommendations to help patients reflect on changes in their response overtime and help them better manage their type 2 diabetes.