A5006691352- Mobile Health and mHealth Applications
- Digital Mental Health Interventions
- Atrial Fibrillation Management and Outcomes
- Artificial Intelligence in Healthcare and Education
- Telemedicine and Telehealth Implementation
- Health Literacy and Information Accessibility
- Patient-Provider Communication in Healthcare
- Cardiac Arrhythmias and Treatments
- Maternal Mental Health During Pregnancy and Postpartum
- Health Systems, Economic Evaluations, Quality of Life
- Heart Failure Treatment and Management
- Electronic Health Records Systems
- ECG Monitoring and Analysis
- Medication Adherence and Compliance
- Technology Use by Older Adults
- Cardiac pacing and defibrillation studies
- Chronic Disease Management Strategies
- Primary Care and Health Outcomes
- COVID-19 and Mental Health
- Geriatric Care and Nursing Homes
- Digital Imaging in Medicine
- Machine Learning in Healthcare
- Blood Pressure and Hypertension Studies
- Mental Health Research Topics
- Ethics in Clinical Research
Columbia University
2016-2025
Columbia University Irving Medical Center
2018-2025
Duke University Health System
2025
University of Minnesota
2025
Weill Cornell Medicine
2019-2024
Cornell University
2018-2024
Faculty of 1000 (United States)
2024
University School
2024
Duran i Reynals Hospital
2022
Institute of Population and Public Health
2022
Background: Heart failure is the most common cause of hospital readmissions among Medicare beneficiaries and these hospitalizations are often driven by exacerbations in heart symptoms. Patient collaboration with health care providers decision making a core component increasing symptom monitoring decreasing use. Mobile phone apps offer potentially cost-effective solution for self-care management at point need.
Abstract: There are limited data on racial and ethnic disparities related to quality of life (QoL) health literacy in adults with multiple cardiac conditions. This article evaluates the relationship between QoL among patients conditions a multiethnic community New York City.
Abstract Objective We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way implement the PPD prediction within EHR system for clinical decision support. describe pre-implementation evaluation process that considered performance, fairness, appropriateness. Materials Methods used an academic medical center (AMC) research network database 2014 2020 evaluate predictive performance...
Abstract Objectives Patients increasingly use patient-reported outcomes (PROs) to self-monitor their health status. Visualizing PROs longitudinally (over time) could help patients interpret and contextualize PROs. The study sought assess hospitalized patients' objective comprehension (primary outcome) of text-only, non-graph, graph visualizations that display longitudinal Materials Methods We conducted a clinical research in 40 comparing 4 visualization conditions: (1) (2) text plus visual...
Abstract Objective Evaluate the impact of community tele-paramedicine (CTP) on patient experience and satisfaction relative to community-level indicators health disparity. Materials Methods This mixed-methods study evaluates patient-reported with CTP, a facilitated telehealth program combining in-home paramedic visits video by emergency physicians. Anonymous post-CTP visit survey responses themes derived from directed content analysis in-depth interviews participants randomized clinical...
An understanding of mental health symptoms during the coronavirus disease 2019 (COVID-19) pandemic is critical to ensure that policies adequately address needs people in United States. The objective this study was examine among US adults an early stage COVID-19 pandemic.
Objectives Early hospital readmissions or deaths are key healthcare quality measures in pay-for-performance programs. Predictive models could identify patients at higher risk of readmission death and target interventions. However, existing usually do not incorporate social determinants health (SDH) information, although this information is great importance to address disparities related factors. The objective study examine the impact on predictive for potentially avoidable 30-day...
Patient-generated health data (PGHD) collected digitally with mobile (mHealth) technology has garnered recent excitement for its potential to improve precision management of chronic conditions such as atrial fibrillation (AF), a common cardiac arrhythmia. However, sustained engagement is major barrier collection PGHD. Little known about barriers or strategies intervene upon through application design.
Patient-Reported Outcomes Measurement Information System (PROMIS) measures can monitor patients with chronic illnesses outside of healthcare settings. Unfortunately, few applications that collect electronic PROMIS are designed using inclusive design principles ensure wide accessibility and usability, thus limiting use by older adults illnesses. Our aim was to establish the feasibility an inclusively mobile application tailored report examining (1) scores collected application, (2)...
Abstract This study aimed to evaluate women’s attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians, developers, systems, the...
Abstract The application of predictive and generative artificial intelligence to health healthcare is rapidly increasing. Several studies have assessed the attitudes professionals but far fewer explored perspectives patients or general public. Studies investigating patient focused on somatic issues including radiology, perinatal health, applications. Patient feedback has been elicited in development specific mental solutions, towards AI for under-explored. To address this gap, we surveyed a...
Artificial intelligence and other digital health technologies may optimize nurses' work. Therefore, we aimed to examine the roles of nurses in facilitating adoption identify opportunities for these reduce burnout. We conducted a cross-sectional survey study focused on use artificial technology with nursing informaticists. Data collection was guided by implementation science framework, Non-Adoption, Abandonment, Scale-up, Spread, Sustainability. Participants were recruited electronically...
I was selected for the Alliance Nursing Informatics (ANI) Emerging Leaders Program and recently completed my 2-year term (2022-2024). A core component of program is completing a leadership project. When began term, digital health being adopted more readily than ever before, due in large part to COVID-19 pandemic necessitating remote care models. Drawing on experiences as cardiac nurse research consumer informatics, set out conduct mixed-methods study understand nurses' roles this...
Heart failure with preserved ejection fraction (HFpEF) disproportionately affects older adults. This study aimed to elucidate the prevalence and prognostic implications of geriatric vulnerabilities across multiple health domains in HFpEF. We examined consecutive patients HFpEF enrolled from Weill Cornell Medicine (WCM) OPTIMISE Programs. The primary exposure was following: multimorbidity, polypharmacy, cognitive impairment, depressive symptoms, frailty, limited mobility. outcome a 1-year...
Background Atrial fibrillation (AF) is associated with high recurrence rates and poor health-related quality of life (HRQOL) but few effective interventions to improve HRQOL exist. Objective The aim this study was examine the impact “iPhone Helping Evaluate Fibrillation Rhythm through Technology” (iHEART) intervention on in patients AF. Methods We randomized English- Spanish-speaking adult AF receive either iHEART or usual care for 6 months. used smartphone-based electrocardiogram monitoring...