Suzanne Bakken

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Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery. This study aims to computationally describe symptom documentation from electronic nursing notes and compare symptom clusters among patients diagnosed with four chronic conditions-chronic obstructive pulmonary disease (COPD), heart failure, type 2 diabetes mellitus, and cancer. Nursing notes (N = 504,395; 133,977 patients) were obtained for the 2016 calendar year from a single medical center...

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OBJECTIVE: The purpose of this study is to describe online health information seeking among a sample of transgender and gender diverse (TGD) people compared with cisgender sexual minority people to explore associations with human papillomavirus (HPV) vaccination, and whether general health literacy and eHealth literacy moderate this relationship. MATERIALS AND METHODS: We performed a cross-sectional online survey of TGD and cisgender sexual minority participants from The PRIDE Study, a longitudinal, U...

OBJECTIVE: This study aimed to identify sleep disturbance subtypes ("phenotypes") among Latinx adults based on objective sleep data using a flexible unsupervised machine learning technique. METHODS: This study was an analysis of sleep data from three cross-sectional studies of the Precision in Symptom Self-Management Center at Columbia University. All studies focused on sleep health in Latinx adults at increased risk for sleep disturbance. Data on total sleep time (TST), time in bed (TIB), wake after sleep onset (WASO), sleep efficiency (SE), number of awakenings (NOA) and the mean length of nightly awakenings were collected using wrist-mounted accelerometers...

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Objectives: Guided by the concept of digital phenotypes, the objective of this study was to identify engagement phenotypes among individuals with atrial fibrillation (AF) using mobile health (mHealth) technology for 6 months. Materials and Methods: We conducted a secondary analysis of mHealth data, surveys, and clinical records collected by participants using mHealth in a clinical trial. Patterns of participants' weekly use over 6 months were analyzed to identify engagement phenotypes via latent growth mixture model (LGMM)...

We designed an infographic intervention to help clinicians provide health information to persons living with HIV. In this study, we assessed the extent to which our intervention may improve objectively and subjectively measured health outcomes (CD4 count, viral load, and engagement with clinician among others) when integrated into routine visits in the Dominican Republic. In this pretest-posttest study, we followed participants for 9 months at 3-month intervals. Physicians administered the intervention during participants' first 3 visits...

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AIMS: Digital health can transform the management of atrial fibrillation (AF) and enable patients to take a central role in detecting symptoms and self-managing AF. There is a gap in understanding factors that support sustained use of digital health tools for patients with AF. This study identified predictors of Alivecor® KardiaMobile ECG monitor usage among patients with AF enrolled in the iPhone®Helping Evaluate Atrial fibrillation Rhythm through Technology (iHEART) randomized controlled trial...

AIM: To develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI). METHODS: We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health...

In chronic conditions, patients and providers need support in understanding and managing illness over time. Focusing on endometriosis, an enigmatic chronic condition, we conducted interviews with specialists and focus groups with patients to elicit their work in care specifically pertaining to dealing with an enigmatic disease, both independently and in partnership, and how technology could support these efforts. We found that the work to care for the illness, including reflecting on the illness experience and planning for care, is significantly compounded by the complex nature of the disease: enigmatic condition means uncertainty and frustration in care and management; the multi-factorial and systemic features of endometriosis without any guidance to interpret them overwhelm patients and providers; the different temporal resolutions of this chronic condition confuse both patients and provides; and patients and providers negotiate medical knowledge and expertise in an attempt to align their perspectives...