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Health Expect
December 2024
Mississauga Health Ontario Health Team, Mississauga, Ontario, Canada.
Introduction: Despite widespread calls to involve patients, families and caregivers (PFCs) as partners at all levels of health system planning and design, there is unevenness in how engagement efforts are supported across these settings. The concept of 'engagement-capable environments' offers a way forward to uncover the key requirements for sustainable, high-quality engagement, but more work is needed to identify the specific competencies required to create these environments. We addressed this gap by developing a capability framework for Ontario Health Teams (OHTs), a newly established structure for planning, designing, organizing and delivering care in Ontario, Canada.
View Article and Find Full Text PDFEpilepsy Res
December 2024
Cornwall Partnership NHS Foundation Trust, UK; Peninsula School of Medicine, University of Plymouth, UK. Electronic address:
Background: Adherence to anti-seizure medication (ASM) by people diagnosed with epilepsy in sub-Saharan Africa remains low. The factors for low adherence are not well understood. To improve adherence, it is important to understand the perceptions and views of healthcare professionals delivering epilepsy care to this population.
View Article and Find Full Text PDFJ Pediatr Psychol
August 2024
Division of Adolescent and Young Adult Medicine, Children's Hospital Los Angeles, Los Angeles, United States.
Objective: Mobile health (mHealth) interventions may be an efficacious strategy for promoting health behaviors among pediatric populations, but their success at the implementation stage has proven challenging. The purpose of this article is to provide a blueprint for using human-centered design (HCD) methods to maximize the potential for implementation, by sharing the example of a youth-, family-, and clinician-engaged process of creating an mHealth intervention aimed at promoting healthcare transition readiness.
Method: Following HCD methods in partnership with three advisory councils, we conducted semistructured interviews with 13- to 15-year-old patients and their caregivers in two phases.
J Am Med Inform Assoc
October 2024
Sutter Health Data Science Team, Sutter Health, Sacramento, CA 95833, United States.
Objectives: Large Language Models (LLMs) have been proposed as a solution to address high volumes of Patient Medical Advice Requests (PMARs). This study addresses whether LLMs can generate high quality draft responses to PMARs that satisfies both patients and clinicians with prompt engineering.
Materials And Methods: We designed a novel human-involved iterative processes to train and validate prompts to LLM in creating appropriate responses to PMARs.
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