The Value of Technology to Support Dyadic Caregiving for Individuals Living With Heart Failure: Qualitative Descriptive Study.

BACKGROUND: The demand for health services to meet the chronic health needs of the aging population is significant and remains unmet because of the limited supply of clinical resources. Specifically, in managing heart failure (HF), digital health sought to address this gap during the COVID-19 pandemic but highlighted an access issue for those who could not use technology-mediated health care services without the support of their informal caregivers (ICs). The complexity of managing HF symptoms and recurrent exacerbations requires many patients to comanage their illness with their ICs in a care dyad, working together to optimize patient outcomes and health-related quality of life. However, most HF programs have missed the opportunity to consider the dyadic perspective despite interdependencies on HF outcomes. OBJECTIVE: This study aims to characterize the value of technology in supporting caregiving for individuals living with HF. METHODS: Motivated by an observed unique pattern of engagement in patients enrolled in our Medly HF management program at the Peter Munk Cardiac Centre in Toronto, Canada, we conducted 20 semistructured interviews with a convenience sample of ICs. All interviews were analyzed using the iterative refinement of a codeveloped codebook. The team maintained reflexivity journals to reflect the impact of their positionality on their coding. Themes were first derived deductively using HF typologies (patient-oriented dyads, caregiver-oriented dyads, and collaboratively oriented dyads) and then inductively refined and recategorized based on concepts from the van Houtven et al framework. RESULTS: We believe that there is a need to formally and intentionally expand HF technologies to include dyadic needs and goals. We suggest defining 3 opportunities in which value can be added to technological design. First, identify how technology may be leveraged to increase psychological bandwidth by reducing uncertainty and providing peace of mind. We found that actionable feedback was highly desired by both partners. Second, develop technology that can serve as a member of the dyad's support system. In our experience, automated prompts for patients to take measurements can mimic the support typically provided by ICs and ease their workload. Third, consider how technology can mitigate the dyad's clinical knowledge requirements and learning curve. Our approach includes real-time actionable feedback paired with a human-in-the-loop, nurse-led model of care. CONCLUSIONS: Our findings identified a need to focus on improving the dyadic experience as a whole by building IC functionality into digital health self-management interventions. Through a shared model of care that supports the role of the patient in their own HF management, includes ICs to expand and enhance the patient's capacity to care, and acknowledges the need of ICs to care for themselves, we anticipate improved outcomes for both partners.

A Road Map of Prompt Engineering for ChatGPT in Healthcare: A Perspective Study.

Generative AI models, such as ChatGPT, have significantly impacted healthcare through the strategic use of prompts to enhance precision, relevance, and ethical standards. This perspective explores the application of prompt engineering to tailor outputs specifically for healthcare stakeholders: patients, providers, policymakers, and researchers. A nine-stage process for prompt engineering in healthcare is proposed, encompassing identifying applications, understanding stakeholder needs, designing tailored prompts, iterative testing and refinement, ethical considerations, collaborative feedback, documentation, training, and continuous updates. A literature review focused on "Generative AI" or "ChatGPT," prompts, and healthcare informed this study, identifying key prompts through qualitative analysis and expert input. This systematic approach ensures that AI-generated prompts align with stakeholder requirements, offering valuable insights into symptoms, treatments, and prevention, thereby supporting informed decision-making among patients.

rTMS for post-covid-19 condition: A sham-controlled case series involving iTBS-300 and iTBS-600.

Post-Covid-19 Condition (PCC) is a syndrome comprised of symptoms persisting 3 months or more beyond SARS-CoV-2 primary infection. It is typically characterized by fatigue, cognitive problems and psychiatric symptoms, as well as cardiac symptoms that contribute to exercise intolerance in many. Despite the high prevalence of PCC among those with a prior SARS-CoV-2 infection, there is currently no widely accepted rehabilitation strategy, and many conventional modalities are movement-based. Non-invasive brain stimulation methods such as repetitive transcranial magnetic stimulation (rTMS) may have some potential to alleviate the cognitive and affective symptoms of PCC without reliance on exercise. The purpose of the present study was to explore the feasibility and tolerability of using rTMS to treat symptoms of "brain fog" and affective disturbance among those living with PCC, using a case series design. We enrolled four individuals with PCC following a confirmed SARS-CoV-2 infection, at least 3 months after the resolution of the primary infection. Participants were randomized to 4 sessions of active and 2 sessions of sham intermittent theta-burst stimulation (iTBS); two intensities of iTBS were evaluated: iTBS-300 and iTBS-600. No adverse events occurred in active or sham stimulation; 2 participants reported tingling sensation on the scalp but no other tolerability issues. Trends in symptoms suggested improvements in cognitive interference, quality of life, and anxiety in the majority of participants. In summary, in this case series iTBS was well tolerated among 4 individuals with PCC; active stimulation was associated with positive trends in some primary symptom clusters as compared with sham stimulation. Future studies should examine the effects of iTBS on PCC symptoms in the context of experimental studies and randomized controlled trials.

Design of a Dyadic Digital Health Module for Chronic Disease Shared Care: Development Study.

BACKGROUND: The COVID-19 pandemic forced the spread of digital health tools to address limited clinical resources for chronic health management. It also illuminated a population of older patients requiring an informal caregiver (IC) to access this care due to accessibility, technological literacy, or English proficiency concerns. For patients with heart failure (HF), this rapid transition exacerbated the demand on ICs and pushed Canadians toward a dyadic care model where patients and ICs comanage care. Our previous work identified an opportunity to improve this dyadic HF experience through a shared model of dyadic digital health. We call this alternative model of care "Caretown for Medly," which empowers ICs to concurrently expand patients' self-care abilities while acknowledging ICs' eagerness to provide greater support. OBJECTIVE: We present the systematic design and development of the Caretown for Medly dyadic management module. While HF is the outlined use case, we outline our design methodology and report on 6 core disease-invariant features applied to dyadic shared care for HF management. This work lays the foundation for future usability assessments of Caretown for Medly. METHODS: We conducted a qualitative, human-centered design study based on 25 semistructured interviews with self-identified ICs of loved ones living with HF. Interviews underwent thematic content analysis by 2 coders independently for themes derived deductively (eg, based on the interview guide) and inductively refined. To build the Caretown for Medly model, we (1) leveraged the Knowledge to Action (KTA) framework to translate knowledge into action and (2) borrowed Google Sprint's ability to quickly "solve big problems and test new ideas," which has been effective in the medical and digital health spaces. Specifically, we blended these 2 concepts into a new framework called the "KTA Sprint." RESULTS: We identified 6 core disease-invariant features to support ICs in care dyads to provide more effective care while capitalizing on dyadic care's synergistic benefits. Features were designed for customizability to suit the patient's condition, informed by stakeholder analysis, corroborated with literature, and vetted through user needs assessments. These features include (1) live reports to enhance data sharing and facilitate appropriate IC support, (2) care cards to enhance guidance on the caregiving role, (3) direct messaging to dissolve the disconnect across the circle of care, (4) medication wallet to improve guidance on managing complex medication regimens, (5) medical events timeline to improve and consolidate management and organization, and (6) caregiver resources to provide disease-specific education and support their self-care. CONCLUSIONS: These disease-invariant features were designed to address ICs' needs in supporting their care partner. We anticipate that the implementation of these features will empower a shared model of care for chronic disease management through digital health and will improve outcomes for care dyads.