Co-designing an interactive artificial intelligent system with post-stroke patients and caregivers to augment the lost abilities and improve their quality of life: a human-centric approach.

Objectives: The motor disability due to stroke compromises the autonomy of patients and caregivers. To support autonomy and other personal and social needs, trustworthy, multifunctional, adaptive, and interactive assistive devices represent optimal solutions. To fulfill this aim, an artificial intelligence system named MAIA would aim to interpret users' intentions and translate them into actions performed by assistive devices. Analyzing their perspectives is essential to develop the MAIA system operating in harmony with patients' and caregivers' needs as much as possible. Methods: Post-stroke patients and caregivers were interviewed to explore the impact of motor disability on their lives, previous experiences with assistive technologies, opinions, and attitudes about MAIA and their needs. Interview transcripts were analyzed using inductive thematic analysis. Results: Sixteen interviews were conducted with 12 post-stroke patients and four caregivers. Three themes emerged: (1) Needs to be satisfied, (2) MAIA technology acceptance, and (3) Perceived trustfulness. Overall, patients are seeking rehabilitative technology, contrary to caregivers needing assistive technology to help them daily. An easy-to-use and ergonomic technology is preferable. However, a few participants trust a system based on artificial intelligence. Conclusion: An interactive artificial intelligence technology could help post-stroke patients and their caregivers to restore motor autonomy. The insights from participants to develop the system depends on their motor ability and the role of patients or caregiver. Although technology grows exponentially, more efforts are needed to strengthen people's trust in advanced technology.

Examining the Real-Life Journey of Individuals and Families Affected by Single-Ventricle Congenital Heart Disease.

Background The lifetime journey of patients with single-ventricle congenital heart disease is characterized by long-term challenges that are incompletely understood and still unfolding. Health care redesign requires a thorough understanding of this journey to create and implement solutions that improve outcomes. This study maps the lifetime journey of individuals with single-ventricle congenital heart disease and their families, identifies the most meaningful outcomes to them, and defines significant challenges in the journey. Methods and Results This qualitative research study involved experience group sessions and 1:1 interviews of patients, parents, siblings, partners, and stakeholders. Journey maps were created. The most meaningful outcomes to patients and parents and significant gaps in care were identified across the life journey. A total of 142 participants from 79 families and 28 stakeholders were included. Lifelong and life-stage specific journey maps were created. The most meaningful outcomes to patients and parents were identified and categorized using a "capability (doing the things in life you want to), comfort (experience of physical/emotional pain/distress), and calm (experiencing health care with the least impact on daily life)" framework. Gaps in care were identified and classified into areas of ineffective communication, lack of seamless transitions, lack of comprehensive support, structural deficiencies, and insufficient education. Conclusions There are significant gaps in care during the lifelong journey of individuals with single-ventricle congenital heart disease and their families. A thorough understanding of this journey is a critical first step in developing initiatives to redesign care around their needs and priorities. This approach can be used for people with other forms of congenital heart disease and other chronic conditions. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04613934.

Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study.

BACKGROUND: Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, "off-the-shelf" devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. OBJECTIVE: To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. METHODS: The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. RESULTS: The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. CONCLUSIONS: The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.