How to create value with unobtrusive monitoring technology in home-based dementia care: a multimethod study among key stakeholders

BACKGROUND: There is a growing interest to support extended independent living of people with dementia (PwD) via unobtrusive monitoring (UM) technologies which allow caregivers to remotely monitor lifestyle, health, and safety of PwD. However, these solutions will only be viable if developers obtain a clear picture of how to create value for all relevant stakeholders involved and achieve successful implementation. The aim of this study was therefore to explore the value proposition of UM technology in home-based dementia care and preconditions for successful implementation from a multi-stakeholder perspective. METHODS: We conducted an expert-informed survey among potential stakeholders (n = 25) to identify key stakeholders for UM technology in home-based dementia care. Subsequently, focus groups and semi-structured interviews were conducted among 5 key stakeholder groups (n = 24) including informal caregivers (n = 5), home care professionals (n = 5), PwD (n = 4), directors and managers within home care (n = 4), and policy advisors within the aged care and health insurance sector (n = 6). The sessions addressed the value proposition- and business model canvas and were analyzed using thematic analysis. RESULTS: Stakeholders agreed that UM technology should provide gains such as objective surveillance, timely interventions, and prevention of unnecessary control visits, whereas pains mainly included information overload, unplannable care due to real-time monitoring, and less human interaction. The overall design-oriented need referred to clear situation classifications including urgent care (fall- and wandering detection), non-urgent care (deviations in eating, drinking, sleeping), and future care (risk predictions). Most important preconditions for successful implementation of UM technology included inter-organizational collaboration, a shared vision on re-shaping existing care processes, integrated care ICT infrastructures, clear eligibility criteria for end-users, and flexible care reimbursement systems. CONCLUSIONS: Our findings can guide the value-driven development and implementation of UM technology for home-based dementia care. Stakeholder values were mostly aligned, although stakeholders all had their own perspective on what UM technology should accomplish. Besides, our study highlights the complexity of implementing novel UM technology in home-based dementia care. To achieve successful implementation, organizational and financial preconditions, as well as digital data exchange between home care organizations, will be important. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-03550-1

Low-Fidelity Prototype of a Sensor-Dependent Interaction Platform:Formative Evaluation With Informal Caregivers of Older Adults With Cognitive Impairment

Background: Unobtrusive sensing technologies developed for monitoring deviant behaviors in older adult care require integration with an interaction platform to facilitate the flow of information between older adults and their caregivers. However, the continuous monitoring capabilities generate a considerable amount of data that must be interpreted, filtered, and personalized before being communicated to the informal caregivers based on their specific care needs and requirements. Objective: For the effective implementation of unobtrusive sensing solutions (USSs) in the care of older adults with cognitive impairment, we aimed to explore the expectations and preconditions regarding the implementation of USSs from the perspective of informal caregivers. Subsequently, we designed and evaluated a low-fidelity prototype of an interaction platform for its conceptual workflow and usability, incorporating persuasive system design features based on the needs and requirements of informal caregivers. Methods: Overall, 6 informal caregivers of older adults with cognitive impairment living alone participated in this qualitative interview study. We explored the expectation and preconditions regarding implementation through open-ended questions and conducted a formative evaluation (usability study with a think-aloud approach) to evaluate the conceptual workflow and used persuasive system design features in the interaction platform. Overall, a combination of inductive and thematic analyses was used to analyze the interviews. Results: The results of this study present both positive and negative outcome expectations regarding the implementation of USSs, highlighting benefits such as objective decision-making and peace of mind and concerns about information overload and the potential substitution of human contact. Strategic information communication agreements between informal and formal caregivers were deemed crucial for the successful implementation of USSs in care. Overall, informal caregivers had a positive experience with the low-fidelity prototype of the interaction platform, particularly valuing the personalization feature. Conclusions: In conclusion, to achieve successful implementation, a holistic design approach is necessary, and equal consideration should be given to the personalization-privacy paradox to balance users' needs and privacy.</p

Big Data for personalized healthcare

Big Data, often defined according to the 5V model (volume, velocity, variety, veracity and value), is seen as the key towards personalized healthcare. However, it also confronts us with new technological and ethical challenges that require more sophisticated data management tools and data analysis techniques. This vision paper aims to better understand the technological and ethical challenges we face when using and managing Big Data in healthcare as well as the way in which it impacts our way of working, our health, and our wellbeing. A mixed-methods approach (including a focus group, interviews, and an analysis of social media) was used to gain a broader picture about the pros and cons of using Big Data for personalized healthcare from three different perspectives: Big Data experts, healthcare workers, and the online public. All groups acknowledge the positive aspects of applying Big Data in healthcare, touching upon a wide array of issues, both scientifically and socially. By sharing health data, value can be created that goes beyond the individual patient. The Big Data revolution in healthcare is seen as a promising and innovative development. Yet potential facilitators and barriers need to be faced first to reach its full potential. Concerns were raised about privacy, trust, reliability, safety, purpose limitation, liability, profiling, data ownership, and loss of autonomy. Also, the importance of adding the people-centered view to the rather data-centered 5V model is stressed, in order to get a grip on the opportunities for using Big Data in personalized healthcare. People should be aware that the development of Big Data advancements is not self-evident