Social Design of Community Service Models with AIoT to Support Aging and Elders Well-Being -68

Along with the increase in average life expectancy, the world’s elderly population is expected to grow to 2.1 billion by 2050. Ageing marks a sensitive and vulnerable period of life, bringing loss of roles and functions and increased dependence on others, often reflected in a decline in quality of life. As everyone experiences ageing, the need to achieve a satisfactory old age for all in the future means that more research and a better systematic understanding of ageing and elder well-being are needed as changing demographics put growing pressure on public health and finance, and the provision of long-term care becomes increasingly inadequate. In this study, we have systematically scoped three streams of literature, design, social studies and digital technology based on Arksey and O’Malley’s (2005) methodological framework. With this RSD presentation, we will report on our ongoing work, scoping our research on three core elements: Aging and elder well-being, Community services and AIoT (Artificial intelligence and Internet of Things). Our preliminary review revealed a cluster of ethnographic studies on ‘ageing in place’ in which community services appeared to be of interest. Several survey studies confirm that most elders prefer to receive care from their families rather than in institutions. In a cluster with a systemic lens, community services have been studied to become an increasingly important model of long-term care a few have demonstrated that community services are more effective in supporting elders’ interests and care preferences. Within the digital technology stream, an emerging cluster of studies proposes Artificial intelligence and the Internet of Things (AIoT) as potential solutions to the challenges associated with an ageing society. AIoT integrated into elderly care expands the range of services and supports social well-being. Experimental studies with prototyped technologies are studied in relation to outcomes of improving the self-care experience of elders at home and how AIoT facilitates the development and sharing of their unique coping strategies, thereby maintaining their vitality and independence. However, the volume of the literature shows that only a few studies have included AIoT as part of community service. Overall, our systemic review work in progress unpacks the relevant literature into different clusters and categories, including theoretical lenses, research methods, findings and outcomes. The initial charting of the studies indicates that despite the accumulation of previous research, the current body of knowledge on the interplay of ageing and elder well-being, community services, and AIoT is underdeveloped, with unresolved issues at multiple levels of the community care model, including policy, organisation, services and individuals

Designing foresights by communities: a new groundbreaker role for strategic design

In order to humanize forecasting, communities have been proposed to activate and enlarge a collective ability of foresight. To better understand how communities relate to collective foresight abilities, this article untangles its critical modes, roles and social media involved. Based on a fine-grained analysis of 10 community practices, we uncovered the abilities of capturing, conceiving and designing foresights enacted in the distinct modes of creative, user and strategic communities. Discoveries included the novel abilities of conceiving foresights, a new groundbreaker role for strategic designers and specific activities of social media listening with regard to future interests. Grounded on the prime findings, we propose a framework with propositions that shape further theory development on community abilities of designing foresights. Further research directions are outlined

Care Model Design for E-Health: Integration of Point-of-Care Testing at Dutch General Practices

Point-of-care testing (POCT)—laboratory tests performed with new mobile devices and online technologies outside of the central laboratory—is rapidly outpacing the traditional laboratory test market, growing at a rate of 12 to 15% each year. POCT impacts the diagnostic process of care providers by yielding high efficiency benefits in terms of turnaround time and related quality improvements in the reduction of errors. However, the implementation of this disruptive eHealth technology requires the integration and transformation of diagnostic services across the boundaries of healthcare organizations. Research has revealed both advantages and barriers of POCT implementations, yet to date, there is no business model for the integration of POCT within general practice. The aim of this article is to contribute with a design for a care model that enables the integration of POCT in primary healthcare. In this research, we used a design modelling toolkit for data collection at five general practices. Through an iterative design process, we modelled the actors and value transactions, and designed an optimized care model for the dynamic integration of POCTs into the GP’s network of care delivery. The care model design will have a direct bearing on improving the integration of POCT through the connectivity and norm guidelines between the general practice, the POC technology, and the diagnostic centre

Correction: A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth

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Correction: A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth (Preprint)

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A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth

Background In the context of the COVID-19 outbreak, 80% of the persons who are infected have mild symptoms and are required to self-recover at home. They have a strong demand for remote health care that, despite the great potential of artificial intelligence (AI), is not met by the current services of eHealth. Understanding the real needs of these persons is lacking. Objective The aim of this paper is to contribute a fine-grained understanding of the home isolation experience of persons with mild COVID-19 symptoms to enhance AI in eHealth services. Methods A design research method with a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from the top-viewed personal video stories on YouTube and their comment threads. For the analysis, this data was transcribed, coded, and mapped into the patient journey map. Results The key findings on the home isolation experience of persons with mild COVID-19 symptoms concerned (1) an awareness period before testing positive, (2) less typical and more personal symptoms, (3) a negative mood experience curve, (5) inadequate home health care service support for patients, and (6) benefits and drawbacks of social media support. Conclusions The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves health and information technology professionals in more effectively applying AI technology into eHealth services, for which three main service concepts are proposed: (1) trustworthy public health information to relieve stress, (2) personal COVID-19 health monitoring, and (3) community support. </jats:sec