As the worldwide population ages, HCI researchers are designing technologies to better support older adults. We investigated how older adult crafters would customize technologies using electronics by building on their crafting skills. This supported them to explore customizing devices for themselves and advance the design of pervasive health technologies for older adults. We first conducted a survey of 42 older adult crafters to learn more about their crafting habits and gauge interest in technology and health tracking. We then conducted a participatory design workshop with 10 older adult crafters, focused on mutual learning to support them in prototyping how they would customize technology with maker electronics. They brainstormed customized devices around health, games, and safety, as well as aesthetically enhanced artifacts integrating electronics. We discuss how promoting older adult crafters to design and build customized pervasive health technologies impacts future research, and we provide guidelines on how to do so.

This paper focuses on addressing the complex healthcare needs of patients struggling with discordant chronic comorbidities (DCCs). Managing these patients within the current healthcare system often proves to be a challenging process, characterized by evolving treatment needs necessitating multiple medical appointments and coordination among different clinical specialists. This makes it difficult for both patients and healthcare providers to set and prioritize medications and understand potential drug interactions. The primary motivation of this research is the need to reduce medication conflict and optimize medication regimens for individuals with DCCs. To achieve this, we allowed patients to specify their health conditions and primary and major treatment concerns, for example, costs of medication, interactions with current drugs, and weight gain. Utilizing data gathered from MTurk and Qualtrics, we gained insights into healthcare providers’ strategies for making/customizing medication regimens. We constructed a dataset and subsequently deployed machine learning algorithms to predict optimal medication regimens for DCC patients with specific treatment concerns. Following the benchmarking different models, Random forest emerged as the top performer, achieving an accuracy of 0.93. This research contributes significantly to the enhancement of decision-making processes, empowers patients to take a more active role in their healthcare, and promotes more informed and productive discussions between patients and their care teams.

Recent advances in artificial intelligence (AI) have created opportunities to enhance medical decision-making for patients with discordant chronic conditions (DCCs), where a patient has multiple, often unrelated, chronic conditions with conflicting treatment plans. This paper explores the perspectives of healthcare providers (n = 10) and patients (n = 6) regarding AI tools for medication management. Participants were recruited through two healthcare centers, with interviews conducted via Zoom. The semi-structured interviews (60–90 min) explored their views on AI, including its potential role and limitations in medication decision making and management of DCCs. Data were analyzed using a mixed-methods approach, including semantic analysis and grounded theory, yielding an inter-rater reliability of 0.9. Three themes emerged: empathy in AI–patient interactions, support for AI-assisted administrative tasks, and challenges in using AI for complex chronic diseases. Our findings suggest that while AI can support decision-making, its effectiveness depends on complementing human judgment, particularly in empathetic communication. The paper also highlights the importance of clear AI-generated information and the need for future research on embedding empathy and ethical standards in AI systems.