Searcharxiv⌕ Search

arXiv · 2610.23259

When Ubiquitous Health Data Stops Helping: Patients' Experience of Breakdowns in the Data-Interpretation-Action Loop at the End of Life

Abstract

Ubiquitous computing systems in healthcare often assume that greater data visibility improves awareness, interpretation, and action. In end-of-life care, however, this assumption warrants scrutiny. Through semi-structured interviews with 25 terminal-stage patients as primary informants and 8 clinicians as supplementary informants, we examine patients' descriptions of how they encountered, interpreted, acted on, and disengaged from state signals, trajectories, algorithmic outputs, and behavioral audits. Our analysis identifies three recurrent patterns in these descriptions. First, participants described repeated and unresolved sensemaking when visible data lacked explanatory pathways. Second, they described outputs they could understand but whose suggested actions did not fit their changing bodily capacities, care goals, or decision-making roles. Third, they described difficulty disengaging when data remained embedded across care environments. Rather than rejecting data altogether, patients called for less data exposure and a reorientation toward existential support. Based on this analysis, we situate data exposure as a central interaction problem in end-of-life ubiquitous computing and derive design implications for interpretive stability, care-context alignment, and existential support.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yibo Meng, Xinru Wang, Zhuoyan Li, Chengxi Zang, Zhuoran Lu, Zhicong Lu. 2026-10-16. When Ubiquitous Health Data Stops Helping: Patients' Experience of Breakdowns in the Data-Interpretation-Action Loop at the End of Life. https://arxiv.org/abs/2610.23259

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

How Children Design and Reason about Trustworthy AI Chatbots

Children increasingly interact with AI chatbots, making trust calibration essential to AI literacy. Prior research has examined children's trust in AI mainly as users evaluating systems built by others, rather than as designers of their own chatbots. We developed a chatbot-building environment with adjustable trust-relevant traits (e.g., confidence, transparency, formality, assertiveness), rules, and persona. We conducted mixed-methods study with 115 learners (ages 8-18) who made 119 chatbots. We examined how children configured their chatbots, reasoned about trustworthiness, and how closely chatbot behavior aligned with their designs. Younger students (age 10-13) set significantly higher confidence than older students (age 14-18), and some deliberately built chatbots that gave wrong answers on purpose, yet still called them trustworthy, arguing that a chatbot does what it was built to do. Younger students equated trust with purpose-fulfillment, while older students linked it to transparent, calibrated design. Students also calibrated academic chatbots to be more transparent and formal than hobby chatbots. We identify seven design dimensions describing what children believe makes a chatbot trustworthy, and discuss implications for AI literacy tools.

cs.HC↗

Designing Human-AI Collaborative Science: The 9A Framework

Artificial intelligence (AI) for Science is often described as a progression from human-directed assistance toward increasingly autonomous AI scientists. Human-AI collaboration offers a further mode in which human and AI contributions substantively shape scientific reasoning, judgments, and the course of inquiry. Existing research addresses work allocation, human-AI coordination, and system evaluation, but how these aspects interact to shape collaborative scientific systems remains insufficiently articulated. We develop the 9A Framework for Human-AI Collaborative Science (HACS) to connect these considerations across three layers. The organization layer specifies what scientific activities are required (Activity), who performs them (Actor), and who holds authority over consequential decisions (Authority). The coordination layer addresses how participants access relevant research information (Awareness), how scientific work advances through iterative proposal, execution, examination, revision, and resolution (Accountability), and when collaborative arrangements should be adjusted (Adaptability). The evaluation layer establishes what constitutes effective system performance (Achievement), how comparisons can be designed to identify the effects of organizational and coordination choices (Attribution), and what evidence supports the resulting conclusions (Assurance). Together, these layers frame HACS as a system-design problem, providing a basis for designing collaborative scientific workflows and for empirically comparing their effectiveness.

cs.HC↗

PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas

Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions. Completing the benchmark instruction does not guarantee serving the person who asked. We introduce PersonaCUABench, which holds 74 tasks across 11 domains fixed and varies only who asks. A persona agent, playing one of 44 personas in four groups, rewrites each instruction in that user's own words and answers the CUA's questions, while the CUA sees only the dialogue, never the profile or the original instruction. Across nine backbones (3,330 runs), mean task completion falls by 36% relative to the original requests. The losses have two sources: in 59.9% of cells that lose points, the agent engages with what the user asked for and still gets it wrong, and 11.2% are stops the user requested that the checker scores as failures. Even when a persona request is fully completed, the user gives the top satisfaction rating in only 62.7% of cells, against 84.1% for the original requests. CUAs should therefore be evaluated not only on task completion but also on whether they meet the requirements and constraints users express. Code: https://github.com/MatrAIx-ai/PersonaCUABench. Project page: https://matraix.ai/research/persona-cua.html

cs.HC↗