Designing System Support for Sharing Everyday Data for Chronic Care

Abstract

The prevalence of chronic conditions has shifted care settings into private homes. In addition, the increasing availability of physical- and algorithmic-sensing solutions promises large quantities of data about one’s everyday life that would be useful for such care. For a person significantly affected by a chronic condition, conducting chronic care at home is likely to involve a care team comprising family members, caregivers, and clinicians. The success of chronic care requires care team members to collaboratively assist people affected by chronic conditions by monitoring their lives and developing care routines for necessary self-care activities in response to different stages of their health trajectory. Sharing these everyday data for care (EDC) could provide awareness and support collaboration among care team members, which would assist the person with a condition requiring a care team (PCT). However, EDC details might have unintended consequences, such as negative impressions or the invasion of privacy.

A variety of solutions for user control have been developed and published in the existing literature on designing systems to support data sharing. However, very few user-facing solutions for user control have carefully considered the healthcare context, particularly the collaborative nature of self-care, the need to support fine-grained control to develop independence, and variance in the PCT’s capacity to direct EDC sharing due to changes in their health and priorities. To address these considerations in the care context, this dissertation presents a threefold investigation into system designs that support PCT’s sharing of EDC in the context of chronic care.

The first approach implemented a user-facing system, Data Checkers, to support fine-grained control and preview shared results. This would allow PCTs to explicitly regulate EDC data flow. The design of Data Checkers, including its grid-based interface, was grounded in a co-design process with a person with a severe condition, and it embeds the critical requirements for a PCT. A qualitative evaluation demonstrated the potential use of Data Checkers for long-term care.

The second approach investigated the applicability of user-created EDC groupings to simplify EDC sharing configurations. The goal of using these groupings is to reduce the user burden when managing many EDC sources. The results of a scenario-based study showed that self-generated EDC groupings can be reused as high-level units to efficiently create sharing settings for potential recipients when dealing with changes in health trajectories. These findings also suggested the possibility of developing a machine-assisted mechanism for suggesting policies that are consistent with the PCT’s preferences, particularly as health changes.

The third approach considered using human and machine delegates to cover the responsibilities of sharing EDC to support PCTs whose capacity is greatly reduced due to health concerns and other life priorities. The results of a scenario-based interview study showed that obtaining assistance for sharing data requires nuanced considerations of both the delegate’s characteristics and the implications of the delegation. Also, human and machine delegates possess possible complementary qualities for handling these nuances, which suggests the potential for a human-machine collaborative approach to EDC-sharing assistance.

This dissertation concludes with a discussion of the necessity to align user-facing system design with the nature and practices of care. Moreover, it provides a meta-analysis and identifies multiple capacity dimensions, including the capacity of a care team, which is critical to examining the development of a capacity-aware approach to EDC sharing for chronic healthcare.