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Hospitals and care homes devote significant resources to placing post-acute patients from hospitals into long-term care. This paper describes a two-phase experiment over SMS, conducted with a hospital in Hawai'i, in which care homes express preferences, indicate availability to accept patients, and express interest in patients. In the first phase, the treatment asks care homes to reconsider their stated preferences to better support matching. The second phase measures whether resulting changes in preferences increased how often homes express interest in patients that match newly stated preferences. First, to motivate and inform experiment design, we explore factors contributing to extended hospital stays for patients, uncovering how care homes' preferences play a major role in what patients they consider accepting. Second, we conduct a 16-week randomized controlled trial with 960 homes, where we experimentally probed, via SMS messages, homes' willingness to change their preferences to improve potential patient match recommendations. We show that inducing homes to reflect on their preferences increased the number of homes who changed their preferences by over 50%: 9.8% of homes who received our treatment changed their preference compared to 6.0% of homes in the control group (p-value = 0.0421).Third, followup interviews with 22 home operators highlight how preference malleability is shaped by a combination of design constraints and on-the-ground realities, such as load-balancing existing patient rosters. Finally, we discuss implications for real-world systems like ours that must balance constrained communication with situational complexity towards improving outcomes. 

As the US population ages, a growing challenge is placing hospital patients who require long-term post-acute care into adult foster care facilities: small long-term nursing facilities that care for those unable to age in place because their care requirements exceed what can be delivered at home. A key challenge in patient placement is the dynamic matching process between hospital discharge coordinators looking to place patients and facilities looking for residents. We designed, built, deployed, and maintain a system to support decision making among a team of six discharge coordinators assisting in the discharge of 127 patients across 1,047 facilities in Hawai'i. Our system collects vacancy and capability data from facilities via conversational SMS and processes it to recommend facilities that discharge coordinators might contact. Findings from a 14-month deployment provide evidence for how timely, accurate information positively impacts matching efficacy. We close with lessons learned for information collection systems and provisioning platforms in similar contexts. 

AI-driven tools are increasingly deployed to support low-skilled community health workers (CHWs) in hard-to-reach communities in the Global South. This paper examines how CHWs in rural India engage with and perceive AI explanations and how we might design explainable AI (XAI) interfaces that are more understandable to them. We conducted semi-structured interviews with CHWs who interacted with a design probe to predict neonatal jaundice in which AI recommendations are accompanied by explanations. We (1) identify how CHWs interpreted AI predictions and the associated explanations, (2) unpack the benefits and pitfalls they perceived of the explanations, and (3) detail how different design elements of the explanations impacted their AI understanding. Our findings demonstrate that while CHWs struggled to understand the AI explanations, they nevertheless expressed a strong preference for the explanations to be integrated into AI-driven tools and perceived several benefits of the explanations, such as helping CHWs learn new skills and improved patient trust in AI tools and in CHWs. We conclude by discussing what elements of AI need to be made explainable to novice AI users like CHWs and outline concrete design recommendations to improve the utility of XAI for novice AI users in non-Western contexts. 

The mass collection and reuse of social data requires a reimagining of privacy and consent, with particular attention to the (in)equitable distribution of benefits and burdens between researchers and subjects. Instrumenting frontline clinical services to collect and steward data might mitigate the exploitation inherent to data collection---with attention to how subjects can meaningfully participate in stewardship. We explore participatory data stewardship in the context of clinical computer security for survivors of intimate partner violence (IPV). Via semi-structured interviews with IPV support workers, we explore how data are produced within the IPV care ecosystem at the Clinic to End Tech Abuse (CETA). We then conduct design provocations with clients of IPV services and their support workers, exploring possibilities for participatory data mechanisms like open records and dynamic consent. We find participation in data stewardship may benefit clients through improved agency, self-reflection, and control of self-narrative, and that incurred burdens may be alleviated by enlisting trusted stewards. We close with future work for CSCW interrogating how knowledge of digital-safety harms can and should be produced from clinical encounters, towards more equitable ways of knowing. 

Home care workers (HCWs) are professionals who provide care to older adults and people with disabilities at home. However, HCWs are vulnerable and especially susceptible to wage theft, or not being paid their legally-entitled wages in full by their employers. Prior work has examined other low-wage work settings to show how technology is designed and deployed has the potential to both cause and address wage theft. We extend this work by examining the relationship between technology and wage theft in the home care context. We collaborated closely with a local grassroots organization to conduct interviews with workers and labor, legal, and payroll experts. We uncovered how the complex, volatile, and diverse nature of home care complicates the errors in time-tracking systems. Through design provocations and focus groups with workers and experts, we also investigated the potential of technology as a part of broader efforts to curb wage theft through educating and empowering isolated HCWs. While we found that approachable design could reduce errors in existing systems, make employer processes more transparent, and help workers exchange knowledge to build collective power, we also discuss concerns around burden, privacy, and accountability when designing technologies for HCWs and other low-wage workers.