HCMar 7

Collaboration by Mandate: How Shared Data Infrastructure Shapes Coordination and Control in U.S. Homelessness Services

arXiv:2603.07354v1
Predicted impact top 81% in HC · last 90 daysOriginality Incremental advance
AI Analysis

This research is significant for public-interest design and civic data infrastructure by illustrating how mandated data sharing can enable coordination and accountability while reproducing power asymmetries in data interpretation and decision-making within homelessness service networks.

This study investigates how mandated shared data systems, specifically the Homeless Management Information System (HMIS) in U.S. homelessness services, function as both coordination tools and instruments of control. It found that while standardization can facilitate collaboration and shared learning, unequal resources and analytic capacity limit equitable participation, often relegating some participants to compliance-focused roles.

When governments mandate collaboration, shared data systems can serve both as tools for coordination and instruments of control. This study examines U.S. homelessness service networks, where Continuums of Care (CoCs) coordinate service providers through the federally mandated Homeless Management Information System (HMIS). With client consent, providers enter data into HMIS and access cross-provider service histories to support coordinated care. At the same time, HMIS embeds standards and governance rules that shape who can collect, access, interpret, and act on data, and thus who holds decision authority. Using qualitative interviews with six experts, we show that standardization can facilitate collaboration and shared learning. However, unequal resources, analytic capacity, and authority limit equitable participation and often shift some participants toward compliance-focused roles. We contribute to public-interest design research on civic data infrastructures by illustrating how mandated data sharing can simultaneously enable coordination and accountability while reproducing power asymmetries in data interpretation and decision-making.

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