Unlocking the Last Mile: Why Housing Providers Need Social Care Data Exchange

Housing providers don’t just offer shelter. They stabilize lives, notice early warning signs, and serve as a daily point of contact for people whose health and social needs often go unmet in siloed systems. Yet these same providers are too often left outside the data-sharing loop. That gap doesn’t just frustrate staff—it derails progress for residents.

Social care data exchange is how we bridge that gap. By connecting housing, health, and human services, we ensure that the last mile of care—where people actually live—is supported with real information, not guesswork.

Gaps That Put Residents at Risk

Even the best housing programs are hamstrung when they don’t know what’s happening in the broader care system. Residents might return from the hospital with new prescriptions or care instructions, but unless housing staff are notified, follow-up often falls through. Others may cycle through crisis stabilization or detox programs without their housing provider ever learning what happened. Case managers spend countless hours trying to confirm referrals for food or transportation, but without electronic confirmation, they never know if the help arrived. Layer on top the complexity of privacy laws like HIPAA and 42 CFR Part 2, and the default often becomes silence.

The result is predictable: more ER visits, more missed opportunities, and more people cycling back into homelessness.

Real-World Examples of Interoperability in Action

Several communities have already shown what’s possible when housing providers are included in data exchange.

In Alameda County, California, a Social Health Information Exchange and Community Health Record pull together data from health, housing, behavioral health, crisis, and even incarceration systems. Housing providers don’t get a flood of clinical details they can’t use—they get a “thin slice” of actionable information, like whether a resident had a recent ER visit or which other agencies are actively involved. This lets staff intervene early and coordinate support before small issues become major setbacks.

Other states have taken a broader approach. Through the NASHP Health and Housing Institute, Illinois, Louisiana, New York, Oregon, and Texas worked to break down silos across Medicaid, housing, and social services. By integrating data across agencies, these states could identify individuals with complex needs, direct supportive housing resources to where they were most effective, and use the evidence to justify ongoing investment. For housing providers, this alignment means fewer blind spots and a stronger foundation for partnership with state programs.

Oregon has gone further by embedding social determinants of health into its Health Information Exchange strategy. Its workgroup identified priority use cases—like food insecurity and housing instability—that flow directly into clinical workflows. That means housing status isn’t an afterthought. It shows up in the same place as blood pressure readings or lab results, prompting providers to coordinate with housing partners and address barriers head-on.

And in Washington State, integrated client databases link more than 30 state agency systems. While these aren’t real-time dashboards for case managers, they do give policymakers and providers a much clearer picture of overlap across health, social services, and housing. That insight helps target interventions, identify gaps, and build a stronger case for supportive housing as a healthcare solution.

The Connecticut Coalition to End Homelessness together with the Department of Housing and the Department of Social Services proceed with monthly data matching between HMIS (Homeless Management Information System) and the Medicaid Management Information System (MMIS). The data match is enabled by a Release of Information (ROI) form that clients sign. With this matched data, the state policy task force has been able to identify families and individuals engaged in multiple systems, understand the cost implications of being unhoused, and design targeted interventions.

Michigan performed a one-time match between MMIS and HMIS to find “high utilizers” — people who are both heavily using health care (via Medicaid claims) and homeless or housing unstable (via HMIS). The matched data helped the state understand where supportive housing investments might reduce costs or improve outcomes.

The Massachusetts “Rehousing Data Collective” aggregates data from all 12 HMIS systems in the state plus the state’s family-based shelter system. The Medicaid agency (MassHealth) uses that data to more accurately identify its members in homeless or shelter systems and adjust its approaches (e.g., in risk adjustment, continuous eligibility policies) to better support those experiencing homelessness.

Minnesota’s statewide HMIS is a collaboration between over 220 participating agencies, which includes the 10 Minnesota Continuums of Care, the Minnesota Tribal Collaborative to Prevent and End Homelessness, the State of Minnesota, the Institute for Community Alliances, and other participating partner agencies. The HMIS website provides important administrative documents needed to facilitate data sharing, including a state agency agreement, coordinated services agreement, and client release-of-information forms.

What Housing Providers Really Gain

For housing providers, these efforts aren’t just theoretical. They translate into practical tools that make daily work easier and more effective. With real-time alerts, a case manager knows when a resident has been discharged and can arrange a check-in before complications arise. Shared care plans reduce duplication and prevent residents from having to retell their story over and over. Closed-loop referral systems confirm whether the services ordered—be it food, transportation, or employment support—actually reach the resident. And on a larger scale, aggregate data helps providers demonstrate to funders and policymakers that housing isn’t simply shelter, it’s healthcare in action.

When these pieces come together, housing providers gain the ability to act with confidence rather than guesswork. They can anticipate needs, coordinate with partners, and show results that sustain funding.

Lessons From the Field

The communities that have made progress in social care data exchange share a few common traits. Strong governance and clear consent frameworks build the trust necessary for sharing sensitive data. Technology is designed with the last mile in mind—simple, mobile-friendly tools that don’t assume providers have a full IT department. Instead of trying to build everything at once, they start with specific, high-value use cases, like discharge alerts or food insecurity screening, and expand over time.

Funding and policy support are also critical. Whether through Medicaid waivers, state appropriations, or managed care investment, sustainable financing keeps systems running long after the pilot phase. And perhaps most importantly, communities that succeed make sure housing providers and people with lived experience are at the table. Without their input, systems risk becoming well-intentioned but irrelevant.

Key Take Aways

• Matching HMIS + Medicaid/MMIS is powerful

  When homelessness / housing data (via HMIS) is linked with Medicaid data, the overlap often reveals who is both housing unstable and high‐cost in health. That gives leverage for advocacy, for designing supportive housing programs, and for making the cost-case to payers or funders.

  
  

• Consent & legal agreements are critical

  In Connecticut, the ROI (release of information) form is what allows monthly matching. In multiple states, data sharing is enabled by memoranda of understanding, data use agreements, or master agreements. Without this legal work, even well-intentioned efforts stall.

  
  

• Aggregation & centralized warehouses help with scale

  Massachusetts’ Rehousing Data Collective is a great example: instead of each CoC or shelter system being isolated, data is merged into a statewide repository, making it possible for the Medicaid agency to “see” across all shelter/family-based systems, and design policies accordingly.

  
  

• One-time matches vs ongoing feeds

  Some of these are “one-time data matches” (Michigan, New Jersey) designed to answer specific questions (who are high utilizers, what are cost savings). Others are ongoing (Connecticut’s monthly matching, MA’s continuous eligibility policies). Both approaches are useful: one-time matches can lay groundwork; ongoing feeds allow operational / preventative action.

  
  

• Data reports & dashboards amplify impact

  Showing the matched data in reports, briefing papers, dashboards gives visibility to the problems and supports policy change. States that publish findings (e.g. data briefs in CT, or evaluation reports in Washington) tend to translate shared data into policy or resource shifts.

  
  

• Local partners / Continuums of Care (CoCs) / public housing authorities are important partners

  Many of the data matches or sharing efforts involve HMIS, CoC administrators, state housing agencies, public housing authorities. Having those housing-side partners engaged is essential for supplying homelessness data, consent, and ensuring results get used in practice.

  
  

• Policies & payment structures enable or constrain

  In Massachusetts, for example, the partnership between HMIS data and MassHealth underlies policies like continuous eligibility for members experiencing homelessness. State Medicaid policy, risk adjustment methodologies, waiver authorities, etc., often determine how much impact matched data / data sharing can have.

Looking Ahead

The future holds enormous promise for housing providers if they are fully brought into the data exchange ecosystem. Efforts like the Gravity Project are standardizing social needs data, which will make it easier to share housing information across systems. Advances in real-time alerts mean housing staff will know almost instantly when residents experience a health crisis or a change in care. Privacy-preserving data exchange models will allow sensitive information to flow securely without compromising confidentiality. And as payment reform accelerates, housing will increasingly be recognized and reimbursed as a form of healthcare, making data exchange not just helpful, but essential.

Taking Action: Where to Start

For organizations considering a community information exchange or other data-sharing approach, the first step isn’t technology—it’s conversation. Bringing together housing providers, healthcare systems, behavioral health agencies, and social service partners around the same table helps identify shared priorities. What information do partners need most urgently? What are the use cases that will deliver immediate value? Often it’s not the grand vision of full integration that sparks momentum, but a small, focused effort like sharing hospital discharge alerts or building a closed-loop referral path for food insecurity.

From there, the focus should shift to governance and trust. Establishing clear agreements on privacy, consent, and accountability builds the confidence needed to move forward. Piloting with a few willing partners can demonstrate proof of concept, attract funding, and show skeptical stakeholders that this isn’t just another data project—it’s a way to improve lives.

The last mile is where lofty goals meet lived reality. By starting small, building trust, and expanding step by step, communities can create data exchange models that truly serve housing providers and the residents who count on them.