Many communities have established infrastructure to improve the delivery of services to clients. If well designed, this same infrastructure can be used to collect data for evaluation purposes. For instance, an information and referral (I&R) system is a valuable asset for sharing information on available services and criteria to access those resources with case managers and clients who may need them. Some I&R systems also automate the referral process to expedite client access to resources and to reduce service under-use or duplication. I&R systems can also inventory system assets to permit monitoring over time. If a community sets a goal to increase the number of prevention resources, mainstream supportive service linkages, or permanent supportive housing units, the I&R database becomes an objective way to measure progress toward that goal.
A homeless management information system (HMIS) that collects client-level data to enable coordinated case management also yields extremely valuable longitudinal information on the extent and nature of homelessness episodes, service use patterns, and short- and long-term client outcomes. The State of Arizonas Homeless Evaluation project exemplifies the value of HMIS for case management and evaluation purposes. Arizonas structure encompasses three continuums of care, all of which have functional HMIS implementations. The homeless providers within each continuum use the HMIS to support case management and internal agency record-keeping. Client information is aggregated and analyzed at the continuum level for each communitys planning purposes. The State of Arizona worked with the continuums to develop a Family Self-Sufficiency (FSS) matrix, which uses 13 domains to track a households change in self-sufficiency. The FSS matrix has been incorporated into each continuums HMIS, and case managers report on each of the 13 domains at program entry and exit, and sometimes more frequently. Case managers use the matrix during client assessment to develop a case plan for promoting greater family self-sufficiency. The Arizona Homeless Evaluation Project has begun to analyze the change in FSS results at the program and continuum levels and is using the initial findings to identify which programs are most successful with different client groups. Early results indicate the ability to predict client success in different program models from an initial client FSS assessment. Results are now being used to guide technical assistance, target appropriate client referrals, and develop baselines for program performance. Over time, the FSS measures will likely be integrated into an ongoing performance-based funding process.
In Philadelphia, the city has used HMIS data to understand client characteristics and patterns of shelter use (personal communication with Dennis Culhane and Rob Hess, Philadelphias homeless czar for many years). This information shaped policy decisions that fueled the dramatic strides in building permanent supportive housing and targeted interventions for individuals and families who are homeless. In addition, the HMIS system has become a day-to-day tool for improving services to homeless clients across disciplines (e.g., homeless programs, child welfare services, and behavioral health treatment). For instance, as interventions for chronically homeless people are developed, outreach staff can use the HMIS to identify specific individuals who have experienced long-term homelessness and would benefit most from permanent supportive housing. City staff also use daily statistics to monitor and immediately fill shelter vacancies, manage caseloads, and redeploy case managers to assessment centers with significant numbers of families waiting to be served, among other operational uses. In the aggregate, this information is also used to allocate annual city-controlled grants, benchmark progress on the citys 10-year plan to end homelessness, and inform homeless policy decisions.
As with I&R, the HMIS is fulfilling two important roles one for direct service, another for evaluation both important tools that support system change. HMIS presents opportunities for the future by building predictive models using longitudinal system data, which in turn can be used to triage clients the first time they present with a housing crisis and direct them to the programs and services most likely to be effective given their circumstances. Conversely, the data collected over time about these clients can be used to assess the effectiveness of and make improvements in the communitys interventions.
In most communities, only homeless providers and a handful of mainstream agencies participate in the HMIS. For HMIS to truly support and/or measure system change, the infrastructure will need to expand and achieve participation among providers from mainstream systems, or develop ways through data warehousing or other techniques to match and integrate data across systems.