Analytics serve the public with privacy

LA County improves indigent adult services, supports policy changes and protects privacy

The Los Angeles (LA) County government wants to better serve indigent adults who depend on government programs. But with numerous programs in separate departments, analysts couldn’t get a complete picture of what was being spent and where. Using SAS® Analytics, LA County was able to get a holistic view of program service utilization, reduce expense redundancies and ensure program participant privacy.

We face tough fiscal restraints and difficult budgetary issues. It’s important that policy makers have access to evidence-based research and cost benefit analysis to guide program policy and governance in Los Angeles County.

Manuel Moreno, PhD
Director of Research

LA County has approximately 96,000 indigent adults receiving social and human services from multiple county agencies, funded solely by the county’s general fund. With no supplemental state or federal support, it is essential that services be delivered cost-effectively – a difficult task to manage when county agencies keep separate records. Individuals might get services or cash payments from several county agencies, ranging from Public Health and Social Services to Public Law Enforcement Services.

“The databases are basically silos of information that are not shared by departments.” explains Dr. Manuel Moreno, Director of Research at LA County’s Chief Executive Office. “Taking a conservative stand, due to confidentiality concerns, government officials previously had been unable to share data across county agencies.”

To help with this issue, SAS provided an analytics environment, a data warehouse and the ability to encode data so that it could be analyzed without compromising privacy. There was initial concern from county officials as to whether de-identifying the data, and encoding it to protect privacy, would still allow successful analysis to achieve cost efficiency. To alleviate this concern, Moreno’s team educated county officials to help them understand that the project would enhance information services, without violating any confidentiality requirements.

“At the end of the day we were not interested in the actual individual identities of the persons participating in the program,” explains Moreno. “We were interested in their history of service utilization – How many times did they go to the emergency room? Do they receive inpatient or outpatient services from a mental health clinic? Are they disproportionately using the services? – as we needed to write a report for the Board of Supervisors highlighting the complex pattern of utilization, either by single departments or across departments, and the costs of serving individuals in the General Relief program. We had all the records of individuals linked across county agencies, but identifiers were removed to comply with confidentiality requirements.”

“We face tough fiscal restraints and difficult budgetary issues,” he continues. “It’s important that policy makers have access to evidence-based research and cost benefit analysis to guide program policy and governance in Los Angeles County.”

The initial work by Moreno’s team is associated with the county’s Enterprise Linkages Project and has enabled the county to:

  • Discover and correct service duplication and program inefficiencies that impact the cost of providing services.
  • Identify General Relief recipients who are eligible for federal programs, like Supplemental Security Income, but haven not applied or their applications are pending due to incomplete documentation.
  • Provide statistical evidence that program expenditures to get homeless individuals into supportive housing deliver savings, as housed individuals depend less on other county services.
  • Use the data to predict costs for new programs.

Moreno’s team is particularly proud of its work on a pilot project for chronically homeless General Relief participants, in which 900 homeless participants were given a rental subsidy. The project saved the county money, and has provided policymakers with evidence-based research to help in deciding whether or not to reinvest funds and expand this viable program.

The county also discovered that mentally ill patients are frequently on and off General Relief and in and out of jail. The data is helping the county identify mentally ill patients for placement in a human services program, that can help them stay out of jail, stay healthy and ultimately, save the county money.

Moreno’s team is now participating in the Intelligence for Social Policy (ISP) initiative, a project funded by the MacArthur Foundation and housed at the University of Pennsylvania. ISP is an initiative in Public Systems Reform and by integrating database systems. Moreno’s team is working with other government agencies and universities to link administrative records for analysis, aimed at improving public programs.

With the first phase of the Enterprise Linkages Project complete, Moreno says the second phase has been recommended to move forward. This phase will provide historical information to eligibility and social workers, so they can match appropriate services to clients. For example, if a person goes to the emergency room 25 times a year, the social worker sees that information and can try a different form of treatment or intervention.

“Recently, the word has been spreading among departments,” Moreno says. “It’s becoming the standard that departments solve problems by integrating data. Knowing that these problems can be resolved without jeopardizing security and privacy allows LA County to lead the nation in providing more effective and efficient public benefits programs.”


  • Understand cross-agency service utilization.
  • Measure the cost and benefits of serving the indigent adults in the county’s General Relief Program.
  • Reduce duplication of services, without compromising privacy.


SAS® Analytics


  • Discover and correct service duplication to reduce costs.
  • Identify adults eligible for federally funded programs.
  • Predict costs for new programs.
  • Conduct evidence-based research.
  • Provide statistical evidence that housing program delivers savings.
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