Data Governance Design for Collaborative Multi-Agency Data Analysis

Overview

The Indiana Family and Social Services Administration’s vision is for “all Hoosiers to live in fully engaged communities and reach their greatest emotional, mental, and physical well-being.” Their primary services are childcare & education, employment help, food & cash assistance, health coverage (including Medicaid), home/community services, mental health & addiction, and many smaller subsets. For a government agency that oversees many different services, especially considering a recent state initiative launched that includes cross-agency collaboration to support individuals in recovery secure housing, it was of utmost importance for them to be leveraging and maintaining the quality of their data.

Access to health insurance for ex-inmates is a real struggle in the United States. Occasionally states fail to re-enroll inmates who are being released in Medicaid despite being included in the Affordable Care Act. According to an article by NPR, most of the state prison systems in the 31 states that expanded Medicaid have either not created large-scale enrollment programs or operate spotty programs that leave large numbers of exiting inmates – many of whom are chronically ill – without insurance. This can lead to a higher risk of re-incarceration or death among former inmates.

Challenges

Although FSSA keeps data quality and security top of mind, they wanted to enable better cross-sector data sharing. Specifically, Indiana Medicaid has demonstrated the power of strong cross-sector data sharing agreements through their collaboration with the Indiana Department of Corrections (DOC). Recently released prisoners have been neglected from re-enrollment opportunities and the Family and Social Services Administration is actively working to improve it. Medicaid and DOC entered a collaborative effort aimed at using data to identify and solve the lengthy amount of time to suspend and reactivate Medicaid for the newly incarcerated and post-incarcerated population.

CSpring leveraged the MITA framework that aimed at identifying, designing, and managing enterprise data across systems. Using the MITA framework ensures that our team would achieve efficient and effective data governance design. This governance strategy combined recognized industry standards and the Center for Medicare and Medicaid Services (CMS) recommendations to guide FSSA’s approach towards managing data and information, including data sharing.

To unravel these issues, CSpring connected siloed and disparate datasets and tables, along with analysis of the business processes behind the data exchange. In response, Medicaid is reviewing how to overlay Presumptive Eligibility (PE), a process that offers short-term coverage of health care services for those with limited incomes who are not currently receiving Medicaid, onto current processes to improve outcomes.

Results

CSpring’s data governance design holds data owners accountable for the consistency and quality of their data. Medicaid can leverage their data assets more effectively without worrying about their data quality. Data governance allows Medicaid to collaborate across sectors to more efficiently deliver services to those in need. Medicaid can reduce costs at the state and federal levels by suspending Medicaid enrollees that are incarcerated and activating coverage upon release. This project advances the Medicaid Enterprise Systems through contributions to the growing body of research behind data governance and Medicaid enrollment for other states to develop their own standards and processes.

Need help getting started with your data governance design and strategy? Request a free consultation today.