A New Approach to Community Partnerships: Sharing Data the Tulsa Way

Author note: Family & Children’s Services (https://www.fcsok.org/) in Tulsa, Oklahoma, was a key data contributor in the data-sharing pilot project described in this article.

Healing Tulsa: A Community-Centered Approach

Family & Children’s Services (FCS) is one of the largest and most trusted behavioral health and social services organizations in Oklahoma, serving communities in the Tulsa region for more than a century. Throughout its history, the organization has deeply understood that mental health care is not just about treating conditions – it’s about building relationships, listening to individual stories and creating a compassionate support system. By fostering these connections, FCS has been able to tailor its approach to meet the unique needs of the people it serves. A crucial part of this effort has been establishing strong partnerships with funders, foundations, governments and community stakeholders. These networks are key to leveraging resources, expanding access to care and providing comprehensive services that make a real difference in people’s lives.

The Power of Data in Addressing Complex Challenges

Information technologies have created new ways to see the challenges facing communities and track and assess interventions and their outcomes. Data reveals structural inequalities and their ongoing effects on behavioral and medical health. Data highlights social determinants and provides trackable, quantifiable insights into the challenges that organizations like FCS aim to address: strengthening families, improving education and employment opportunities, reducing substance use and addiction, treating mental health needs, and ensuring coordinated care.

The challenges that communities face today are complex and interconnected – often called “wicked problems” by design theorists and online innovators [1]. From health disparities to housing instability, these issues demand coordinated responses rooted in deep understanding and adaptability to ever-evolving communities.

For example, when data showed that Oklahoma led not only the nation but the entire world in the rate of female incarceration, it signaled the need for drastic improvement and targeted intervention. Family & Children’s Services, in partnership with the George Kaiser Family Foundation, launched the nationally lauded Women in Recovery (WIR) program to address this complex problem [2]. The research identified pathways to female incarceration as shaped by social determinants of health including access to treatment addressing addiction and trauma, parent-child reunification services, education, economic opportunities, unmet medical needs, criminal legal involvement and more. This data showed all the domains in which WIR interventions needed to work to address this complex problem.

Each participant in the WIR program brings a distinct narrative, which is intricately mapped within sophisticated service delivery tracking systems. This data informs individualized care tailored to each participant’s needs. Since the inception of Women in Recovery, this data-driven approach has significantly contributed to achieving a 72% reduction in female incarceration rates in Tulsa County.

The Case for Integrated Community Data Ecosystems

Although WIR has had a significant impact, it’s just one part of the larger picture. Addressing these complex issues at scale requires more than isolated programs – it demands a coordinated, data-driven approach. Integrated community data ecosystems are crucial to addressing these complex social problems on a broader and more systemic level [3]. Simply collecting data is not enough. When information is coordinated through collaboration and data sharing, integrated community data can reveal larger patterns and highlight gaps, driving better decision-making and policy.

Data-Sharing Challenges

Though the benefits of data sharing are clear, the hurdles are significant [4]. Privacy concerns, especially around sensitive data such as health records, demand strict compliance with regulations, including the Health Insurance Portability and Accountability Act of 1996 (HIPAA), in which even a small breach can have serious consequences. Organizations often use different systems, making data sharing technically challenging, especially when outdated infrastructure is involved. Trust is another barrier – many fear that shared data could be misused, misunderstood or taken out of context.

These concerns can lead to siloed data, resulting in fragmented solutions and poorly informed decision-making [5]. Without integrated data, organizations risk incomplete information, limiting their ability to effectively serve communities. Opportunities to collaborate and reduce redundancy, waste or gaps in care can be missed. Worse, communities may face delayed interventions, inefficient use of resources and a lack of comprehensive support, leaving the most vulnerable populations underserved.

Addressing Oklahoma’s high rate of female incarceration required gathering data across disparate systems, including criminal legal system data. This involved coordinating criminal legal data insights, understanding where the need was and who to serve, and tracking outcomes for those in diversion programs versus those who were not. It also included related data such as employment, Department of Human Services (DHS) involvement and health outcomes. All of this needed to be collected while protecting individual privacy.

Sharing Data the Tulsa Way: A Federated Model

This work in Tulsa set out to greatly reduce the complexities of data sharing [6]. Together, data-driven organizations and technologists across the city developed a model that allows for secure data sharing without requiring the physical transfer of data. By cryptographically hashing the data where it resides and deidentifying sensitive personally identifiable information for analysis, this system minimizes the time needed to address legal and regulatory compliance.

Why This Model is Better Than Traditional Methods

Traditional data-sharing methods rely on transactional exchanges: One organization hands over its data to another, or to a central system, often losing control over how it’s stored, managed and used. This can lead to significant delays, duplicated efforts and concerns over data privacy and security, effectively excluding key community players from participating.

In 2019, a promising architectural concept called “data mesh” emerged from the mind and experience of technologist Zhamak Dejani [7].

Data mesh represents a growing shift toward federated data infrastructure, in which data remains decentralized and each department, agency or organization maintains ownership of its data while still participating in a collaborative network of governance, data enablement services and shared tooling. As we build more complex, integrated data ecosystems, data mesh offers a clear path forward allowing us to balance organizational control of data sources with centralized governance and technical capacity, promoting innovation and trust.

Key Features of the Tulsa Way

Our acceptance of decentralization as the starting point of our approach isn’t an architectural decision as much as an acknowledgment that there is never one integrated data store, organization, individual, institution or initiative that will completely centralize the data in a community. Our federated model builds on this experience and provides practical steps toward achieving a more interconnected, data-driven ecosystem.

• Federated Technical Infrastructure: Unlike hub-spoke models in which data is centralized, in our approach, sensitive personally identifiable information (PII) stays with the organizations that produce it. Only service data necessary to answer a question or enable an event notification is transported, along with linking keys when necessary. Each partner maintains control, allowing secure collaboration across the network.
• Federated Governance: Unlike centralized governance in which one entity controls decisions, federated governance distributes decision-making across all partners. This ensures equal participation and trust with shared rules for security and data access.
• Decentralized Domains: Each organization owns its data, ensuring more flexibility and local autonomy.
• Privacy-Enhancing Technologies (PETs): Traditional data-sharing models often involve risky data transfers. In contrast, our model uses PETs, such as secure, hash-encoded, privacy-preserving record linkage techniques that allow organizations to securely analyze and share data without exposing sensitive information.
• Bridging Transactional and Analytical Data: Traditional systems struggle to integrate real-time transactional data with historical or analytical data. Our federated model allows nodes to contribute to the greater ecosystem and support both transactional and analytical data pipelines.

Looking Forward: Scaling and Improving Data Sharing

Combining well-designed data collection, organization and reporting informs program decision-making to enhance outcomes. Many of the community data sources that provide value require data cleaning and connecting program data with arrest records, court records and Department of Corrections records. While the FCS WIR program focuses on life-changing interventions and effective service delivery, technology firm Asemio focuses on data wrangling, technical pipeline creation and integrated data systems development to make data interoperable. The combined power of strategic program delivery and sophisticated data-informed decision-making has helped identify gaps in services, measure reach and monitor impact.

This same data-sharing approach is now being used to tackle chronic absenteeism in Tulsa [8]. By linking court eviction data to absenteeism, schools can target interventions more effectively. Additionally, this model has opened doors for identifying and addressing substance use disorder and mental health treatment needs, strengthening families and providing wraparound support.

Building Resilient Communities Through Data

Data is more than just numbers – it’s a frame for understanding the lives of individuals and the makeup of our communities. Behind every data point is a person: a family seeking stability, a child in need of opportunity or a community fighting to overcome systemic barriers. Advancing an agile, safe and person-focused data-sharing model builds a framework to more quickly understand and address the root causes of complex social issues.

The Tulsa way of sharing data brings more than just efficiency; it fosters collaboration. This approach has the potential to circumvent traditional barriers, enabling organizations to access data and reach partners they might not have been able to reach before. Using the latest technologies to overcome privacy concerns and outdated systems is making it possible to foster new relationships within the community and help build more holistic, equitable solutions.

To learn more about the pilot project that has spurred dozens of data-sharing projects, please download the full case study [9].

References

1. https://www.interaction-design.org/literature/topics/wicked-problems
2. Nicholas Kristof, 2024, “The Addiction Recovery Story We Don’t Hear Enough,” The New York Times, February 14, https://www.nytimes.com/2024/02/14/opinion/drug-addiction-recovery.html.
3. https://player.captivate.fm/episode/f62eacb4-7013-477f-b6ca-28cca5f17612/
4. “Issues and Challenges Associated with Data Sharing,” 2016, Principles and Obstacles for Sharing Date from Environmental Health Research: Workshop Summary, Washington, D.C.: National Academies Press.
5. Alex Bendersky, 2023, “The Challenge of Data Silos in Healthcare,” Medium, October 13, https://medium.com/@alex.bendersky/the-challenge-of-data-silos-in-healthcare-45820de7597c.
6. https://asemio.com/wp-content/uploads/2022/10/How-Tulsa-is-Preserving-Privacy-and-Sharing-Data-for-Social-Good-2.pdf
7. https://www.thoughtworks.com/en-us/profiles/z/zhamak-dehghani
8. Justin Ayer, 2024, “Impact Tulsa Discovers Correlation Between Student Evictions and Absenteeism,” 2 News Oklahoma, March 7, https://www.kjrh.com/news/local-news/impact-tulsa-discovers-correlation-between-student-evictions-and-absenteeism.
9. https://asemio.com/who-we-serve/case-studies/download-data-sharing-case-study/