Data sharing agreements between K–12 schools and school-based health centers or other health care systems

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Data sharing agreements between K–12 schools and school-based health centers or other health care systems — August 2017

Question

Could you provide information on data sharing agreements between K–12 schools and school-based health centers or other health care systems?

Response

Following an established REL West research protocol, we conducted a search for research reports as well as descriptive study articles on data sharing agreements between K–12 schools and school-based health centers or other health care systems. The sources included ERIC and other federally funded databases and organizations, research institutions, academic research databases, and general Internet search engines (for details, please see the methods section at the end of this memo).

We have not evaluated the quality of references and the resources provided in this response. We offer them only for your information. Also, we searched for references through the most commonly used sources of research, but the list is not comprehensive and other relevant references and resources may exist.

Research References

Center for IDEA Early Childhood Data Systems. (2014). Data sharing agreement checklist for IDEA Part C and Part B 619 agencies and programs. Washington, DC: Privacy Technical Assistance Center (PTAC), U.S. Department of Education. Retrieved from https://dasycenter.sri.com/downloads/DaSy_papers/DaSy_Data_Sharing_Agreement_Checklist_Access.pdf

From the abstract: “This 2014 document is an adaptation of the 2012 release of ‘Data Sharing Agreement Checklist’ intended for K–12 audiences. Presented as a checklist, the document summarizes the requirements for the written agreements under the audit or evaluation exception that is specified in FERPA and that also applies to the IDEA for Part C early intervention and Part B 619 preschool special education.”

Hedden, E. M., Jessop, A. B., & Field, R. I. (2014). An education in contrast: State-by-state assessment of school immunization records requirements. American Journal of Public Health, 104(10), 1993–2001. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4167093/

From the abstract: “Objectives. We reviewed the complexities of school-related immunization policies, their relation to immunization information systems (IIS) and immunization registries, and the historical context to better understand this convoluted policy system. Methods. We used legal databases (Lexis-Nexis and Westlaw) to identify school immunization records policies for 50 states, 5 cities, and the District of Columbia (Centers for Disease Control and Prevention “grantees”). The original search took place from May to September 2010 (cross-referenced in July 2013 with the list on http://www.immunize.org/laws). We describe the requirements, agreement with IIS policies, and penalties for policy violations. Results. We found a complex web of public health, medical, and education-directed policies, which complicates immunization data sharing. Most (79%) require records of immunizations for children to attend school or for a child-care institution licensure, but only a few (11%) require coordination between IIS and schools or child-care facilities. Conclusions. To realize the full benefit of IIS investment, including improved immunization and school health program efficiencies, IIS and school immunization records policies must be better coordinated. States with well-integrated policies may serve as models for effective harmonization.”

National Forum on Education Statistics. (2012). Forum guide to supporting data access for researchers: A state education agency perspective (NFES 2012-809). Washington, DC: National Center for Education Statistics. Retrieved from https://eric.ed.gov/?id=ED533865

From the foreword: “This document recommends a set of ‘core’ practices, operations, and templates that can be adopted and adapted by state education agencies (SEAs) as they consider how to respond to requests for data about the education enterprise, including data maintained in longitudinal data systems. These recommendations reflect core practices and principles for managing the flow of data requests, establishing response priorities, monitoring appropriate use, protecting privacy, and ensuring that research efforts are beneficial to the education agency as well as the research community. It should be noted that a corollary, but significant, benefit to ‘best practice’ data sharing is improved relationships between SEAs and the colleges, universities, and policy organizations that frequently employ education researchers. The primary audiences for this document are data policymakers and managers in SEAs who are generally responsible for managing and responding to requests for data. An additional important audience for this resource is the research community that submits data requests to SEAs. Data requests come from diverse sources, ranging from members of the research community to advocacy organizations, the media, the public, and other parties interested in education data. This document is intended to help SEAs determine whether and how to voluntarily fulfill requests from researchers for access to education data, including confidential or otherwise restricted data. It does not focus on recommendations for dealing with requests for publicly available data, Freedom of Information Act requests, or requests from legislators or other stakeholders who may be in a position to require a response from the SEA. Although this document focuses on SEAs, other stakeholders, including staff in local education agencies (LEAs), may wish to consider adapting components of these core practices to suit their needs.”

U.S. Department of Education. (2016). Data-sharing tool kit for communities: How to leverage community relationships while protecting student privacy. Washington, DC: Author. Retrieved from https://www2.ed.gov/programs/promiseneighborhoods/datasharingtool.pdf

From the abstract: “The purpose of this tool kit is to inform civic and community leaders who wish to use shared data to improve academic and life outcomes for students while protecting student privacy. A key success factor in doing this well is understanding the Family Educational Rights and Privacy Act (FERPA) and its parameters relative to the sharing of personally identifiable information (PII) from education records. This tool kit is designed to simplify the complex concepts of FERPA. It may be used both as a comprehensive guide and a collection of one-page resources. The tool kit covers the following three primary focus areas: (1) Understanding the importance of data collection and sharing; (2) Understanding how to best protect student privacy when collectively using PII from students’ education records that is protected by FERPA (including the best practices for obtaining written consent, or, where applicable, complying with FERPA’s exceptions to non-consensual disclosure of data); and (3) Understanding how to manage shared data using integrated data systems.”

U.S. Department of Education, Privacy Technical Assistance Center. (2012). Written agreement checklist. Washington, DC: Author. Retrieved from http://ptac.ed.gov/sites/default/files/Written_Agreement_Checklist_0.pdf

From the introduction: “The purpose of this document is to summarize the requirements for the written agreements under the studies exception and the audit or evaluation exception as specified in the Family Educational Rights and Privacy Act (FERPA). The FERPA regulations on the studies exception requires that the educational agency or institution or the state or local education authority or agency headed by an official listed in 34 CFR §99.31(a)(3) execute a written agreement with the organization conducting the study when disclosing personally identifiable information from education records without consent (see 34 CFR §99.31(a)(6)(iii)(C)). The FERPA regulations on the audit or evaluation exception require that the state or local education authority or agency headed by an official listed in 34 CFR §99.31(a)(3) must use a written agreement to designate any authorized representative other than an employee (see 34 CFR §99.35(a)(3)). The mandatory elements of that agreement vary slightly between the two exceptions. The following checklist delineates the minimum requirements under the studies and the audit or evaluation exceptions. The list of the mandatory elements is followed by best practice suggestions that may help to further enhance the transparency and effectiveness of the agreements. It is important to keep in mind that individual state privacy or procurement laws may contain more stringent requirements for data sharing written agreements, and that other Federal privacy laws, such as the Individuals with Disabilities Education Act (IDEA) and the Health Insurance Portability and Accountability Act (HIPAA), may be applicable depending on the type of data being shared and the entities with whom the data are shared. Therefore, parties entering into an agreement are advised to always consult with their procurement staff and/or legal staff to ensure compliance with all applicable Federal, state, and local laws and regulations.”

Other Resources

Academy Health. (2016). Explaining the value of data sharing: Lessons learned [Blog post]. Washington, DC: Author. Retrieved from http://www.academyhealth.org/blog/2016-11/explaining-value-data-sharing-lessons-learned

From the abstract: “Health leaders, including those in public health are increasingly engaging and mobilizing community partners from multiple sectors to share and use data to address social determinants and improve population health. Building meaningful relationships with other sectors requires engagement and determination of common values that clearly map to the potential benefits of collaboration and data sharing. This is no easy task; different entities engaged in data sharing projects, particularly those from other sectors, may have quite varied capacity aims, business models, and desired outcomes.”

Fraser, J. (2011). Framework for collaboration: The memorandum of understanding between Allegheny County DHS and Pittsburgh Public Schools. Pittsburgh, PA: Allegheny County Department of Human Resources. Retrieved from http://www.alleghenycountyanalytics.us/wp-content/uploads/2016/06/Framework-for-Collaboration-The-Memorandum-of-Understanding-between-Allegheny-County-DHS-and-Pittsburgh-Public-Schools.pdf

From the abstract: “The idea had been discussed for several years. What if Pittsburgh Public Schools and the Allegheny County Department of Human Services found a way to integrate the data they gather on students of mutual interest and use it to better inform strategies for helping those in need and improving their outcomes? Integrating data on issues ranging from student achievement and attendance to housing, child welfare and mental health services offers several potential advantages. It could, for example, help school officials better understand circumstances outside of school that influence the performance and behavior of students in school. Child welfare caseworkers could more reliably monitor how their young clients are doing academically and whether they are attending school regularly. A research partnership could lead to a better understanding of the impact certain interventions have on children’s education. And it could provide the basis for richer analyses, which, in turn, could help identify areas of need and suggest new approaches to addressing them. While the concept of integrating data was fairly straightforward, finding a way to do so was anything but. Several obstacles stood in the way. Chief among them were state and federal laws with acronyms such as FERPA and HIPAA whose purpose is to protect the confidentiality of personal education and health information respectively. The laws created a web of restrictions that made sharing the data they were enacted to protect a daunting legal challenge, even for those with benevolent reasons for using it. As a consequence, the idea of sharing school and human services data in Allegheny County remained little more than a concept full of possibility —until October 21, 2009. On that evening, the Pittsburgh Public Schools Board of Directors approved a memorandum of understanding between the city school district and county Department of Human Services to integrate student and human services data. Two months later, it was made official with the signatures of county and school officials, ending a year-long process during which they worked with staff and community leaders to overcome numerous challenges and create a data sharing agreement that was the first of its kind in the nation.”

Institute for Higher Education Policy. (2015). How to build successful community data collaborations. Washington, DC: Author. Retrieved from http://www.ihep.org/guidebook-data-chapter-two

From the description: “The Data Quality Campaign (DQC) and StriveTogether have identified several promising practices within communities that have been successful in sharing actionable data. This guidebook includes a joint animation produced by DQC, StriveTogether, and IHEP that outlines these practices. It emphasizes leadership buy-in, user training, and a thorough understanding of the data systems that already exist in order to better integrate and house them. In addition, the importance of protecting student data cannot be overstated. Our animation and other resources in this guidebook help communities understand how the Family Educational Rights and Privacy Act (FERPA) is a roadmap to safely sharing data that will be used to benefit students.

A vital tool in data governance and data sharing is a community data-sharing agreement. This formal agreement clearly outlines what information each partner will exchange and be able to access; its development takes a significant amount of time, coordination, communication, and commitment. As students of all ages navigate through schools, after-school programs, and other community-based services, the ability to track individuals across service providers enables communities to identify important information: leaks in the pipeline, successful interventions that herald more positive outcomes, interventions that need to change to produce better results, and inefficient processes that lead to unmet needs or duplicative efforts. Data about the trends and experiences among underserved students in the community—whether they are still moving through high school or are adults returning to college—allow community partners to engage more deeply to seek out more information and develop new ideas. When communities are able to access and organize these data, they often discover opportunities to realign resources, increase efficiency, and spend time and money more wisely in supporting students. In addition to the data-sharing animation that outlines tips for successful community data collaborations, this section of our guidebook also features an interview with leaders in Providence, R.I. on a service and data-sharing agreement that has recently been put in place between Providence Public Schools and a collaborative of youth-serving organizations. These relationships enable stakeholders to share a new online case management system to better match students with the supports they need. Finally, this chapter ends with a list of additional resources where you can find more information on successful data-sharing agreements and data governance.”

Kingsley, C. (2012). Building management information systems to coordinate citywide afterschool programs: A toolkit for cities. Washington, DC: National League of Cities (NLC). Retrieved from https://eric.ed.gov/?id=ED537006

From the abstract: “City-led efforts to build coordinated systems of afterschool programming are an important strategy for improving the health, safety and academic preparedness of children and youth. Over the past decade, municipal leaders, foundations, major nonprofit intermediaries, and school and community-based providers have increasingly come together to expand the number of high-quality programs available, increase youth participation, and improve outcomes for young people. Yet even cities with strong leadership and effective coordinating entities are often challenged by the lack of reliable information to answer basic questions about the scope and impact of after-school programs in their communities. The decision to build or enhance a management information system (MIS) raises its own set of tough questions about what information to collect and how to use it; how to negotiate data sharing agreements without violating privacy laws; how to think about the difference between evaluating youth outcomes and measuring program quality; and whether to build or buy the technology backbone that will support the data needs of policymakers, service providers, program managers, and researchers. The National League of Cities (NLC), through its Institute for Youth, Education and Families, produced this report to help city leaders, senior municipal staff and their local partners answer those questions as they work to strengthen and coordinate services for youth and families, particularly for those cities building comprehensive afterschool systems.”

McLaughlin, M., & London, R. A. (2013). From data to action: A community approach to improving youth outcomes. Cambridge, MA: Harvard Education Press. Book description retrieved from https://eric.ed.gov/?id=ED568849

From the description: “This book is a welcome guide for educators, civic leaders, and researchers looking for ways to leverage data to identify the most effective policies, interventions, and use of resources for their communities. In the current era of reform, much has been made of the fact that there are many influences that shape children beyond the walls of the schoolhouse. Powerful data ‘warehouses’ have been built to track children and interventions within school bureaucracies and in other social service sectors. Yet these data systems are rarely linked to provide a holistic view of how individual children are faring both in and out of school and which interventions—or combinations thereof—are most promising. Privacy laws and institutional traditions have made such collaborations difficult, if not impossible. Until now. The Youth Data Archive, based at the John W. Gardner Center for Youth and Their Communities at Stanford University, is an effort to blaze a new path to the productive use of cross-agency data now employed by researchers, school officials, and service providers in San Francisco, San Mateo, Alameda, and Santa Clara counties. Editors Milbrey McLaughlin and Rebecca London, leaders of the Youth Data Archive, bring together participants who describe the initiative and its challenges and successes. The participants also give detailed background on how the archive was built and how it has led to improvements in services, particularly for children at risk. This book is a welcome guide for educators, civic leaders, and researchers looking for ways to leverage data to identify the most effective policies, interventions, and use of resources for their communities.”

Rittenhouse, D. R., & Shortell, S. M. (2016). Accountable communities for health: Data-sharing toolkit. Sacramento, CA: California Health and Human Services Agency (CHHS). Retrieved from http://www.chhs.ca.gov/InnovationPlan/CHOIR_dec2016_revised.pdf

From the introduction: “Myriad resources exist to assist with various aspects of the data-sharing (e.g., setting community priorities, building relationships across sectors, establishing data use agreements, and related functions). Our goal is to help the reader identify the needs within their own community and to point them in the direction of relevant resources. Different communities will be drawn to different resources depending on their own experiences and challenges. And most will find it necessary to obtain expert consultation at some point in the process. Community-based examples are presented to exemplify different communities’ success with various aspects of data-sharing. Though there is no ‘one size fits all’ for many obstacles, we encourage communities to reach out to each other to share resources, support, and best practices throughout their journey towards shared data integration. All of the resources we have compiled are current as of December 2016. The work of sharing data across sectors to improve health is challenging and requires an investment of time and resources. Even the lowest-tech options of meeting in a room together and sharing static printouts can be challenging if participants from different sectors are mistrustful of one another, or are using different terminology. Technology can solve some problems, while creating others; and the need for human interaction around data-sharing never goes completely away. This interactive toolkit is designed to point the reader toward a variety of resources that will help with this very important but challenging work.”

University of Pittsburgh Office of Child Development. (2011). Framework for collaboration: Integrating school and human services data in Pittsburgh. Pittsburgh, PA: Author. Retrieved from http://files.eric.ed.gov/fulltext/ED573994.pdf

From the abstract: “This Special Report discusses how the Allegheny County Department of Human Services and Pittsburgh Public Schools took a major step toward closing a knowledge gap that prevents schools and human service agencies around the country from developing a deeper understanding of the children in their systems and collaborating on more effective, better targeted strategies for improving children’s academic performance and overall well-being. After more than a year of research and negotiation, county human services and city public school officials reached a memorandum of understanding that enables them to integrate previously segregated data on students enrolled in the city’s public schools. Integrating data on issues ranging from student achievement and attendance to housing, child welfare, and mental health services offers several potential advantages. Finding a way to integrate data was a challenge that had deterred previous attempts to negotiate an agreement. Among the major obstacles were state and federal laws that protect the confidentiality of personal education and health information whose web of restrictions made sharing data a daunting legal challenge. The memorandum of understanding (MOU) provides the framework for integrating school district and Department of Human Services (DHS) data, including confidentiality provisions, responsibilities of the parties, and the type of information that can be shared and for what purposes. A key provision of the agreement authorizes the use of the data for conducting an ‘action research’ project, a problem-solving process in which DHS and the school district work toward improving the way they address certain issues involving students of mutual interest.”

Wright, T., Zimmerman, J. B., & Knott, R. (2013). At the intersection: Connecting health and education data in school-based health centers. Lansing, MI: School-Community Health Alliance of Michigan. Retrieved from https://schoolhealthteams.aap.org/uploads/ckeditor/files/At-the-Intersection_Connecting-Health-and-Education-Data-in-SBHC.pdf

From the introduction: “This report captures the experiences of Cincinnati, East Baton Rouge, Miami-Dade County, and Seattle in linking complex data sets generated independently by school-based health centers and schools. The communities are at different points in a continuum of linking SBHC and educational data. Their experiences are captured in the following case studies, which were organized to answer several questions. Why did they link or attempt to link their data? What data have been linked? How are they linking these data? And how has the linked data been used? The report also captures some of the challenges they faced and lessons learned along the way. The intent of this report is to provide information that can serve as a guide for other communities in their attempts to connect health and educational information of their students. As we all strive to better serve our communities and meet the enormous needs of the over one million children and adolescents who use SBHCs, it is clear that analyzing and documenting SBHCs’ impact on academic success requires data that includes students’ health and educational needs and outcomes. May the stories that follow inspire you to build the base of information in your own communities.”

Additional Organizations to Consult

All In: Data for Community Health – http://www.allindata.org

From the website: “All In: Data for Community Health is a nationwide learning collaborative that helps communities build capacity to address the social determinants of health through multi-sector data sharing collaborations. All In was founded by two national initiatives, Data Across Sectors for Health (DASH) and the Community Health Peer Learning (CHP) Program, who joined forces to coordinate formal and informal technical assistance for communities, foster dialogue across sites, and cultivate peer-to-peer learning activities for those tackling common challenges or with similar population improvement goals.”

Data Across Sectors for Health (DASH) – http://dashconnect.org

From the website: “As part of its multi-sector data and information system focus, the Robert Wood Johnson Foundation launched Data Across Sectors for Health (DASH). DASH aims to identify barriers, opportunities, promising practices and indicators of progress for multi-sector collaborations to connect information systems and share data for community health improvement.

Connected information systems and access to integrated data from different sectors can improve communities’ capacity to plan, monitor, innovate, and respond for health improvement. DASH works to develop strong cooperation and connections between public health, health care, human services, and other sectors that contribute to building a Culture of Health that will enable all Americans to live longer, healthier and more productive lives. DASH will support collaborations that seek to improve the health of their communities, promote health equity and contribute to a Culture of Health by strengthening information sharing, engaging additional sectors and building sustainable capacity. DASH aims to create a body of knowledge and advance this emerging field by identifying and sharing opportunities, barriers, lessons learned, promising practices and indicators of progress for sharing data and information across and beyond traditional health sectors.”

Stanford University, John W. Gardner Center for Youth and Their Communities, Youth Data Archive – https://gardnercenter.stanford.edu/youth-sector-research

From the website: “The YDA is a cross-agency, integrated longitudinal data system containing the contributed data that public institutions and nonprofit agencies collect on young people who participate in their programs, and represents the broad community contexts young people experience. Instead of viewing youth within individual sector-specific contexts such as school or out-of-school programs, the YDA offers the Gardner Center’s research practice partnerships a comprehensive view of the opportunities and resources available to the community’s youth, enabling them to see where youth-serving investments are mutually supportive, where they overlap, and where the gaps are.”

William T. Grant Foundation, Research Practice Partnerships / Developing Data Sharing Agreements – http://rpp.wtgrantfoundation.org/developing-data-sharing-agreements

From the website: “Data-sharing agreements are central for partnerships known as Research Alliances. Trust is central for developing agreements about how and with whom data will be shared, data security, and communicating about findings.”

REL West note: This website includes work samples and other resources.

Method

Keywords and Search Strings

The following keywords and search strings were used to search the reference databases and other sources:

(“Data sharing agreements”) AND (“K–12” OR schools OR “school-based health centers” OR “health care systems”)

Databases and Resources

We searched ERIC for relevant resources. ERIC is a free online library of over 1.6 million citations of education research sponsored by the Institute of Education Sciences. Additionally, we searched Google Scholar and PsychInfo.

Reference Search and Selection Criteria

When we were searching and reviewing resources, we considered the following criteria:

Date of the Publication: References and resources published for the last 15 years, from 2002 to present, were included in the search and review.
Search Priorities of Reference Sources: Search priority is given to study reports, briefs, and other documents that are published and/or reviewed by IES and other federal or federally funded organizations and academic databases, including ERIC, EBSCO databases, JSTOR database, PsychInfo, PsychArticle, and Google Scholar.
Methodology: Following methodological priorities/considerations were given in the review and selection of the references: (a) study types – randomized controlled trials, quasi-experiments, surveys, descriptive data analyses, literature reviews, policy briefs, etc., generally in this order; (b) target population, samples (representativeness of the target population, sample size, volunteered or randomly selected, etc.), study duration, etc.; and (c) limitations, generalizability of the findings and conclusions, etc.

This memorandum is one in a series of quick-turnaround responses to specific questions posed by educational stakeholders in the West Region (Arizona, California, Nevada, Utah), which is served by the Regional Educational Laboratory West at WestEd. This memorandum was prepared by REL West under a contract with the U.S. Department of Education’s Institute of Education Sciences (IES), Contract ED-IES-17-C-00014524, administered by WestEd. Its content does not necessarily reflect the views or policies of IES or the U.S. Department of Education nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.