期刊名称：International Journal of Population Data Science
电子版ISSN：2399-4908
出版年度：2018
卷号：3
期号：4
页码：1-1
DOI：10.23889/ijpds.v3i4.734
出版社：Swansea University
摘要：IntroductionSince 2008, our team has studied sites with strong track records of integrating cross-sector data to address complex social problems. We have identified best practices and are supporting 16 sites with individualized technical assistance and participation in a learning community to facilitate data sharing that leads to actionable intelligence. Objectives and ApproachThis presentation will provide an overview of our findings in studying established integrated data systems from across the United States, and a summary of our efforts in supporting the development of new integrated data systems (IDS). Specifically, we will answer: What are the core features of integrated data systems (IDS) needed to support data linkage across government agencies? How can sites develop these core features to facilitate data sharing, and ultimately, actionable intelligence? What are the common roadblocks to successfully sharing data across agency siloes? How can these roadblocks be addressed to create a sustainable data sharing ecosystem? ResultsAgencies collect vast amounts of data, yet struggle to assess program impacts due to data siloes. The ability to link data across agencies to develop potential policy and service improvements is difficult, and at times, impossible. This technical assistance supports a different approach. Our Integrated Data System (IDS) Learning Community initiative is an 18-month engagement for sites selected through a competitive process. As of September 2018, sites will be 11 months into the IDS development process. This presentation will discuss site-based approaches to facilitate data sharing, with an emphasis on governance, foundational agreements, data management and security, and stakeholder driven processes that ensure ethical use of data. This discussion will include progress to date, common challenges and solutions to developing an IDS. Conclusion/ImplicationsWhile there is broad agreement in the value of integrating data across domains, developing the capacity and skills necessary to link administrative data for policy evaluation and research remains an elusive goal. Initial results indicate that an individualized yet collaborative technical assistance approach is successful in developing data integration capacity.
