The building blocks to a data sharing strategy are laid out in this Guide to Data Management, Privacy & Confidentiality, and Predictive Analytics. The three-part Guide, which you will find below, includes state and county use cases, case studies, compendia, principles, and much more. This work was created by the National Collaborative Analytics Committee (NCAC), whose membership includes county health and human service (H/HS) directors; health IT coordinators, analysts, and Chief Information Officers (CIO) of state and county H/HS departments; representatives from research and policy centers; and industry partners. This Guide is the NCAC’s third work product, building on previous work around data analytics and system readiness in H/HS. More information and links the previous work can be found at the bottom of the page.
Human-serving programs (publicly-funded programs that provide people social and economic supports that can improve their quality of life) have been aiming to modernize and improve how their services can most effectively improve outcomes for individuals, children, and families. The complexities of this cross-programmatic and cross-sector system of care have presented obstacles to the human-serving field’s ability to “modernize” and to keep pace with other industries, such as healthcare. Still, even with limited funding, restrictive data sharing requirements, and workforce shortages, public sector health and human service (H/HS) agencies, and the private providers with which they contract, continue to design innovative solutions and to adopt existing business methods to make such improvements.
H/HS organizations are putting a lot of time, effort, and whatever resources they can marshal to maximize individual, programmatic, and population-level data as an asset to reconfigure their operations and measure success. With the increasing demand for accountability, the human-serving field is determining how to best use and share data, particularly sensitive data, to make life easier for people, and to improve the health and well-being of Americans and the organizations that interface with them on a regular basis. [You can read the rest of the Overview here.]
Full Committee Roster - We thank everyone who volunteered their time and expertise to help create this body of work.
This section of the NCAC was charged with (1) the identification of jurisdictions with successful examples of data sharing and management, and (2) the development of practical templates of data governance structure. Here you will find use cases, definitions and graphical representations of various agreements (ex: MOU, MOA, etc.), and a list of barriers to data sharing with proposed mitigation strategies.
This section of the NCAC was charged with identifying or developing practical tools for states/localities to assist in making the legal case for data sharing (and consent). Here you will find a resource compendium of consent agreements and data sharing statutes, and an FAQ and resource collection for data governance.
This section of the NCAC was charged with developing case studies on current and cross-usage by states/localities of predictive analytics. Here you will find an introduction to predictive analytics, and five case studies from states all along the “analytics curve” – presenting their work and lessons learned.
This Guide is composed of "living documents" that may be updated frequently. If you have any comments, questions, or updates to share, please contact Christina Becker at email@example.com.
This Guide is the extension of efforts previously conducted by APHSA’s NCAC. Below you will find information and work products from the past two years of our Committee’s work, and it may also be found online here.
Last Updated: 11/22/17