Data Sharing & Analytics

The Analytics Committee

Analytic Capability Roadmap

In 2014, the Committee released the Analytic Capability Roadmap 1.0 for Human Service Agencies. The Roadmap provides human service agencies with a starting point to define what analytics in human services means. The Roadmap also helps them assess current (analytic) capability and developing an analytic strategy to help meet their organizational objectives and measure outcomes across programs.

On June 2, 2014, the Analytic Committee members reviewed the elements of the Roadmap and provided examples of where state & local jurisdictions are on the Analytic Capability Curve. Slides

Roadmap to Capacity Building

In 2015, the Committee released the 2015 Roadmap to Capacity Building in Analytics, a white paper building upon the Committee's 2014 work. The 2015 Roadmap provides information on: (1) analytical capabilities required for successful analytical efforts, (2) skill sets as well as governance structures and change management processes for such efforts, and (3) practical examples of existing solutions across public and private health and human service sectors.

Guide to Data Management, Privacy & Confidentiality, and Predictive Analytics

In 2017, the Committee created a 3-part guide, the Guide to Data Management, Privacy & Confidentiality, and Predictive Analytics. The building blocks of a data sharing strategy are laid out in this Guide, and it 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.

• Overview: Why Does Analytics Matter in the Health & Human Services Field?
• Full Committee Roster - We thank everyone who volunteered their time and expertise to help create this body of work.

I. Data Management & Proven Data Governance Practices

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.

• Presentation on Data Management & Proven Data Governance Practices
• Inventory of Barriers & Enablers to Data Sharing/Governance (slide 4)
• Executed Agreements (slide 39)
  • MOA for Data Sharing between NJ State Agencies
  • Multi-agency MOU for Commonwealth of Pennsylvania data sharing and linking
  • MOU between APHSA and a State for the National Electronic Interstate Compact Enterprise (NEICE)
  • Data Use and Reciprocal Support Agreement (DURSA)
  • eHHR Enhanced MOU – Virginia
  • Agreement for Electronic Access to Protected Health Information – Washington State
• Use Cases (slide 43)
  • Chapin Hall - Multistate Foster Care Data Archive Web Tool
  • Pennsylvania - Workforce Data Quality Initiative
  • Virginia Department of Social Services Foster Care Outcomes
  • Virginia Department of Social Services TANF Employed Clients
  • Washington - PRISM, a Web-based Clinical Support Application

II. Privacy & Confidentiality Impact on Data Sharing

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.

• Models & Resources for Data Exchange
• Data Governance Practices & FAQ
• Inventory of Statutes Concerning Data Sharing

III. Predictive Analytics & Modeling

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.

• Introduction & Acknowledgement
• What is Predictive Analytics?
• Common Themes Across Case Studies in the Use of Analytics
• Summaries of 5 Case Studies
• Lessons Learned
• Appendix – Complete Case Studies
  1. Maryland: Innovative Integrated Case Management in Health and Human Services: The Montgomery County, MD Experience
  2. Michigan: Coordinating Care in Michigan by Using Analytics and Integrating Behavioral Health, Physical Health and Human Services Data – CareConnect 360
  3. Tennessee: Division of TennCare: Data & Analytics Case Study
  4. Tennessee: Performance-Based Contracting in Tennessee’s Foster Care System
  5. Washington State: Bringing Together Innovative Care and Prescriptive Analytics: The Puget Sound Regional Fire Authority

This Guide is composed of "living documents" that may frequently be updated. If you have any comments, questions, or updates to share, please contact Christina Becker at [email protected].

H/HS Analytics in 2019

In 2019, the Committee created four resources for H/HS agencies.

1. Population Health: A whitepaper, Data Governance: A Critical Component for Strong Analytics to Address Population Health, which strives to assist H/HS agencies to define the value proposition for an increased focus on population health analytics, paying specific attention to the need for strong data governance to realize robust population health analytics.
2. Infrastructure & Operations: A two-page executive summary on the need for data dictionaries in an agency, Why Data Dictionaries Matter.
3. Child Welfare: A two-part webinar series on prediction, learning, and prevention in child welfare.
1. Part One: What are the considerations & training needed to put evidence to work in child welfare? Speakers from APHSA, Youth Villages, and the National Council on Crime & Delinquency (NCCD) shared opportunities and challenges related to the use of evidence-based decision support tools, including predictive analytics, in child welfare.
2. Part Two: How do you predict abuse and neglect? Erin Dalton from Allegheny County's Office of Analytics, Technology and Planning explained how Allegheny County went through evaluation and planning to create its CPS program.
4. Economic Assistance: A use case from WA state on their extensive integrated analytic environment by linking data from dozens of administrative data sources. You can find the use case here. For extensive examples of analytic products derived from the Washington State Integrated Client Database, check here. Finally, you can also check out a report from WA state about The Maternal Well-Being of Washington State’s TANF Population; and you can check out an infographic from Colorado, Evaluating the Effect of Colorado’s Full Child Support Pass Through Policy.

A special thank you to the co-chairs of this Committee, who spent significant time developing all these tools: Connor Norwood (IN), Sean Pearson (NM), Britany Binkowski (New Allies, Youth Villages), and David Mancuso (WA). Thank you to all our state, county, industry, and association partners who took time to exchange ideas and expertise throughout the 2019 Analytics Committee.