1. technology and computing
2. software
3. net conferencing

Collaborative Data Partnerships: The Promise and Pitfalls of Big Data in the Human Services
Terry V. Shaw, MSW, MPH, PhD
Jill Farrell, PhD
University of Maryland, School of Social Work Health
Mental Health
Education
Juvenile Services
Child Welfare
The Problem
Health
Mental Health
Education
Juvenile Services
Child Welfare
Connections to Allow for the Sharing of Programmatically Necessary Information Between Agencies • The information in this presentation comes from a white paper that will be released by May, 2015.
Road Map
•
•
•
•
•
•
•
•
History of Data
Legislative Concerns
What is Big Data
Power of Data
Collaborative Structures
Service Silos – Some Sharing – MD LINKs
Utilization of External Structure – Our Kids
Integrated System – Montgomery County, MD History of Data • 1980’s widespread use of computers and integration into public services
• 1993 – Federal government began providing funding for states to develop SACWIS
• Technology is not the limiting factor at this point
– Computer systems are more than adequate as far as speed, processing power, and connectivity.
– Limiting factors at this point are resources (time and funding), data security, and legislative concerns
Legislative Concerns
• Health Insurance Portability and Accountability Act (HIPAA) • Family Educational Rights and Privacy Act (FERPA)
• Child Abuse Prevention and Treatment Act (CAPTA)
• Juvenile Justice and Delinquency Prevention Act (JJDPA)
What is Big Data?
• Big Data ‐ at the most basic level it’s about distilling meaning from an incredibly large volume of information generated across a number of varied sources. • 3 Traditional V’s of Big Data (Volume, Velocity, and Variety).
• Recently a fourth V – Veracity was suggested.
• An additional V needs to be added to the discussion ‐ Value
Source: Soubra, Diya. The 3Vs that define Big Data
Data Science Central: The Online Resource for Big Data Practitioners
Power of Data • BIG DATA vs. Big Data
• Opportunities for cross‐system collaboration
• Examples of successful data collaboratives
– Washington State
– South Carolina
– Washington University ‐ Missouri
Power of Data • What is Big Data
• BIG DATA vs. Big Data
• Opportunities for cross‐system collaboration
• Examples of successful data collaboratives
– Washington State
– South Carolina
– Washington University ‐ Missouri
Power of Data • What is Big Data
• BIG DATA vs. Big Data
• Opportunities for cross‐system collaboration
• Examples of successful data collaboratives
– Washington State
– South Carolina
– Washington University ‐ Missouri
Collaborative/Cooperative Structures
Service Silos – Some Sharing
• Primarily siloed structure
• Some cross‐system sharing and possible integration
– Agency‐to‐agency sharing
– University or third party partnerships
– Can be legislated or agreed upon through MOUs
Data Partnerships at UMB/SSW
• Family Welfare Research and Training Group (DHR/FIA) • Child Welfare Accountability (DHR/SSA)
• Institute for Innovation and Implementation (DJS, GOC, DHMH, MSDE)
• LINKs (DHR, DJS)
• Maryland Longitudinal Data System Center (MSDE, MHEC, DLLR)
Multi-Agency Data Collaborative at the University of Maryland
(LINKs - Linking Information to eNhance Knowledge)
The LINKs Data Collaborative at the University of Maryland facilitates comprehensive, data‐driven, evidence‐based decision making in Maryland through the use of a linked data system. • Provide an understanding of the true incidence/prevalence of social service participation through the ability to un‐duplicate at the person level across multiple agencies.
• Strategically identify trends related to the needs of children, youth, and families across systems;
• Evaluate key indicators and outcomes of aggregate well‐being for children, youth, and families; • Evaluate the impact of policy changes, practice changes, or fiscal investments; and
• Provide information to multiple stakeholders on the efficiency and effectiveness of public services in Maryland.
Necessary for Success…
• Partner agencies value independent research and evaluation
• History of trust and mutual respect between state and University
• Data is being shared with the University
• University is value‐added to agency capacity in data reporting/analysis needs
• University is an equal partner with the state agencies in developing research agenda
Where We Currently Stand…
 Provide cross‐over reports to our partner agencies highlighting the number of children served at any point by either agency.
 Completed an examination of dually‐involved girls in Baltimore County.
 Examined cumulative (unduplicated) contact with partner agencies over time.
 Facilitating the evaluation of Maryland’s Birth Match program by the Center for Disease Control (Birth Match is a collaborative project between DHR and Vital Records to identify at risk infants in order to provide services as needed).
 Examining movement into DJS as a type of system reentry for children who reunified from child welfare.
 Developing a paper focused on dually‐involved girls in Maryland.
 Investigating the role of out‐of‐home placement factors for child welfare youth on juvenile justice system entry.
Utilization of External Structure
• Primarily siloed structure
• Use an additional third‐party structure to coordinate and provide access to data
– Can be State or government centered
– University or third party partnerships
– Can be legislated or agreed upon through MOUs
“Follow My Child” – Our Kids
• Developed an electronic passport focused on serving children in the Child Welfare System.
• This electronic passport utilizes a data warehouse maintained by an external group (Our Kids of Miami‐Dade/Monroe) with relevant information from various agencies to provide required information to case workers and foster parents on children they serve.
• Contact: www.ourkids.us
Integrated System
• Data sharing occurs through a consistent infrastructure and network
• Uniform means of identifying individuals across service‐systems
• Means to identify all pertinent information about a client to enhance access
Montgomery County, Maryland | Department of Health and Human Services
October 7, 2013 Integration and Interoperability in a Health and Human Services Enterprise
26
How is DHHS Organized?
Moving towards single client record supported by an interoperable database
One Director
Centralized Administrative Functions
Uniform intake form to identify all service needs
Designated entire HHS entity as HIPAA covered –
including social service and income support programs
27
We adopted four guiding principles to serve as touchstones as we move forward.
Because we want to provide seamless, consistent services to our clients across HHS, we seek to define and implement common processing, approaches and methods throughout HHS
Because we want to maintain and upgrade our systems over time, we will leverage the existing functionality of the software as fully as possible and minimize customization
Because we want to treat clients in holistic, integrated manner, we will share information about our clients and their treatment within HHS, unless prohibited by law
We will never let the perfect be the enemy of the good
28
No Wrong Door in the Future.
29
• Seamless customer experience integrated in all 50 sites (27 program sites and 23 clinic sites)
• Key to the experience will be:
– Access to all DHHS Programs
– Shared information and data
– Customer telling their story once
An Aspect of Interoperability.
30
Sharing of Information
•
•
•
•
•
Policy and Practice
Business Process
Need to Know
Role‐Based Access
Balance between Interoperability/Data‐
sharing and Guarding against Breaches
Thank you – Contact Information
Terry V. Shaw ‐ tshaw@ssw.umaryland.edu
‐ 410.706.3811
Jill Farrell
‐ jfarrell@ssw.umaryland.edu
Sarah Nadiv
‐ snadiv@ssw.umaryland.edu
Karen Kolivoski ‐ kkolivoski@ssw.umaryland.edu