1. Constructing the Next Generation of Integrated Data Systems
Ramesh Raghavan
PARTNERING for CHANGE

2. Disclosures
Dr. Ramesh Raghavan has no relevant financial relationships or conflicts of interest to disclose

3. - Andreas Weigend, former chief scientist, Amazon
“Data is the new oil.”
- Andreas Weigend, former chief scientist, Amazon

4. What do we mean by data integration?
Physically bring data together in one place (e.g., Extract, Transform, Load – ETL solutions)
Physically copy data across various locations (e.g., Integration Platform as a Service)
Formerly, Enterprise Application Integration or Enterprise Data Integration
Data repositories (e.g., common data storage solutions or “warehouses”)
Data virtualization (e.g., “screen scraping” or dashboarding)
Use a global schema (what a user needs) +
A local schema (the sources from which data are extracted) +
A mapping approach

5. Overview Why integrate data?
Selected high performance data integrations
Common elements and challenges in data integration

6. Overview Why integrate data?
Selected high performance data integrations
Common elements and challenges in data integration

8. Social Determinants are Key to Health Outcomes

9. DeBord DG, Carreón T, Lentz TJ, Middendorf PJ, Hoover MD, Schulte PA
DeBord DG, Carreón T, Lentz TJ, Middendorf PJ, Hoover MD, Schulte PA. Use of the “Exposome” in the Practice of
Epidemiology: A Primer on -Omic Technologies. American journal of epidemiology. 2016;184(4):

12. Health Plans Have to Report on These Determinants
Contractors’ Obligations
Develop relationships with local organizations to implement social determinants interventions (housing support, nutrition classes, exercise equipment)
Collaborate on community needs assessments
Develop community resource directory
Evaluate members’ health related social needs
Utilize data to reduce health disparities
Share information (health records) with community organizations
Evaluation to measure success of social determinants of health interventions
Bonus payments for addressing social determinants of health
NASHP analysis of accountable health initiatives in 12 states (CA, CO, CT, DE, MA, MI, MN, NY,
OR, RI, VT, and WA) that emphasize population health in their models. 

13. Overview Why integrate data?
Selected high performance data integrations
Common elements and challenges in data integration

14. Geisinger Health’s Unified Data Architecture (2017 onwards)
Shift from large data to big data
Create a Hadoop-based platform
Build source ingestion pipelines
Some data elements are in the enterprise data warehouse (EDW)
Some are in free text – analyzed using natural language processing
“We are on our way to proving that we can replace the EDW.” Joseph Scopelliti, quoted in Healthcare Informatics (

16. Comprehensive Child Welfare Information System
45 CFR (e): Addition of new data exchanges
Courts
Education
Medicaid claims
(f): Establish common data exchange standards
As of 8/1/2018, 46 states, DC, and PR have declared as CCWIS

17. NJ’s integrated Population Health Data (iPHD)

18. High-Touch Data Systems
“Alberta and other Canadian provinces function as public health systems. …They are already collecting patient reported outcomes data. It’s more than just collecting the data that exists. It’s acknowledging the data you need but does not exist, and then building a strategic roadmap to fill the gaps.” (Dale Sanders)
Such data includes factors like income, language, patient-activation measures, and all the lifestyle behaviors that have an impact on a person’s health such as air and water quality, sexual behaviors, diet, alcohol use, smoking, diet, housing and access to transportation.

19. Overview Why integrate data?
Selected high performance data integrations
Common elements and challenges in data integration

20. What Does the Next Generation of Integrated Data Systems Look Like?
“Big” data (not large data) Require novel analytics Expand prediction science Integrate social + geospatial ecology Instantiate “post-privacy” world Shift from sciences of outcomes (e.g., medicine), to sciences of populations (e.g., public health), to sciences of contexts (e.g., social work)

21. Our Future Challenges
Technical Common ontologies Source identification Source ingestion Sunk costs in older data systems Data quality (=completeness x validity)
Ethical Personal privacy and consent Data control/fair use Use of conditional predictions

22. Whether or not it draws on new scientific research, technology is a branch of moral philosophy, not of science.
Paul Goodman, New Reformation

23. Ramesh Raghavan ramesh.raghavan@rutgers.edu 848-932-5337
Thank You
Ramesh Raghavan
PARTNERING for CHANGE