Presented by Sam Wambugu and Manish Kumar at a January 2016 webinar.

1. Data Warehousing
Bridging Islands of Health
Information Systems
Sam Wambugu, MPH, PMP
Manish Kumar, MPH, MS
MEASURE Evaluation

2. Global, five-year, USAID-funded
cooperative agreement
Strategic objective:
To strengthen health information
systems–the capacity to gather,
interpret, and use data–so countries
can make better decisions and
sustain good health outcomes over
time.
Project overview
Photo by Jane Silcock/USAID

3. • Improved country capacity to manage health
information systems, resources, and staff
• Strengthened collection, analysis, and use of routine
health data
• Methods, tools, and approaches improved and applied
to address health information challenges and gaps
• Increased capacity for rigorous evaluation
Project framework

4. Global footprint
In some countries,
work is core-funded;
in some, it is field-
funded; and in many, it
is both core- and field-
funded.
We also receive PMI
funds in a number of
countries.

5. Presentation objectives
• Outline the current HIS situation, challenges, and opportunities
• Demonstrate the need for national health data warehouses
• Discuss data warehouse tools, models, and platforms
• Share data warehouse architectures from countries
• Provide data warehousing resources

6. HIS situation in LMICs
• Redundancy in data collection
• Heavy burden on health workers
• Inadequate analysis and use of data
• Weak governance and leadership
• Lack of health data standards
• Uncoordinated and unstandardized use of ICT
• Fragmented information system
• Lack of data integration

7. What is a data warehouse?
• Data repositories, for the storage of essential data and indicators from multiple
data sources
• A subject-oriented, integrated, nonvolatile, and time-variant collection of data in
support of management’s decisions (Inmon, W., 2005)
• A decision support database that is maintained separately from the organization’s
operational database
• Supports information processing, by providing a solid platform of consolidated,
historical data for analysis
• It contains information that has been culled from operational systems
• Is typically read-only, with the data organized according to business requirements
• Types of health data warehouses: aggregate or clinical

8. Why data warehousing?

9. Health information systems

10. HMN architecture for a national data
warehouse
,

11. Promising opportunities
• Use of and investment in ICTs for data collection, transfer, compilation,
analysis, dissemination, and systems interoperability
• Improved internet access
• Improved data availability and quality over time
• Improved human and technical capacity
• Explosion of data matched by growing data demands
By 2011, 93 of 112 health
systems in countries
surveyed by the World Health
Organization (WHO) had
already adopted some form
of an eHealth or mHealth
(mobile phone-based)
approach.

12. Data warehouse vs. high-performing HIS
• Maintains data history, even if source transaction systems do not
• Presents the organization's information consistently
• Ensures that data are trustworthy
• Provides data to monitor trends in health outcomes and services
• Provides evidence for what works
• Ensures the coordination and equity of health services

13. Establishing the need for a data warehouse
• Stakeholder leadership team
• Determine the goal of the data warehouse
• Define data sources for the data warehouse
• Define data quality processes
• Data warehouse architecture
• Granularity of the data warehouse
• Users and stakeholders
• Data security, privacy, and confidentiality

14. Important considerations
• Software and hardware based on international
standards
• Open-source vs. proprietary
• Data warehouse hosting: on ground or in cloud
• Capacity: human and institutional capacity and
financial resources
• Governance structure
• Infrastructure: physical and ICT
• Maintenance

15. Data warehouse tools
• Traditional DBMSes, mostly relational but not exclusively, from such
vendors as IBM, Microsoft, Oracle, and SAP
• Community developed/open-source data warehouse platforms–DHIS 2
• Online analytical processing (OLAP) and reporting tools
• Business intelligence tools and dashboards
• Extract, transform, and load (ETL) tools: e.g., Websphere DataStage
• Data integration software
• Cloud data warehouse offerings

16. Data warehouse platform: DHIS 2

17. Data warehouse models: centralized
Centralized model: Also
called the warehouse
model, data are collected
from local sources
(facilities/districts) but
stored in a central
repository. All information
exchanges are routed
through the central
repository.

18. Data warehouse models: federated
Federated model:
Also called the
decentralized model,
subsystems have control of
the data.
The individual subsystems
are linked and exchange
information.
Mostly works well for a
clinical data warehouse.

19. Data warehouse models: hybrid
Hybrid model: In this mix of
the centralized and federated
architectures, data are usually
stored and managed at
organizational or regional/
state/county levels, but
information exchange is
enabled through a central hub.

20. Swaziland’s HIS architecture

21. DATIM

22. Conclusion
• Many LMICs are developing national data warehouses,
using an incremental and adaptive approach
• The data warehousing process requires leadership and
organizational and political support, because it is about technical
solutions
• Identify champions and have them lead the way
• Viable and tested data warehouse solutions for LMICs are
available

23. Data warehousing: resources
• Paris21. Road map for a country-led data revolution. (2015). Paris, France: Organisation for Economic Co-operation
and Development (OECD). Retrieved from
http://datarevolution.paris21.org/sites/default/files/Road_map_for_a_Country_led_Data_Revolution_web.pdf.
• Kossi, E.K., Sæbo, J.I., Titlestad, O.H., Tohouri, R.R., & Braa, J. (2010). Comparing strategies to integrate health
information systems following a data warehouse approach in four countries. Journal of Information Technology for
Development. Retrieved from
http://www.uio.no/studier/emner/matnat/ifi/INF3290/h10/undervisningsmateriale/ComparingStrategiesForHISinteg
ration.pdf.
• Boone, D. & Cloutier, S. (2015). Standards for integration of HIV/AIDS information systems into routine health
information systems. Retrieved from http://www.cpc.unc.edu/measure/resources/publications/ms-15-103.
• MEASURE Evaluation (2016). Defining electronic health technologies and their benefits for global health program
managers: crowdsourcing. Retrieved from http://www.cpc.unc.edu/measure/resources/publications/fs-15-165a.

24. MEASURE Evaluation is funded by the U.S. Agency for International
Development (USAID) under the terms of Cooperative Agreement
AID-OAA-L-14-00004 and implemented by the Carolina Population
Center, University of North Carolina at Chapel Hill, in partnership
with ICF International, John Snow, Inc., Management Sciences for
Health, Palladium, and Tulane University. The views expressed in
this presentation do not necessarily reflect the views of USAID or
the United States government.
www.measureevaluation.org