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Data Warehousing: Bridging Islands of Health Information Systems
- 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
- 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.
- 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