Data Management Needs Assessment

1. Data ManagementNeeds Assessment Uganda Ministry of Health June 8th, 2007

2. Overview • Introduce Blum Fellows • Goals & Objectives • Background – technology & past efforts • Methods • Findings • Pilot Possibilities • Feedback, Discussion, and Questions

3. Team Introduction Shaffeque Abas: Bachelors, Computer Science, Mbarara University Melissa Ho: Ph.D., Information; MSc CS Simon Morfit: Ph.D., Sociology; MPH Mallory Primm: BA, Human Rights Katrina Robinson: Masters, Social Welfare Admas Zewdie: Masters, Business Administration

4. Guidance • Uganda • Ananias Tumukunde, Presidential Private Secretary for Science & Technology – State House • Harriet Mukunguzi, Executive Director Science and Technology Enterprise Development Organization • Richard Tushemereirwe, Assistant to the Presidential Private Secretary for Science & Technology-State House • UC Berkeley • Kristiana Raube, Haas School of Business • Sandra Dratler, School of Public Health • George Scharffenberger, Executive Director Blum Center for Developing Economies

5. Original Objectives • Assess the need for and feasibility of health data collection using smart phones • improve the timeliness of regional and national data collection • save health worker time by performing verification automatically • provide accurate and timely data for clinical care and policy decision making

6. Revised Project Goals • Identify technological needs • Data collection and reporting • Data analysis and decision making • Communication • Evaluate feasibility of currently available technologies • Determine key stakeholders • Inform future pilot projects

7. Project Timeline Health Centers (Nakaseke District) MOH Makerere U UHIN (Kampala) Mbarara U (Mbarara District) Health Centers (Rakai District)

8. Methods

9. Interviews Completed

10. Interview Content • Current data collection, management and analysis procedures • Strengths and weaknesses • Opportunities for improvement • Existing technology infrastructure

11. Findings - MOH • Substantial compliance with HMIS • Concerns of data quality • Full capacity of electronic format not yet realized • Labor intensive data entry • Problem of migration of trained personnel from the rural to urban areas

12. Findings - Universities • Desire to collaborate with MOH • Exchange data • Share ICT knowledge • Mbarara University • Emphasis on rural needs and research • Research on solar energy technology • Makerere University • Computer science expertise • Distance learning capacity

13. Findings – Rakai District HCs • Data captured on paper and PDA • PDAs: • Used to complete and transmit HMIS forms • Used to receive information • Open to local innovation • Challenges: • Power shortage • Limited points of connectivity to relay data • Previous computer knowledge not required • Commendable training program • UHIN willingness to collaborate

14. Findings – Nakaseke HCs - 1 • HMIS reports • Weekly: often submitted via text message • Monthly: delivered in person • Data flow is unclear between HCs • Compiling reports is a time consuming and error prone process • Ascertaining patient medical histories can be difficult • Little feedback from referrals and data submissions

15. Findings – Nakaseke HCs - 2 • Consistent network coverage, but inconsistent electricity supply • Transportation difficulties – cost, road conditions and lack of vehicles • Some data analysis at lower HC levels • Lack of basic supplies

16. Findings – Current mobile phone use in HCs • Emergency reporting • Submitting weekly HMIS forms • Checking salary and drug order status • Requesting transportation • Clinical consultations

17. How much can data management tools improve healthcare?

18. Pilot Considerations • Hybrid solutions – different technology for different HC levels • Universal HC access to HMIS data • Electronic medical record • Software for automatic data compilation and analysis • Bidirectional data flow

19. MoH computers + broadband computer + smartphone smartphone + pdas smartphone or paper

20. Electronic hand-held device Functions as a mobile phone Provides internet access Has built-in keyboard Additional capabilities: E-mail Word processing and spreadsheets GPS Custom programs can be installed Smart Phone

21. Related Work • CAMPhone: Use of smartphones with bar-coded forms to facilitate microfinance. Tapan Parikh, University of Washington Computer Science. (India) • OpenMRS: Open source web-based electronic medical record software, currently developing mobile phone-based interface. (Kenya, Rwanda, South Africa) • ReACH: Web based system supporting asynchronous remote consultation between doctors and specialists over a variety of networks. Melissa Ho, Rowena Luk, Paul Aoki, University of California, Berkeley. (Ghana) • Smartphone and web-based patient records pilot project. Dartmouth University. (Vietnam) • Uganda Health Information Network: Use of PDAs for dissemination of content and submission of forms. (Uganda) • Simputer: PDA-based data collection for monitoring of TB. (India)

22. Challengesto Implementing ICT • Balancing paper vs digital data recording • Power • Network • Clarity in data flow • Current cost of ICT • Limited computer literacy • Privacy of health information

23. Thank You!