Futures group approach to strengthening a data culture use

2. Futures group approach to strengthening a data culture use Ian Wanyeki Senior technical evaluation manager Africa and the Caribbean iwanyeki@futuresgroup.com

3. Contents • AIDSRelief overview • Context that we work in • Data source and library of solutions used • Practical examples of data use • Lessons learnt

4. AIDSRelief Project AIDSRelief Extended FY2013 Program CDC, subcontractor to Catholic Relief Services Client As part of the President's Emergency Plan for AIDS Relief, this 9-country project provides rapid scale up of anti-retroviral therapy. Using in-country networks and innovative open source technology, . Futures is building a strong, sustainable clinical records and program information system that will be used to collect and track data across the various local partner treatment facilities. Description

5. Who we are Catholic Relief Services (CRS) University of Maryland School of Medicine – Institute of Human Virology (IHV) Futures Group Catholic Medical Mission Board (CMMB) IMA World Health (IMA)

6. Key achievements Successful evidence based scale up in resource poor settings 123,000 active ART; 333,000 in care 185 local partners Local Increased skills in service delivery supported by improved systems Durable viral outcomes Providing a continuum of care through local partners and involving communities Strengthening health systems to provide sustainable chronic care delivery

7. Where Do We Work? AIDSRelief overview 29 7 18 25 185 Local Partner Treatment Facilities (LPTF)+ 112 satellite sites 10 51 18 3 24

8. Who are we? • Futures group has a comprehensive team of: • M&E specialists • HIV/AIDS experts • Clinicians • HMIS experts • Programmers • Project Managers

9. AIDSRelief overview • Futures group provides strategic information capacity building and focuses on: • Improved data demand and information use • Enhancing/implementing patient monitoring and management systems that meet local requirements • Data quality for improved services and clinical outcomes • Using local experts to build capacity at partner sites

10. AIDSRelief Strategic Information • Committed to collecting only information essential for LPTF clinical management and quality improvement • Committed to feeding back information to the LPTF to help improve services

11. Why do we collect data? • To inform clinical and programmatic decision making • To foster good clinical management when used effectively • To make strategic decisions about the need for improved infrastructure, increased personnel, and annual planning • Absolutely necessary for continuity of care • To use data to enable facilities to apply for additional funding • Easier to track outcomes • Adherence levels • Missing clinic appointments • Clinical outcomes – OIs • Treatment failures • Drug forecasting

12. Context LPTF in Nigeria (Akwanga)

13. Before: No organized filing system

14. Before: No dedicated M&E room

15. Before: how do we locate patient files and analyze data?

16. After: Well organized patient filing system

17. Our strategy: To offer a library of tools and solutions built around adaptive management, quality, and sustainability Requirements developed through practical field experience and lessons learned Collaborative approach using local experts throughout the development process Current areas of focus PMM, ART Registers, Data Quality Tools International QualitySolutions

19. Scenery on the way to the site

20. Scenery on the way to the site

21. Hazards while getting to the sites

22. Hazards while getting to the sites: airport security

23. Traveling to Bartica in Guyana

24. Practical examples of data use in the field

25. Caregivers only a short time to spend with each patient. • Our IQSolutions help prepare for the clinical encounter so that it is focused and informative • It provides essential, up to date medical history information necessary to make appropriate clinical decisions?

27. ENROLLMENT TREND ANALYSIS

29. WORK LOAD FORECAST

33. Life Table Analysis Applications Life tables can help compare retention: Across sites Over time Based upon CD4 count at initiation Based upon gender Based upon program intervention at a particular point Others, WHO stage, Distance to clinic, etc

34. Does retention vary across treatment facilities? Is it statistically significant? If it does, what are possible reasons? What can the clinical team do to help sites improve on this? What best practices can be learnt from sites with better retention rates?

36. Does retention vary by Baseline CD4 ? Is it statistically significant? If it does, what are possible reasons? What can the clinical team do to improve on this?

37. The steeper the slope, the faster clients are being lost.

38. Lessons Learnt • Have entire staff support effort • Every one in the clinic should be able to use quality indicators generated by the PMM system. • Until the clinics “owns” the data M&E and data utilization will continue to be perceived as an external requirement • Utilize tools that make your work easier • Validate data before carrying out analysis • Continuous Feedback is essential !!!

39. THANK YOU Questions?