![Acknowledgements
• Tara Nutley (MEASURE Evaluation, Palladium)
• Amanda Makulec (MEASURE Evaluation, JSI)
• Cal Bruns and Jason Coetzee (Matchboxology)
• Roselyn Kareithi (MEASURE Evaluation – Strategic
Information for South Africa [SIFSA])
• Sono Kusekwa and Jackie Patrick (MEASURE
Evaluation – Tanzania)](https://image.slidesharecdn.com/aphaucdmichellelifinalbr-171109204413/85/Applying-a-User-Centered-Design-Approach-to-Improve-Data-Use-in-Decision-Making-26-320.jpg)
Applying a User-Centered Design Approach to Improve Data Use in Decision Making
- 1. Applying a User-Centered A Design Approach to Improve Data Use in Decision Making Michelle Li MEASURE Evaluation Palladium APHA November 7, 2017
- 2. Presenter Disclosures The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months: No relationships to disclose Michelle Li
- 3. Functions and Goals of a Health System (2000) FUNCTIONS (6 Building Blocks) GOALS/OUTCOMES OF THE SYSTEM Stewardship Commodities Infrastructure Service delivery Financing I N P U T S Health Responsiveness (the way people are treated and the environment) Fairness in financial contribution Quality Coverage Efficiency Source: WHO, 2000. 3 Human resources Information Safety
- 4. Improving Information Systems • Global commitment to accountability • New data resources • Result is more and better data
- 5. More Data = Better Decisions?
- 6. Problem Statement How can we develop interventions that address individual needs and barriers to data use?
- 7. Methods • Location: Two districts in South Africa and Tanzania • Participants: data users and data producers • Immersion interviews • Design workshops: • Understand barriers • Ideate and prototype
- 8. Design Framework Adapted from Standord d.school
- 9. Empathy • How can we put ourselves into the shoes of the data user? • What are our data user’s wants, needs and interests? • What are our data user’s challenges and pain points?
- 10. Empathy – Immersion interviews • Observe and speak to people at their place of work to understand beliefs, attitudes, and behaviors around data use • What do you love most about your job? • Who is the best data user you know? Why are they good at their job? • What is the most important aspect of your work? What role does data play in this?
- 11. Design Framework
- 12. Define – Workshop I Activities
- 13. Insights on Data Use Barriers • Technical • Adequate and timely provision of resources • Low confidence in quality of data collected • Lack of clarity on how data is used • HIS systems are adequate, but not used to full potential. There is no involvement of end users in the design of HIS systems and tools. • Tension between service provision and data tasks, especially at lower levels • Data collection tools are overly complicated and there is a lack of prioritization of data needs
- 14. Insights on Data Use Barriers • Behavioural • Little top-down positive feedback on data • Primary data collectors display lack of commitment and negative attitudes toward data use • Organizational • Value of data for decision making is not well understood – more emphasis on collection • Desire exists for a culture of data use • Lack of clarity, poor communication, and complex SOPs hinder data sharing and use
- 16. Go Getters • LOVE data • Hard workers and proactive in work • Frustrated by environments that accept lower performers • Desire for perfection can push them to work long hours and burn out Hyper- actives • Full of energy • Seek attention constantly • Require very hands-on supervision • Can be creative problem solvers, but requires focused workplan to stay on task Lazy Dodgers • Always “busy” but do not produce quality outputs • Always miss deadlines and targets • Flourish in an environment with no consequences • Full of excuses and avoid responsibilities Personas
- 17. Design Framework
- 18. Ideate – How might we?
- 19. Design Framework
- 20. Prototype
- 21. UGUdoc.com: A digital portal connecting health workers with current and archived data, policy docs, SOPs, and an AskMe bulletin board to receive feedback on specific facility and system challenges What’s Up Health/Social Media: A communication solution to consolidate district/provincial data- related communication in one place to accelerate messaging, and information Prototypes – Improving Communication
- 22. Prototypes – Improving data use at lower levels Data Use Score Cards: Rapid data use assessment score cards targeted to health facilities, to provide feedback and encourage competition for best performer facilities Clinicians on Board: A clinician data sensitization program focused on including data management in clinician KPAs and training for “using data to manage”
- 23. Champion Cascade: Rapidly create a culture of data use by appointing champions across levels; develop a toolkit to develop presentation, facilitation, and supervision styles and share good practice tools through a What’s Up Health communications system. Champions are awarded based on overall HIS performance of their facilities. Prototypes – Promoting Data Use Champions
- 24. Strengths of the UCD Process • Development of context specific solutions • Build ownership with users • Facilitates empathy between data users and data producers • Engaging and fun!
- 25. Lessons Learned • Design workshops are an innovative facilitation tool to engage people to discuss and brainstorm solutions to data use challenges • Agree on an action plan (with resources, timelines, and responsibilities)for prototype testing • Identify highly motivated people (i.e., data use champions) to move prototypes forward • Support participants who want to take up facilitation techniques and activities in their own work
- 26. Acknowledgements • Tara Nutley (MEASURE Evaluation, Palladium) • Amanda Makulec (MEASURE Evaluation, JSI) • Cal Bruns and Jason Coetzee (Matchboxology) • Roselyn Kareithi (MEASURE Evaluation – Strategic Information for South Africa [SIFSA]) • Sono Kusekwa and Jackie Patrick (MEASURE Evaluation – Tanzania)
- 27. This presentation was produced with the support of the United States Agency for International Development (USAID) under the terms of MEASURE Evaluation cooperative agreement AID-OAA-L- 14-00004. MEASURE Evaluation is 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. Views expressed are not necessarily those of USAID or the United States government. www.measureevaluation.org