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Using Data Visualization to Make Routine Health Information Meaningful

Information design is both a technical skill and an art form. To design great visualizations requires a diverse range of skill sets and a keen ability to understand the decisions to be made, the data available, the tools and platforms available for visualization design, and how to apply design best practices to create effective visualizations that communicate clearly. Even the most robust routine health information systems face challenges around how to visualize data in a way that facilitates decision-making by key stakeholders.

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Using Data Visualization to Make Routine Health Information Meaningful

  1. 1. Routine Health Information Network ​ Using Data Visualization to Make Routine Health Information Meaningful Amanda Makulec John Snow Inc. With forum co-moderators Michael Edwards John Snow Inc. Tiana Jaramillo University Research Co, LLC
  2. 2. The Routine Health Information Network (RHINO) connects people around the world who believe we can promote better health through the use of information produced by high quality, sustainable routine health information systems.
  3. 3. For decades, health data has been captured in dense ledgers & reports. Photo credit: Robin Hammond
  4. 4. Handwritten tables and wall charts were (and still are) common monitoring tools.
  5. 5. Today, many systems have gone digital. Photo credit: Robin Hammond
  6. 6. Digital HIS expands the opportunities to develop dashboards and other visualizations of routine data.
  7. 7. A brief history of data visualization
  8. 8. William Playfair 1786: line graph and bar chart of economic data 1786
  9. 9. John Snow 1854: Mapping deaths from a Cholera outbreak in central London 1854
  10. 10. Florence Nightingale 1858: Polar area diagram 1858
  11. 11. Minard 1869: Sankey Diagram (later named) 1869
  12. 12. Decision Support Systems 1998
  13. 13. Decision Support Systems 2000
  14. 14. 2017
  15. 15. What do we mean by data visualization?
  16. 16. What is data visualization? A way of visually conveying information – often quantitative in nature – in an accurate, compelling format.
  17. 17. What is data visualization? A way of visually conveying information – often quantitative in nature – in an accurate, compelling format. Usually makes relationships more apparent (e.g. by clustering, color coding and by putting items in scale).
  18. 18. What is data visualization? A way of visually conveying information – often quantitative in nature – in an accurate, compelling format. Usually makes relationships more apparent (e.g. by clustering, color coding and by putting items in scale). Can be static or interactive.
  19. 19. conceptual data driven declarative exploratory idea illustration everyday data viz idea generation visual discovery Matrix credit: Harvard Business Review’s Good Charts
  20. 20. In RHIS, we talk primarily about dashboards, but shouldn’t ignore other forms of visualization.
  21. 21. Who creates compelling data visualizations?
  22. 22. HIS / M&E Developers Program Staff Designers Inspired by the diagram from “Building Successful Data Teams” https://policyviz.com/2017/03/09/building-successful-data-teams/
  23. 23. How can we create user-centered dashboards and visualization tools?
  24. 24. Data Users Design Test Problem
  25. 25. Data Users Design Test Problem
  26. 26. Focus on the BIG QUESTIONS.
  27. 27. Ben Shneiderman’s Information Seeking Mantra Overview first. Zoom and filter. Then details on demand. Ben Shneiderman, The Eyes Have It; A task by Data Type Taxonomy for Information Visualizations. In Proceedings o the IEEESymposium on Visual Languages, pages 336-343, Washington IEEE Computer Society Press, 1996
  28. 28. Data Users Design Test Problem
  29. 29. Invest (significant) time in understanding and exploring your users’ needs.
  30. 30. Analytical ability Job function Education Programmatic knowledge Access to tools Motivations to use data Pain points
  31. 31. User personas are a tool we can use to understand who the different users of a dashboard could be. Sample persona from User Personas https://www.pinterest.com/tovissy/user-personas-ux-sd-cx/
  32. 32. Data Users Design Test Problem
  33. 33. Map your data flow
  34. 34. Unique identifiers and demographic data allow for filters and dissaggregations.
  35. 35. Ensure the right users have access to the right level of visualization.
  36. 36. Data Users Design Test Problem
  37. 37. Design compelling, useful visualizations that provide insight. Image from Gapminder.com
  38. 38. Interesting Function Form Integrity David McCandless, 2012 1. Function: they let you see trends and patterns clearly. 2. Form: they are visually appealing and well structured to attract readers and hold their attention. 3. Integrity: they portray the data accurately and honestly. 4. Interesting: they are relevant and meaningful, or reveal new information.
  39. 39. Image from HubSpot + Visage Data Visualization 101 Guide Pick the right chart for your purpose. Trend over time? Comparison? Distribution?
  40. 40. decluttered design
  41. 41. Simple big numbers provide a quick reference of an outbreak
  42. 42. 0 10 20 30 40 50 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8 Facility 4 showed the highest quality of care. Despite scoring highest, its overall score was below 50%, indicating there is work to be done to improve quality of care across facilities. Decluttered chart eliminates the “non- data ink” where possible to focus on the data story.
  43. 43. color strategically
  44. 44. Color used thematically for a set of charts representing data on one topic.
  45. 45. Alert bar features red for indicators performing poorly where action is required.
  46. 46. Promote accessibility by avoiding red-green maps and charts that can be challenging for the colorblind. Dashboard from Tableau Public, designed by Data Ink https://public.tableau.com/en-us/s/gallery/changing-diseases
  47. 47. purposeful title
  48. 48. Charts are titled with the question they aim to answer. Visualization from Tableau Public Gallery https://public.tableau.com/en-us/s/gallery/malaria-africa
  49. 49. Charts are titled with the data presented.
  50. 50. When designing, try sketching first to develop a rough concept.
  51. 51. The visualization toolbox is packed with options.
  52. 52. Remember to keep your user front-of-mind when you pick your visualization platform.
  53. 53. Data Users Design Test Problem
  54. 54. Done well, visualizations promote the use of data for decisions. Photo credit: Robin Hammond
  55. 55. Michael Edwards, PhD, MPH Biostatistician & Senior HIS Advisor John Snow Inc. Tiana Jaramillo Information Systems and M&E Specialist University Research Council, LLC Amanda Makulec, MPH Visual Analytics Advisor John Snow Inc. Meet your Moderators
  56. 56. Now it’s your turn. Join us on the Forum to share your examples, experiences, challenges, and data visualization tricks.
  57. 57. RHINOnet.org @RHISNetwork
  58. 58. 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