Improving the Visualization of Health Data on 2.3 Billion People
By: Tricia Aung
Tags: Design, Global Health, Data Visualization, Data Science, Data

Tricia Aung: Why is data visualization important to DHIS2?

Aprisa Chrysantina and Johan Ivar Sæbø (DHIS2): Visualisation is a big part of the platform. The users can visualise and analyse their data using pivot tables, all kinds of charts from bar, pie, line, to speedometer, and also GIS functionality. The DHIS2 team believes that the end point of having data is to use it (albeit, correctly) and it will not be possible without ability to visualise the data.

DHIS2 Level 1 Data Use Academy training in Indonesia https://community.dhis2.org/t/indonesia-promotes-meaningful-data-use-through-localisation-of-regional-data-use-academy/37380

How has DHIS2 been used in Indonesia?

Indonesia has been implementing DHIS2 to integrate health data from different programs such as HIV, TB, and reproductive, maternal, newborn, and child health. Dashboard implementation has been central to the implementation as it allows health staffs to directly visualise the data they have collected. Less than 2 years after the pilot in the country, DHIS2 has been implemented or at least introduced in 127 districts (24.7% of 514 districts) in Indonesia, and has expanded from 6 pilot programs to 17 different work streams, including pharmaceutical and medical devices, human resources, home care, and COVID-19. The application is mostly used to integrate, visualise, analyse, and report aggregate data across various levels, from facility to district to province, and even national levels.

What inspired your dashboard assessment in Indonesia?

We understood that appropriate and relevant dashboards are not straightforward to make. Users need a certain level of data literacy to create, read, and analyse charts. There was limited evidence in the literature regarding the quality of dashboards created by health staff in the field.

What led to your decision to use Stephen Few’s dashboard design criteria in the study?

When we conducted this study, guidance to create dashboard and data visualisation are often not presented systematically. However, in his book Information Dashboard Design, Few lists common mistakes with dashboard creation, ranging from clutter and inappropriate contextualisation, to outright wrong use of visualisation techniques. We identified that these mistakes could be translated into guiding assessment questions.

Table 3. Frequency of dashboard issues from Chrysantina A, Sæbø JI. Assessing User-Designed Dashboards: A Case for Developing Data Visualization Competency. InInternational Conference on Social Implications of Computers in Developing Countries 2019 May 1 (pp. 448–459). Springer, Cham.

What do you think are the most important findings? Was there anything that surprised you?

Aprisa was (ironically) glad that her suspicion of dashboard quality was proven and that we now have evidence that support that. We were surprised that the frequency of the inappropriate data visualisation usage problem was higher than we expected to be. Unfortunately, although we were involved in some of the trainings where these users created the dashboards, we didn’t investigate the reasons nor the creation process specifically for the purpose of the research. Also, we were surprised that these dashboards and charts were created by health information systems (HIS) consultants or government HIS staff which had either Bachelors or Masters degrees in clinical health/public health or IT. This implied that these advanced educational backgrounds did not guarantee data visualisation literacy.

Table 4. Sample Visualization Problems from Chrysantina A, Sæbø JI. Assessing User-Designed Dashboards: A Case for Developing Data Visualization Competency. InInternational Conference on Social Implications of Computers in Developing Countries 2019 May 1 (pp. 448–459). Springer, Cham.

Did you notice any variation in dashboard quality across levels of the health system (e.g. national vs. facility)?

We didn’t specifically address this question in our study. However the people we mentioned above created dashboards and/or charts in different scopes, whether it was for facilities or at the national level. Through my field visits and discussions, I also found that many of these dashboards were developed as part of DHIS2 training process with trainer/facilitator(s) being around. But as we didn’t investigate the training process and trainer’s roles in data viz creation in depth, we could not say anything further about it.

Example of Indonesia DHIS2 dashboard issues

How have you used the study findings?

As the research highlighted that people are making poor chart type selection, we have implemented new dimensions for selection panels in the Data Visualization app in DHIS2. Thus, we expect that it will be easier to choose the data and it will guide the chart selection.

The research has become a foundation for us to encourage the use of standardized dashboards that are built around standard operating procedures and shared down to users. This way, users will get more practically useful dashboards.

The dashboard sharing function has been improved as well. We also added descriptions and dashboards items like boxes that will allow dashboard creators to input more instructions and guidance on how to use the dashboards directly on the dashboards.

Specifically in Indonesia, we developed data visualisation guidance materials based on this study result that use real-life examples (i.e of common problems). We delivered these materials academic lectures, roll out, and refresher trainings in discussion method.

In addition, the WHO meta-data packages primarily focuses on “best practice” data analysis through dashboards. Working with the WHO, we also make accessible pre-defined dashboards based on current knowledge on how to manage various health programs. These may or may not be fully compatible with what countries are currently collecting, but we also see that these are used as inspiration to make better dashboards in countries.

This interview has been lightly edited for clarity.

Thank you to Aprisa Chrysantina and Johan Ivar Sæbø for participating in this interview. You can learn more about DHIS2 here. Special thanks to Senthil Natarajan for his editorial support.

Tricia Aung is a Research Associate and Faculty member at Johns Hopkins School of Public Health in the Department of International Health. She leads workshops and research in visualizing global health data for decision-making in low- and middle-income country audiences. She is a Co-Chair for the DVS Diversity Committee.