How can healthcare data visualizations be made more accessible?

Tailoring healthcare data visualizations for optimal accessibility begins with a deep understanding of the audience. Identify user demographics, including healthcare professionals, patients, and policymakers, to align visuals with their specific needs. For instance, a radiologist may require detailed imaging data, while a policymaker may seek broader trends. Employing user personas aids in crafting visuals that resonate with diverse stakeholders. Technical considerations, such as responsive design for varied devices and adherence to accessibility standards like WCAG, enhance inclusivity. Ensuring that data insights are presented in a contextually relevant manner caters to the distinct requirements of each audience segment.

One of the most essential things when creating data visualization and dashboards is tailoring the visualization to the user's needs. In my experience with healthcare, I worked with the Head of the Department and the Head Nurse at a hospital. One was more technical than the other. So, I needed to design data visualizations that were applicable to various audiences. One thing I've found helpful when making data visualization for diverse audiences is ensuring the visualization is simple, legible, easy to understand, replicable, and has comprehensive documentation.

1)Create a KPI dashboard that highlights the key metrics w.r.t healthcare. 2)Consider providing the data in different formats like simple charts, text table,etc. 3)Perform the testing of dashboards with individuals who have different abilities and disabilities to gather feedback and identify areas for improvement. 4)Provide the contextual help of the dashboard so that the users understands the data and the metrics and charts that are displayed.

In my experience, utilizing participatory approach through designing with your audience in mind is crucial to creating accessible healthcare data visualizations. By understanding their needs, goals, and characteristics, you can tailor your visualizations to effectively convey information. This approach ensures that your visualizations are inclusive and meaningful to all users, regardless of their data literacy, impairments, or cultural backgrounds. It also creates a sense of ownership, empowerment, and attachment towards the case.

The ultimate goal of any visualization should be to have simplicity at its core. Identify what the lowest level of user knowledge is on the subject and simply start from there. Take inputs from your most novice audience first and build your data visualization around it.

Enhancing accessibility in healthcare data visualizations necessitates meticulous format selection. Tailor the visualization to the nature of the data and the audience's preferences. Opt for formats like interactive dashboards for professionals requiring detailed exploration. For example, in clinical settings, an interactive dashboard displaying patient vital signs over time enables comprehensive monitoring. Implementing responsive design ensures adaptability across devices, maintaining clarity and functionality. Technical considerations like SVG images for scalability and color contrast optimization for readability further refine the chosen format.

To enhance the accessibility of healthcare data visualizations, consider using clear and intuitive design elements, such as color coding and simple icons. Provide concise labels and ensure compatibility with screen readers for visually impaired users. Additionally, offer tooltips or brief explanations to guide users through the information, making the data comprehensible to a wide audience.

When dealing with multiple audiences, I encourage my team and our clients to use a multi-level communication approach that has simple, explanatory information for a broader audience, and as people engage, they can find more complex/interactive forms of visualization. We like to create the rabbit hole technique for visualizations since one approach will never be suitable for all types of users. And I believe health is an area we certainly need more people engaging and understanding.

Choosing the ideal format for data visualization is integral for effective data storytelling. Understanding the data type driving the data visualization will help select the appropriate charts and diagrams. During my healthcare research, I built data-driven simulation models to help improve nurses' response time to an in-hospital life-threatening event. This data visualization was in two parts. The simulation displays a real-world replication of hospitals and the impact of changing factors on response time. The second data visualization is a simple table to show variability and average key metrics (e.g., response time) by various factors. Customizing visualization to data type increases knowledge and valuable insight gained by the audience.

When generating complex charts/graphs using common software packages, e.g., ggplot2 in R, keep in mind that some default settings usually end up in unpleasant visual effects (e.g., the default ggplot2 theme will draw a large grey box at the bottom layer of the whole graph and reduces overall contrast). Be meticulous and make sure the output figure has the maximum brightness and contrast if possible.

Enhancing accessibility in healthcare data visualizations involves catering to diverse learning preferences by providing multiple modalities. Incorporate visual elements alongside descriptive text and auditory cues to accommodate various information processing styles. For instance, in depicting patient demographics, a combination of charts, concise textual summaries, and voice-guided explanations ensures a comprehensive understanding for a wide audience. Leveraging assistive technologies such as screen readers for text-based content and alternative text descriptions for images addresses accessibility standards.

Enhance health data accessibility by offering information in various ways. Use a combination of visuals, such as charts and graphs, accompanied by clear text explanations. Consider audio descriptions for the visually impaired and provide downloadable data for those who prefer detailed analysis. Incorporating multiple modalities ensures that individuals with diverse preferences and needs can access and comprehend health information more effectively.

Visualization is a very powerful mode of sharing data driven insights but is not useful if cannot be accessed easily or interpreted easily. The ease of access and data literacy differs by the audience say providers or doctor offices have to log on to a different platform to gain the valuable insights like logging into a website or a Citrix platform whereas a payor audience may need to login to the visualization software platform. These kind of additional access steps causes hindrance and can be worked upon by designing the mode of delivery of the visualizations in the format best catered to the audience.

In addition to the filters and sliders, provide a way for users to download a dataset so they can get even more granular with the data and visualizations in ways that they prefer. Always be sure to de-identify and have this option approved with your regulatory oversight team.

It’s important to remember that people have diverse abilities and preferences, and what works for one person may not work for another. Some people may prefer to interact with data through touch, using haptic feedback devices. Others may prefer to use voice commands or gestures to navigate and manipulate data visualizations. Consider the use of machine learning and AI to provide personalized experiences. For instance, an AI could learn a user’s preferences and automatically adjust the visualization to meet their needs.

Especially in healthcare it is essential that you make your data visualization accessible. People in a hospital are often uncomfortable (in a normal situation they should not be there) and having accessible data visualizations might make them feel more at ease, as data is then communicated in a clear and concise way. I often compare it with a child hospital, where there are no “doctors“ (just call me by my name), or where everything is designed for the children (colourful, joyful, …) to make the experience less frightening.

If you are providing visualizations online, make sure you are using responsive web design so those visualizations look great on all types of devices. Don't rely only on emulators for testing. Go through the effort of using various devices to view your visualizations.

Ensure accessibility for all: Pay attention to accessibility guidelines, such as providing alternative text for images, using high contrast colors for those with visual impairments, and ensuring compatibility with assistive technologies like screen readers.

When presenting visualizations online, ensure the use of responsive web design to ensure optimal display across all device types. Avoid relying solely on emulators for testing; make the effort to view your visualizations on different devices for a comprehensive evaluation.

Always make sure that you include some mechanism for soliciting feedback from users. This can be as simple as an email address to click on; a link that opens a page to send feedback; or some other way. If you don't provide this feedback loop, you will never know about any accessibility issues being encountered by your consumers. When you do get some feedback, take it, and enhance your visualizations based on it. Deploy, then wait for updated feedback. Consumers will love that you are listening to their ideas, and have updated the product based on their ideas. It's a win-win for everyone.

Performing user testing to collect feedback and pinpoint areas for enhancement is crucial. Consistently revise and fine-tune the visualizations in accordance with user insights to improve both accessibility and usability.

Follow the principles of "design thinking" and schedule so called "playbacks" with your key users (the personas). Based on my experience, when we engage with the representatives of the intended data visualization consumers more often than less often, the product will better address the painpoints of the respective users and adoption will be faster and more successful.

To enhance healthcare data visualization accessibility, I: Conduct usability testing with representative users to evaluate effectiveness and ease of use. Seek user feedback through surveys, interviews, or focus groups to understand preferences and needs. Validate with domain experts to ensure accuracy and industry relevance. Iterate and refine based on user feedback, enhancing accessibility and usability.

Testing and iterating involve trying things out and making improvements. In health data visualization, regularly test your visuals with the audience, gather feedback, and then make changes based on what works best. This process helps refine the presentation, ensuring it's clear and meets the audience's needs effectively.

While there have been deliberations on accessibility of visualisations, I think we must exercise care on the potential misuse of the visualisations primarily due to the lack of awareness on the appropriate choice of visualisation or the complexity of the visualisation itself amongst other factors. I believe this fact requires a greater emphasis among all the stakeholders associated with the development and dissemination of data visualisation. Secondly, from a layman perspective, it’s important to create a user-friendly interface to enhance engagements with the data.

What if, instead of adapting data visualizations to cater to diverse audiences, we completely reimagined how we present health information? A disruptive approach would be to create customizable and interactive interfaces, allowing each individual to shape the representation of data according to their own needs and preferences. This would not only promote accessibility but also empower users to be the designers of their own understanding of health data

As complex and far-reaching as public health and holistic wellbeing is for humanity, it is no longer enough to excel in a limited area. It is far more important to create an integrated health ecosystem where data from disparate platforms about patients - be it medical data, mental and physical fitness data, sleep data, nutrition data - are all integrated, and correlated with scientifically proven research insights in industry specific large language models. Such visualizations can provide a precise and personalized insights and recommendations to each patient.

One thing that I have found that worked very much for me is it to keep it all simple. People with intellectual abilities would be accessing this information. Let the data visualisation make sense technical and non-technical audience at the same time

Distinguish between exploratory and explanatory analysis. Exploratory analysis entails examining diverse datasets for patterns without predetermined hypotheses, fostering discovery and pinpointing areas for further investigation. Conversely, explanatory analysis rigorously tests existing hypotheses, communicating findings precisely. This distinction is vital; exploratory analysis uncovers insights, while explanatory analysis clarifies specific hypotheses, contributing to a comprehensive understanding of data. Integrate both analyses in your discovery phase for optimal storytelling potential.