Digital health systems are ubiquitous. They manage and protect the data that goes into our medical records, remind us to take our insulin or to go for a run, store our medications and ailments, collect information about ongoing symptoms, and even serve as a repository for our genetic data. But with the growth of digital solutions in the healthcare space comes a deluge of concerns with user experience, information access, ethics, and privacy problems.
Typically, software engineers and managers have been in charge of making decisions about human-computer interaction, data transparency, workflow and task support, and user experience. As a result, the users of these systems — which include patients, medical researchers, scientists, and policymakers — are forced to interface with obscure, obsolete, inaccessible, and clunky systems that disservice their target population.
Oftentimes these decisions were based on unverified assumptions about the user’s goals and needs, and designers were left out of the equation, reducing their profession to only visual aesthetics.
Digital Health Systems Have a UX Problem
Most of the problems with digital health systems stem from a fundamental lack of understanding of the users and the tasks that they are trying to accomplish.
Take the electronic medical record (EMR) system as an example. These systems are typically difficult to use and require many training sessions due to their bloated interfaces, mismanagement of the user’s cognitive load, and bad scalability. Moreover, because EMRs require a lot of focus to correctly fill out the different patient forms, they often force healthcare professionals to pay less attention to the actual patient.
In the United States, for example, these systems have also been designed with a primary goal of facilitating some billing activities, not necessarily making it easy for the doctor to dedicate their full attention to their patient.
Another major issue is the lack of system interoperability and the information silos created by incompatible formats and poor understandings of user workflows. While there is improvement due to technologies that allow easier data transfer between different EMRs, moving data from one hospital to another can be an arduous task (for instance, when you switch your healthcare provider). This also often renders data into unstructured, text-based documents, which makes it hard for healthcare providers to review medical history or for healthcare apps to automatically find your list of medications or illnesses.
Information silos happen in research settings, too, where clinical researchers use many different tools (oftentimes custom-made or maintained by open-source teams). Switching between one tool and the next takes time and effort because a unified user journey was never conceptualized.
And on the topic of designing with intention, conveying transparency in EMRs has also become a major issue, particularly when asking users to give their medical information for clinical research. In fact, during this pandemic, we’ve seen that a major risk for the adoption of contact tracing and monitoring mobile apps has been the mistrust that users have in these systems. If getting contact and symptom information from the population could be helpful for public-health planning, then designers must rethink how they approach creating this software.
As digital healthcare continues to expand, and designers make their way into hermetic spaces that have been historically dominated by other specialists, they have a responsibility to design secure, easy to use, accurate, transparent, and interoperable systems based on evidence gathered through design research and evaluation, peer-reviewed articles and ethnography.
To do so, designers in the digital healthcare space should incorporate new, more rigorous skills into their practice.
Learn to Read and Understand Scientific Articles
While designers are not expected to become domain experts, they should strive to acquire domain-specific knowledge and apply these rules to new contexts. Become familiar with scientific terms and concepts that are at the core of the business or research done in your organization. This is fundamental to create a rapport with the domain experts, users, and other stakeholders that influence the design of the digital products you’re building.
Moreover, you’ll be surprised by the amount of data already out there that describes specific populations like physicians or mobile health app users. Therefore, the literature can be used as a solid foundation for understanding your target users as it provides detailed, trustworthy data about user groups, pains, goals, and barriers to adoption. At TriNetX, I encourage our team to do roundtables where we review and discuss literature about a scientific topic or a group of users. Don’t know where to start? Try PubMed.
Change Your Mindset to Facilitate Discovery
Healthcare problems are generally addressed through a collective effort of transdisciplinary teams, not by individuals. Designers in healthcare must shift from the mindset of “I am the problem solver,” to a mindset of “I can help create an environment for ideas and systems to flourish.”
Designers should let users and medical experts be an active part of the design process by inviting them into the early stages of design, not just as a target audience from which we can gather research, but as co-creators of the health systems.
In essence, designers should foster this collaboration. Ways to do this include:
- Hosting co-design activities such as problem-definition exercises.
- Sharing and discussing observational insights.
- Offering sketching sessions.
- Teaching users how to give and receive design feedback, and giving them instruments to express their thoughts through physical and digital mediums.
Participants should never feel like they’re not creative enough, or that the user experience of the system is not within the scope of their capacities and abilities.
Strive to Manage Complexity, Not to Reduce It
Reducing complexity for effortless, delightful experiences may be desired in many B2C applications, including healthcare applications. But designers will also want to expose the nuance and complexity that comes with running statistical analyses on healthcare data, or by showing historical results of a person’s blood pressure measurements.
Use well-tested and reliable interaction methods here, such as progressive disclosure, focus-plus-context, or overview-plus-detail interfaces just to name a few, as well as clear language that was tested to be well understood by your users. I recommend reviewing Nielsen Norman Group reports as a primer.
Brush Up on Statistics and Review Uncertainty Concepts
Depending on what your team is building, you should be keenly aware of the information you’re presenting, particularly if you’re designing data visualizations or displaying results such as a laboratory test or genomic data. Do you know the difference between a median and a mean? How about between a histogram and a bar chart?
Presenting data clearly is a prerequisite to increasing the trustworthiness of the system, and it reduces opportunities for users to doubt the veracity of the information they see on the screen. For example, think of a mobile app that predicts the likelihood of having influenza based on symptoms and ask yourself:
- How can I convey that this is a probability and that there is a margin of error that the patient needs to consider?
- How can I anticipate how the user will feel with a likely positive result?
- What immediate information can I surface to help them navigate this situation?
Design With Ethics in Mind
Ethics in technology should be considered by everyone that plays a role in developing software. Having said that, designers in healthcare must constantly advocate for the creation of transparent software, actively avoid dark UX patterns, and give users as much control as possible over their data. Don’t settle with being HIPAA and GDPR compliant — although it’s definitely a good start, try to go beyond the minimum requirements of the law.
Collect as little data as possible: For instance, determine if you really need to know a user’s gender, their email address, or their location. Is collecting these data going to have a tangible benefit for the user? Take advantage of user tests and other design research to not only make sure that users can accomplish their goals but that they feel comfortable with the software you’re designing. Finally, be always on the lookout for any signs of implicit biases that could negatively impact the experience for some of your users.