With social movements garnering mainstream coverage, there is a heightened awareness around delivering more inclusive products. As people, and creators of digital solutions used by diverse groups of patients, we’ve been forced to take stock. We’ve found ourselves asking, as a digital healthcare company, how are the solutions we create supporting inclusion and diversity? Do the solutions we build represent our real users? And what about those designing the technology itself? Does leadership look homogeneous, or are we more representative of the ‘real world’?
Read on as we share how you can evaluate the digital solutions you’re offering to patients and injured workers. Are these apps, solutions, or platforms as diverse as your injured workers? Are these health products designed by and for, the people you support?
Here are some elements to think about when evaluating a patient-facing digital healthcare solution.
Is there Diversity in Decision-Making?
In order for end-users (in this case patients or injured workers) to really be represented, the decision-makers behind the digital solution should be diverse themselves. When looking at digital health solutions, look for diversity in leadership. This will trickle down into the program and content itself. Are there women in leadership positions? What about visible minorities?
Ultimately, is the team diverse enough to create representative content for a diverse group of patients?
In addition, product and content feedback from a widespread, representative group of patients and advisors is critical. Does the solution resonate with users? Do patients feel included in the language, imagery, video, audio etc.? Does the program adapt to the needs of the patient themselves? Does it reflect their reality?
Is it Accessible Anywhere, For Anyone?
For a digital health solution to reach and support patients, it needs to be widely accessible. In technology speak, the four principles of accessibility are: perceivable, operable, understandable, and robust.
When looking at a healthcare solution, perceivable means that patients are able to understand the information presented—this relates to health literacy. Operable means that those with disabilities and other limitations can still use the technology. Understandable means that the clearest, most concise language is the benchmark—the patient should know what they’re getting, and the program should be logical and intuitive. Robust relates to a mix of technologies that work on various devices, browsers and platforms. In other words, the program isn’t just limited to one type of phone, computer software, or relies on specific add-ons.
Accessibility factors to look out for:
- No extra, wearable tech required. Technology is more accessible when the injured worker, patient, or user can access the program from devices they already own.
- App, platform or program can be used in various browsers, phone types, and devices. Make sure the program doesn’t require the latest smartphone to operate.
- Content is written for everyone. Medical content isn’t often written for every patient. A 6th grade reading level should be the benchmark.
- For all ages. Technology isn’t often designed for senior end-users. Large fonts, oversized buttons, and high-contrast images, and subtitles for audio and video help to reach all patients.
“The biggest drawback for seniors when it comes to using technology is they feel like the design of devices and interfaces is not inclusive…Devices are exclusionary and make the older population feel isolated from modern technology“
Does the Solution Reflect Reality?
There should be diverse representation in the imagery, videos, audio, and language used in patient-facing digital solutions. The patient should be able to see themselves in the app or program they engage with. For instance, if patients are older, from different ethnic backgrounds, male and female, are living with chronic pain, etc., this should be represented in the daily lessons.
For instance, if the healthcare program shares nutrition tips and recipes, are the recipes geared strictly toward Western diet and taste? If you have an ethnically-diverse user-base, the recipes and food tips should reflect worldly cuisine.
America is a diverse country, workplaces aren’t homogenous, and neither are your patient populations.
By including images, examples, and program elements that are authentic and diverse, you’re less likely to alienate groups of users.
Does the Solution Support Different Types of Learning?
Learning is not one-size-fits all. So, if the goal of your digital healthcare program is to improve health literacy and guide a patient through recovery, incorporating different learning types is critical. The different learning types are: visual, auditory, and kinesthetic. Hitting these different learning types will better the chances the program will be perceivable and understandable to patients.
Avoid any program that is one-size-fits-all and doesn’t remove the barriers to get to the core concepts. Here are different content types that should be reflected in a patient-facing solution:
- Visual– Images, photos, animations, and video are great for engaging patients. Strictly text within a digital solution is not just boring, it’s excluding various types of learners.
- Auditory– Learning through listening, and when concepts are explained out loud is a common learning type. Audio lessons, soundbytes, or even songs, can help with patient understanding and engagement.
- Kinesthetic– Learning by moving, physical activity, and sense of touch is embraced by digital physical therapy programs through video and health metric collection. In-app tools that encourage movement, and can actually measure that a physical therapy exercise is being executed correctly is even better!
People are visually-oriented: 90% of information transmitted to our brain is visual. Our brain processes visuals 60,000 times faster than text.
Do You Know “What’s Under the Hood” with Your Choice Technology?
Machine Learning (ML) and Artificial Intelligence (AI) are hot topics in digital healthcare. In order for technology to automatically do anything, a real person needs to program in the foundation. That programming can either support diverse populations, or reinforce the bias of the software engineers.
- Ask about the diversity of the technical teams who design the products which can help to avoid unconscious bias. For example facial recognition software has been shown to have a 35% higher error rate detecting darker skinned women compared to light skinned males similar to those who created the software.
- Ask which populations were part of the clinical trials for the product you are considering. Software which was programed and tested using a more homogenous population will provide skewed results when applied across a more diverse population
- Build in a check and balance system by making sure to review the automated parts of the program during the product evaluation period, and periodically over time.
As we all consciously try to move the needle, checking ourselves and the technology we build and share with others is the name of the game. With a more critical lens, we can choose technology that supports patients, while better reflecting them and the world we live in.
Any questions about using PeerWell to speed-up patient and injured worker musculoskeletal recovery? Send us a note.