Innovator Spotlight Q&A Series: Shuhan He, MD
Innovator Series
The CIDH Innovator Spotlight is a Q&A series that celebrates innovative ideas, highlights the important work that digital health innovators are doing to advance patient care and outcomes at Mass General Hospital, and shares key learnings about the innovation journey.
We are pleased to feature Shuhan He, MD, for his exceptional work in developing an open-source Emoji Based Visual Analogue Scale to give patients more ways to communicate subjective information, such as someone’s pain perception. His second innovation was the creation of more medical-based Emojis onto the standard Emoji keyboard, with successes for the anatomical heart and lung emoji in 2020. Since then, a team of physicians, medical societies, and patient advocacy organizations have come together to advocate for a more comprehensive set of medical emojis, believing there could be significant patient benefits.
Q: Tell us about your innovation and the challenge(s) you were trying to solve.
SH: The innovation: An open source Emoji based visual analogue scale, similar to the Wong-Baker Scale that can be used in any patient device worldwide, Android or iOS, and that also integrates into EMR’s since it uses the universal Unicode standard. The second innovation is the creation of more medical based Emoji onto the standard Emoji keyboard, with successes for the anatomical heart and lung emoji in 2020.
More information can be found here at Medicalemoji.org!
The motivation is that I’m a practicing ER doctor here at Mass General, and my job is to understand how I can help patients. To do so, I have to find information that is both subjective and objective, such as someone’s perception of pain (subjective) and lab results (objective). However, the data that a patient tells me about their pain doesn’t make the medical record but is recorded as free text, and I have no context as to how much pain someone is in compared to their usual baseline.
This is a thriving area of research called patient-reported outcomes, and fundamentally an area that requires digital health. It requires that we codify and measure a lived experience and translate that into big data. Our open-source emoji-based visual analog scale is visually the same as the popularly used Wong-Baker scale, however since it is digital, it comes with underlying encoding that can be semantically interpretable and interoperable.
Q: The innovation process can be long and challenging, but also rewarding. What inspired you to begin this journey?
SH: I’ve been working on this project since 2017! I have been developing healthcare applications as a medical student and was helping researchers solve the problem of how to ask patients how they felt about the educational resources they had. There are hundreds of different types of surveys, and most visual scales are very difficult to implement into apps. So I wondered about how we could use Emoji to better capture data on mobile applications
Q: Please tell us about your overall experience and some of the major milestones you’ve achieved so far. What are the next steps?
SH: I was one of the co-authors of the Anatomical Heart and lung Emoji that was approved on Unicode 13.0, and available on all Android and iPhone devices worldwide.
Please use the heart and lungs emojis!
In 2021, we were able to publish an editorial in JAMA describing how Emoji could be further used in medicine, given the universal nature of how it is used.
We then did a study here at MGH with my colleague Dr. Jarone Lee, studying how patients’ respon
ses to pain rated on an Emoji based visual analog scale, similar to the original validation studies for the Wong-Baker Scale. Our results showed they were equivalent, and we published this finding in JAMA in 2022.
We are now actively working to endorse the Anatomical Stomach, Liver, Kidney, Spine, and other more medically salient Emoji for the next submission in 2024. We are also working to understand how people use Emoji in free text with a high degree of freedom to express themselves. This is a very complicated big data and machine learning problem!
Q: What resources have been most helpful to you, and that you think other MGH innovators would benefit from?
SH: Well, the CIDH of course! The innovation community here at MGH is a thriving one and the support system at CIDH really helps to push along projects, whether you need a link to industry, help with writing grants, or a technical evaluation of technology.
It’s also been great to have so much cross-departmental collaboration here at Mass General between the Emergency Department, Lab of Computer Science, and Department of Surgery. These sorts of interdepartmental work really help move things along! I am also grateful for the many incredible mentors I have had here at Mass General.
Q: What advice would you give to other innovators at MGH that you wish you had been given early in your innovation journey?
SH: I think having a general roadmap of where you are going really helps. I keep a roadmap of all the directions that the project could be going, and then prioritize them on critical needs vs. nice to haves. It’s a tool I learned from project managing complex technical builds and something that applies to almost any project.
I also think that diving in to make a website for the project really helps to make it real and give a project life. It is common ground for people to learn about a project, update it, and force you to give a pitch to a new audience for what your goals and objectives are.
I am also a big fan of open-source tools as they allow you to capture the power of entire developmental communities instead of being a sole developer.
