CES POV: Healthcare Data in 2022: Learning the Future Language of Health

This year, the healthcare industry made CES history when CEO of Abbott, Robert Ford, delivered the first ever CES keynote by a healthcare company. During the presentation, Ford revealed an exciting new wearable called Lingo. Based on Abbot’s Freestyle Libre glucose monitor for diabetes, Lingo is designed to help all consumers live healthier lives every day. It promises to “learn your body’s unique language” by taking your health data and translating it so you can listen, learn, and take action. At that moment, I realized the incredible amount of work that data scientists have been doing behind the scenes to power so many tech advancements we’ve seen this year. They are defining the Latin roots of our new language—the language of data that will shape the future of health discussions with our doctors, our peers, and most importantly, with ourselves.

Is the healthcare system ready to adopt this new language?

Data-driven AI has already been useful in radiology and breast cancer screening, and companies like binah.ai are bringing the technology to everyday monitoring of vital signs with their video-based platform. While large investments are being made to advance data science and AI in healthcare, regulatory concerns present major speedbumps slowing the adoption of health data into our everyday lives.

Entrepreneur Dr Eric Dy, Founder and CEO of maternal monitoring and data company  Bloomlife, believes that technology is moving much faster than policymakers can handle. Initially, the company marketed their remote monitoring services directly to consumers and began collecting longitudinal patient data for two years without regulation. One day, the US FDA decided what they’re doing should be regulated and had to be distributed through HCPs, requiring them to start over. While regulations might slow down startups, they are critical to building trust. They must ensure unintended consequences have been considered, and thoroughly tested for accuracy. Without standards, trust is at risk, and so is adoption.

How do we make sure the language speaks to everyone?

In a session about consumer safety driven by AI, Pat Baird, Head of Global Software Standards at Philips discussed different needs to build trust. Patients need to see personal relevance with inclusive representation in AI training data. While large sets of data mean we can develop AI algorithms to predict and prevent disease, they will only be useful to everyone if we collect data from everyone. We must consider a community approach to data, not just a personal one.

To collect data inclusively, we need to help close care gaps. This is something that remote monitoring company OMRON is attempting to do. During their press conference at CES, OMRON highlighted that their VitalSight data hub does not require WiFi or cellular connection, a critical feature to provide access to care and data exchange in underserved communities.

Are HCPs on board with the data revolution?

For decades, doctors have been talking about clinical trials—controlled data with finite parameters. Health data sets today are much more complex, with infinite possibilities for collection, analysis, and interpretation. But doctors don’t want more work managing more data. So, to win the support of our healthcare heroes, when creating a connected device and data output we must consider three things:

1. Does it make practice easier?
2. Does it save doctors time?
3. Does it significantly improve outcomes?

To help with adoption, physicians also need training. Dr Tania Elliot, Chief Medical Officer of Virtual Care at Ascension is helping their doctors realize technology is the future. They offer a robust graduate medical education program to teach doctors about the use of AI and healthcare technology in clinical practice.

Can we really make heath data conversational for patients?

Dr Hon Park, Chief Medical Officer of Samsung believes that the next battlefield for healthcare is in the home. Patients are demanding a remote or hybrid care model where data is part of the virtual or asynchronous conversation. Diabetes patients want to just upload their glucose data and hear back from a qualified professional. That professional can be another care provider, or a coach as in the Livongo and Omada models.

While virtual care seemed forced upon patients during the pandemic, the benefits and conveniences are undeniable. A key takeaway from this event is the opportunity to enhance telehealth with remote devices to share patient data as common practice. In essence, we can use the language of data to communicate with doctors about our daily health, not just during a 15-minute consultation.

Health tech companies large and small are determined to harness the power of data to digitize, decentralize, and democratize healthcare. As our data scientists and engineers define and develop the language, we need a new wave of experts to translate, humanize, and champion it in our communities. With their help, I’m hopeful we’ll become fluent faster than we think.