The last couple years since starting Overlap, I’ve kept my head down so I can focus on making an awesome product that will help organizations transform how they deliver healthcare. Because of that, I haven’t attended many conferences.
I started 2018 right by attending HIMSS in Las Vegas. What follows are my impressions of the event. I’ll list the few things I felt were special and welcome anyone to add their opinions and fact-check me. This was a large event and there’s no way I could have seen everything.
46,000 people + 5,000 vendors
Not only was this my first time visiting Las Vegas (which in itself is overwhelming), but this was a large event. Two stories of exhibitor halls. Thousands of people rushing up escalators, waiting in lines to get food and coffee and filling auditoriums. This was a hard event to keep a pulse on trends.
(I took a screenshot of my Apple Health app showing how many steps I did in just two days alone. HIMSS could be good for your health!)
My first day was spent bouncing between talks, meetings, and wandering around the exhibit hall. I bumped into a friend and colleague, David Goldsmith from Wego, and he advised to spend at least one day in the exhibit hall only. He was right.
By spending that next day with the exhibitors I got to listen to much more intimate talks by companies, interact with people at those talks and learn more what companies were doing.
It’s now obvious more than ever that we have a full fledged market. Using data to power healthcare is a real thing and it’s not going to go away. Yes there were many exhibitors doing parlor tricks (I won’t name who) but as an industry this is real. Like most nascent industries there is a lot of churn and there will inevitably be winners and losers (just look at the auto industry and those have survived till today). But it was great to see healthy competition in action. I have hope for the future.
Not to sound like an old timer, but I remember the first mHealth Summit in 2009 which was hosted at World Bank in DC. It was small with more doubts and questions than people.
I think HIMSS has really helped put those doubts to bed.
Some companies I’m rooting for
- Google Cloud and their new Healthcare API
- Change Healthcare
- Syllable.ai
- Uber Health
- Blackbaud
- Aptible
“I miss the mob”
At the backdrop of this massive conference, it was hard to get a sense of the magic of the industry that we’re lucky to be a part of. We’re doing something special. We’re spending our time and money to help carve out the future of how people live a healthy and happy life. We’re very lucky.
That first mHealth event I mentioned earlier. Yes it was small. Yes it was a little disorganized. But it was magical. We gathered together in DC because we wanted to share what we’re doing but also how we can help each other change the world. I know it’s nostalgic but it helps to remember those days so we can know where we’re going.
Derek Sivers wrote a great post about his interaction with a cab driver in Vegas (fitting for a post on the reflections from HIMSS 2018). The point is that when you corporatize an event or an industry, you start to lose the essence and simplicity from when it first started. Not to knock on salesmen in suits, but there were so many of them spouting buzz words like “machine learning algorithms”, “AI”, “analytics”, and more. When every conversation is clouded with dollars and cents and buzzwords it makes our industry seem a little less fun.
Which brings me to my next impression.
There was little to no discussion on the socio-cultural aspects of AI and Machine Learning
We are living in a world of where Machine Learning (ML) and AI are becoming more and more a part of our lives. Or at least they’re talked about all the time in tech circles. There are heated debates about these technologies on the Internet and in public forums. You get the proponents like Eric Schmidt (keynote speaker at HIMSS) and the realists like Elon Musk and our industry’s own, John Mattisson.
With how many vendors and talks about AI and ML, we are still a long way away from these technologies becoming part of the “plateau of productivity” (a nod to the Gartner hype cycle).
While a lot of science is being poured into using AI and ML for healthcare, I’m not seeing much of a discussion on how to prevent these technologies from corrupting our society.
I’m mostly concerned about the non-existent conversations around biases.
I’m not an expert on AI and ML and happy to stand corrected from the opinions of those who are.
But I have built models in my days as a health economist. And there is without a doubt bias applied to many of the assumptions I made in developing those models. Now I was trying to be a good scientist and be as unbiased as possible but I’ve come to learn that bias is a natural, unavoidable human characteristic.
If we’re really going to make AI and ML a larger part of healthcare, an industry that affects every human on the planet, then the assumptions that scientists and companies are making need to be published or at least discussed.
Tacitly assuming that AI and ML will solve all of our healthcare problems is a scary thing and needs to be challenged in order for us to get to the outcome we want.
FHIR is picking up steam
It was great to see so many conversations and companies making FHIR endpoints available. Interoperability is becoming less and less of a hurdle. We still have a little while to go, but I was happy to see this part of healthcare advancing.
(An aside, my non-profit, Open mHealth, is starting to build more FHIR endpoints to bridge the gaps between patient-generated data and EHR data. Stay tuned)
The big question: Would I go back again?
Maybe.
I’m happy I went and I learned a ton.
But I’d prefer more intimate talks where I can have less transactional conversations with people though. I’d also like to see more talks poking at the norm. I think we need more of this juxtaposed to the tech evangelists.
Regardless, I look forward to seeing the program for next year! Maybe I’ll see you there.
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You might enjoy Overlap for your hospital, clinic or research project. We help you do remote monitoring right.
