Earlier this year, we collaborated with Mayo Clinic’s Center for Innovation to develop prototypes of data-driven healthcare applications for the future. Our design and development process is unusual in the healthcare field — as it is in many fields — and Mayo, impressed with the results, asked us to run a breakout session at Transform outlining how we use our process to approach problems surrounding patient data.
But as humans, evolution isn’t working on an 18 month scale. So while the technical side of working with data is making lots of progress, we haven’t made nearly as much useful progress in understanding all of it.
I wrote another piece about what I called the “Four Cs of Data + Design,” which was a way of framing some of where I think the focus should be in this kind of work. And last week, I spoke about those examples, and then more specifically about how they apply to healthcare, at the Mayo Clinic’s Transform Conference. (Video is here if you prefer listening to reading.)
Our current moment is a decision point (another theme of the conference) that requires a shift in how we’ve worked with data over the last 20 years.
When I started doing lectures about this kind of work in the late 90s, I had to spend a lot of time setting up the idea that this deluge of data was on its way, and that design (by way of information visualization) was an important part of the potential solution. The data is coming, the data is coming! (A feeble attempt at playing a kind of Paul Revere of data and design.)
Five years later, Google had launched, and people were all too familiar with information overload, but were impatient for answers.
Ten years ago, big data and analytics were going to save us. Your organization is just a data scientist (or two) away from having all the answers! Everyone was wrapping up their dashboard projects. We’re leaving the data warehouses and jumping into data lakes! With enough analytics, the story went, we can make sense of the black box of what’s in all that data.
Now in the last couple years, these organizations, having stocked up on data crunching and analytics capacity, still find themselves stuck.
Where’s the insight?
We know this because this is usually the point at which a potential client will get in touch with us: they were sold on something like Tableau and have just figured out it’s not giving them the answers.
And in the meantime, the hype cycle has shifted its attention to machine learning and artificial intelligence. Maybe the machines can figure it out for us! These are really powerful tools, but the problem is that now you don’t have just one black box you’re trying to figure out, you’ve got two! Why did the AI make that recommendation? It may work great for certain kinds of first order problems, but you still can’t generate insight from algorithms. Never could. People create insight, and the focus should be about giving them tools that help them develop these insights.
Thinking about where this puts us, let’s now think about the next twenty years, and apply the four Cs to priorities around understanding data in healthcare. Those aren’t the only four categories to consider, but I think they’re helpful for focusing on a few threads that are really important.
It’s about people communicating. The primary focus of EMRs should be about communication. How do patient and provider communicate? How does the provider convey results to a specialist? How do multiple specialists work together on a problem, as represented by a single patient’s data in that EMR?
We’re social animals, and we need better ways to socialize a set of information amongst all the people who need it. This shouldn’t be a tacked on feature like an email inbox embedded in an EMR application, it should be the central focus of how people work.
You look outside healthcare and you have tools like Slack, which is “a real-time collaboration app and platform.” But that’s just a 15 or 20 billion dollar way of saying “glorified chat client with document sharing.” There’s a reason they’ve been really successful: simplifying communication matters.
This is about data availability and new capabilities. So for instance, a promising recent development is what Apple is doing with its the Health Records API, where medical records are cracked open ever so slightly, on an individual basis. With it, users can access parts of their health record directly on their personal mobile phone.