Bloggers like myself see a lot of data on the uptake of emerging technologies. My biggest sources are market research firms, which typically provide the 10,000-foot view of the technology landscape and broad changes the new toys might work in the healthcare industry. I also get a chance to read some great academic research, primarily papers focused on niche issues within a subset of health IT.
I’m always curious to see which new technologies and applications are rising to the top, and I’m also intrigued by developments in emerging sub-disciplines such as blockchain for patient data security.
However, I’d argue that if we’re going to take the next hill, health IT players need to balance research on long-term adoption trends with a better understanding of how clinicians actually use new technologies. Currently, we veer between the micro and macro view without looking at trends in a practical manner.
Let’s consider the following information I gathered from a recent report from market research firm Reaction Data. According to the report, which tabulated responses from a survey of about 100 healthcare leaders, five technologies seem to top the charts as being set to work changes in healthcare.
The list is topped by telemedicine, which was cited by 29% of respondents, followed by artificial intelligence (20%), interoperability (15%), data analytics (13%) and mobile data (11%).
While this data may be useful to leaders of large organizations in making mid- to long-range plans, it doesn’t offer a lot of direction as to how clinicians will actually use the stuff. This may not be a fatal flaw, as it is important to have some idea what trends are headed, but it doesn’t do much to help with tactical planning.
On the flip side, consider a paper recently published by a researcher with Google Brain, the AI team within Google. The paper, by Google software engineer Peter Lui, describes a scheme in which providers could use AI technology to speed their patient documentation process.
Lui’s paper describes how AI might predict what a clinician will say in patient notes by digging into the content of prior notes on that patient. This would allow it to help doctors compose current notes on the fly. While Lui seems to have found a way to make this work in principle, it’s still not clear how effective his scheme would be if put into day-to-day use.
I’m well aware that figuring out how to solve a problem is the work of vendors more than researchers. I also know that vendors may not be suited to look at the big picture in the way of outside market researcher firms can, or to conduct the kind of small studies the fuel academic research.
However, I think we’re at a moment in health IT that demands high-level research collaboration between all of the stakeholders involved. I truly hate the word “disruptive” by this point, but I wouldn’t know how else to describe options like blockchain or AI. It’s worth breaking down a bunch of silos to make all of these exciting new pieces fit together.
