For those who don’t know, I was on parental leave for almost all of 2020. Not only did this mean almost no research time, but it meant no blogging time as well.
Of course, there were plenty of benefits!
I still got 5 blog posts done this year, which I’m pretty amazed about given my general level of alertness 😴. The Viz/CMS stuff in particular was a huge task, but well worth it IMO. It definitely reminded me again why I have the blog in the first place – I never would have spent all that time learning about the byzantine reimbursement systems in the US, but having done so has been very useful in understanding the pathways to success for commercial medical AI. And maybe this is a bit nerdy 🤓, but it was fun!
There will be lots more coming on that front this year as well, as we will see if Viz’s competitors will receive the same payments, and (I didn’t have the time to cover this so far) but the first ruling approving outpatient reimbursement for AI snuck in at the end of the year (pdf link, relevant section page 290-295), which at over $50 per patient might have an even bigger impact on medicine in the long run.
Anyway, I will be returning to work more consistently this coming year, and I will make an effort to blog more often. I actually have … (checks) … yikes,
68 69 70* unfinished blog posts (😱)! Many of them are close to complete, so hopefully it will be a productive year.
On topics and style for the coming year, expect more of the same. I still favour long-form, in-depth (but not too complicated) analysis and opinion stuff. One concept I came across for the first time this year was research intimacy, which is the deep connection and fluency that a researcher develops for a topic after working on it for a while. As I finish my PhD this year (🎉yay🎉) I suppose that I have some of that myself.
So that is what I will focus on this year. I see research intimacy not as knowledge per se, but more about how a researcher thinks about their field 🤔. I’ll try to share the concepts and framings that have helped me get a better appreciation for the complexities of medical AI. Can’t promise they will be useful to y’all, but they feel like the distillation of my last few years of work so hopefully they aren’t terrible 😛
I’ll also try to continue my “best X paper you have probably never heard of” series, to highlight and celebrate 👏 some less well known work that I’ve found really compelling. I’ve already got one of these mostly done but it is related to a paper that is under review 📝 so I’ve been holding off on posting it 📨.
I’ll also be hopefully having another post or two about our experiences training Australia’s first Radiology AI Fellow 🎓! Maybe even a guest post if we are lucky 🤞 (sneak peak : it has been great, and we have been promised the necessary 💸funding💸 to continue the role in 2021)
I’m also intending to start the year off with a bit of a bang 💥, so keep your eyes peeled 👀 for some controversy 😤
Happy new year! Here’s hoping 2021 isn’t a complete dumpster fire ಠ_ಠ
* yes I started two more blog posts during the day I spent writing this one 😅