Healthcare IT Today
February 28, 2024
Burnout is something that can slowly creep up on you but take a long time to crawl out of. This serious problem is something that happens with you are overworked and overstressed from the problems you are tasked with at work. In the world of healthcare, where we are constantly looking for ways to make things better, it is no surprise that we are facing a high burnout rate. While burnout is hard to recover from, it is possible. Taking time off and finding things that you enjoy and are excited about are key to letting your brain reset and being ready to get back to work.
While there are countless problems and struggles in healthcare, there are a lot of ideas and technologies right now that are working to make things better. So let’s take a moment to step away from our problems and stress to focus on the health IT trends that we are excited about! We reached out to our wonderful Healthcare IT Today Community and asked them: which health IT trend has you most excited and why is it exciting? The following is what they had to share.
Joseph Zabinski, Managing Director, AI & Personalized Medicine at OM1
I’ve seen 2024 described as the year AI ‘grows up’. We’ll only know in retrospect if this ends up being accurate, but I’m most excited about the trend toward maturing AI applications in healthcare. This means a few things: hype fading to background noise, growing acceptance of AI outputs by patients and providers, well-targeted applications with demonstrated value, and most importantly, the technical infrastructure to ingest data, analyze them quickly, and deliver insights seamlessly to end users. This pipeline is critical to unlocking some of the tremendous value AI can provide, like surfacing ‘hidden’ or ‘lost’ patients for diagnostic evaluation or access to effective treatments.
I do worry that hype, unrealistic expectations, and even concerns about AI will continue to hold back adoption – especially if we expect AI to be perfect, or to solve every problem. The newest tools like large language models can compound these challenges, as they can also generate even more value for patients. The key to meeting these challenges is to ground expectations in real-world problems – focusing, for example, on circumstances where providers could genuinely use added insight to inform decision-making around a particular course of treatment.
Characterizing these needs in language users understand, meeting those needs using AI tools, and doing so in a compliant, minimally disruptive way is the key to building momentum and trust. If we can do that, AI adoption in healthcare will take off, and we’ll see the real-world effects on patient outcomes we know AI can deliver.