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Turns out doing AI right in healthcare applications can be hard...

According to recent research in a recent article published by STAT News, there is more to successfully developing an AI-enabled application than just getting the technology and algorithms right. There are also those other details, like workflow and people processes, along with measurements of success that can include system response time, overall usability, patient outcomes, and more. Reminds me of the important work AHRQ and ONC (and their grantees) did in the 2000's looking at degrees of workflow and data integration for EHRs and clinical decision support modules (CDS). (History really does repeat itself with respect to technology adoption lessons).

"The challenges uncovered by the project point to a dawning realization about AI’s use in health care: building the algorithm is the easiest part of the work. The real difficulty lies in figuring out how to incorporate the technology into the daily routines of doctors and nurses, and the complicated care-delivery and technical systems that surround them. AI must be finely tuned to those environments and evaluated within them, so that its benefits and costs can be clearly understood and compared".


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