3 eye-opening findings from AI in Healthcare’s 2020 Leadership Survey

AI in Healthcare recently published its 2020 Leadership Survey, asking more than 1,200 physicians, executives, IT specialists and healthcare professionals about AI and how it might impact the future of patient care. The survey results were fascinating—click here to read about seven key findings—and there were some smaller takeaways that may be worth exploring in greater detail.   

These are three relatively small findings from the survey that deserve additional attention:

1. IT departments are helping foot the bill

While administrators hold the financial responsibility for AI investments 43% of the time, IT departments are actually paying for the technology at 26% of facilities. It isn’t necessarily a shocking development—AI will affect IT employees as much as anyone—but it does show that health systems are truly embracing the potential of these solutions. Also, it’s hard to make concrete changes at any business without the full support of your IT department. This statistic shows that everyone is in on this together, a positive sign for anyone hoping to see AI implementation continue to rise.

2. The more the merrier? Some health systems are already using more than 50 AI applications

Of the survey respondents currently using AI in clinical practice, a vast majority (89%) are utilizing somewhere between one and 10 applications. Another 9% are using 11-50 AI applications. Two percent, however, are using more than 50 AI applications—a number big enough to make your jaw drop to the table.

How are health systems managing this many applications? How are they getting the funding for so much new technology? These questions—and more!—immediately come to mind.

3. Health systems want their AI and EMR to work together

Survey respondents were asked about their top priorities when it comes to the development of AI solutions. Fifth on that list? They want to be able to use EMR data to predict patient outcomes.

Researchers around the world are currently exploring this concept, but the early results seem to indicate that there is still some significant work to be done before AI and EMRs can truly detect at-risk patients as effectively as providers want. One recent study, for example, found that applying AI to EHR data could help doctors predict AFib—but the EHR-fueled AI model was not “substantially better” than a simpler, more direct model.

“Further work is needed to explore the technical and clinical applications of this model to improving outcomes,” the study’s authors concluded.

For now, that seems to be the most common answer when it comes to using EMR data for predicting outcomes. As time goes on, however, the sky is the limit when it comes to transforming patient care through the power of AI.