It seems as though machine learning and artificial intelligence (AI) are making headlines week after week. Many, including those of us at PointClear Solutions, believe these emerging technologies are poised to revolutionize businesses across a variety of industries. From finance and education to manufacturing and healthcare, we’re all eager to better understand how to leverage these “robots” (figurative and literal) to improve performance, increase efficiency, and reduce costs.
In Healthcare, They’re Already Here
While fully-autonomous, primary care robots – the likes of which are alluded to in sci-fi novels and films – may still be years away, less assuming robots are already being used on a smaller scale to help automate certain tasks, enabling clinicians to focus more time on patients. Robots are serving as virtual receptionists, and “chatbots” (i.e. intelligent personal assistants) are being used for everything from triage to chronic disease management. In addition, hospitals and surgical centers are beginning to utilize purpose-designed surgical robots to assist surgeons in the operating room. In fact, according to a recent report by Allied Market Research, the surgical robotics market is estimated to double from $3 billion in 2014 to $6 billion by 2020. We’ve only begun to see how robots can help humans better serve patients and our healthcare system as a whole.
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What the Future Holds
It’s not totally far-fetched to suggest that, at some point down the road, robots may be capable of performing highly technical medical procedures on their own. There is plenty of research and development being done to integrate collaborative robots into the operating room and, as previously mentioned, many robot-guided machines already exist to help surgeons operate with great precision.
Easier Said Than Done
But, while it’s easy to envision the leaps in progress that can be made by utilizing new technologies fueled by machine learning and AI, it’s not quite as simple when it comes to implementation. Just look at the current state of EHRs / EMRs, patient data, and interoperability. We still have silos of data living in systems that don’t communicate with each other. Data isn’t being captured as completely and accurately as it should be. The regulatory environment makes compiling sensitive data difficult. There is still much work to be done.
Ultimately, the optimization of machine learning and AI (and the reality of fully-autonomous robots) in healthcare will largely depend on how well we capture and integrate the entirety of a patients’ health history and data points in order to provide informed, comprehensive care.
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