2 min readA crosswalk doesn’t take offense when signals are ignored because it can’t. The automated machine indicating “walk” or “don’t walk” isn’t a self-aware, human replacement. Despite technological advances—communication, consideration, and compliance still rest with our integrated society at the intersections of daily life.
Today, in an assembly line, a robotic arm repeatedly installs the same car parts as programmed, greatly reducing or eliminating perpetual human intervention. Similarly, machine learning (ML) can eliminate the need for constant human presence on redundant tasks. However, machine learning has the ability to “learn on the job” and determine if the right “parts” are being produced and delivered to the right recipients, at the right time. This helps people to work more safely, efficiently, and profitably—it does not eliminate the need for human involvement.
ML may be able to predictively identify optimal make, model, and color for a buyer—but a human has to tell the machine what a car is first.
How much truer is that with healthcare and the human “vehicle”? What is cancer, diabetes, or influenza? What are approved treatments or under review? What’s the difference between psychiatry and psychology? What does HIPPA stand for and why is it important? How many wearable devices exist currently that may provide patient produced data? AI and machine learning must be told the answers to these questions, and cannot replace a healthcare professional’s ability to explain in human fashion.
With that in mind, a discussion of the digital journey should start with a “people and process” focus before discussing how technology empowers payers and providers to save lives, time, and money while improving healthcare for patients.
To learn more about people-centric approaches to AI and ML, read SoftServe ’s latest white paper, “The Brains Behind Smarter Healthcare”.