Four Types of AI And Why it Matters for Health Care

Image of AI robot

Artificial Intelligence (AI) is catalyzing change across industries worldwide. This is especially true for healthcare, as AI ushers in a new era of rapid automation, broad access and a higher quality of care for patients. Physicians and other medical practitioners are reaping the benefits too, as AI machines remove the physical and human constraints that limit speed, complexity and precision of care delivery. How? I’m glad you asked. Here’s how four distinct types of AI are revolutionizing health care:

Cognitive Robotics

Cognitive robotics go beyond motor skills and physical movement, using evolving perceptions to formulate new ideas, plan new actions and learn from the results. Adapting to their environment, cognitive robots can make predictive decisions and in-the-moment adjustments through their exceptional capabilities to embody cognition. In health care, this has a wide-range of applications, from robots that aid surgeons--or conduct complex operations on their own with a greater rate of precision. Cognitive robotics can also be used in healthcare settings for repetitive tasks such as physical therapy or rehabilitation.

Intelligent Automation

Today, the volume of data available to medical providers is increasing every minute. Enter intelligent automation, which is enabling more seamless and automated care delivery and administration by sorting through data to develop more personalized treatments for patients. Intelligent automation has the ability to complement human operations using speed and scale to deliver faster, more accurate results. The result? Digital robots that can interact with patients during check-in for medical appointments, and virtual care providers answering questions based on symptoms and other information supplied by patients. Rather than replacing people, intelligent automation equips healthcare workers to operate more effectively in roles where humans excel.

Machine Learning

Speaking of mining through data, machine learning enables complex math calculations to be applied to larger data sets repeatedly, and with increasing speed. As computers learn to recognize things, ongoing data continually updates machine learning models. This scalable, accurate and accessible data--often collected by wearable devices and sensors--can be used to help medical care providers quickly analyze trends or identify red flags in data, leading to improved diagnosis and treatment at the point of care. Today, machine learning is supplementing radiologist skills by picking up subtle changes in imaging scans, potentially leading to earlier diagnoses of life-threatening diseases. Likewise, doctors are able to address risks quicker, referencing all kinds of patient history information through predictive analytics in order to improve clinical and behavioral outcomes.

Robotic Process Automation

Finally, robotic process automation (RPA) refers to computer systems that are capable of automating activities that previously required human judgment. This automation applies to operational tasks that result in cost reduction, better efficiency and improved analytics. It also results in increased quality, engagement and innovation as healthcare workers can focus on deeper patient interactions rather than mundane activities. Use cases for RPA include claims administration, insurance member management and provider management, improving processes for things like account enrollment, billing management, credentialing and customer service.

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So, are humans going to be out of healthcare jobs? Not at all. Rather, AI and humans will work side-by-side to deliver a higher quality, lower cost of care that focused on personalized treatment for patients. Consider AI the new co-worker you’ve always wanted.

Kelly M. Hamilton is Digital Communications and Operations Lead for MedStar Health’s Digital team. In this role, Kelly supports Digital leadership in strategy, coordination and execution of digital initiatives at MedStar. With responsibility for planning and implementing the overall digital communication strategy, she utilizes a variety of communication vehicles to articulate the Digital team’s business strategy and priorities among internal and external stakeholders.