Brian C. Berry
The very idea of Artificial Intelligence (AI)—machines learning on their own and adapting to change—sounds like a daunting one. Concerns can range from the pragmatic, such as the risks of taking decision-making out of human hands, to the far-fetched, such as the old science fiction idea of “the robots taking over.”
But we shouldn’t be afraid of AI, rather we should embrace it for what it is—the next great technological frontier in the business world. Because at its very core AI is about machines augmenting the work of humans and making it better, rather than replacing human intelligence. And one area that right now is at the epicenter of the advancements of AI and machine learning is the healthcare field.
Simply put, tremendous advancements are being made on a daily basis in the medical field thanks to the ability to integrate machine learning into patient care, thus giving health care providers the highly advanced tools they need to recognize symptoms earlier, diagnosis conditions earlier and work much faster to begin the necessary treatment. The benefits to the patient are myriad, and at the center of everything is still human expertise leading the way, only now being helped by AI.
As an example, IBM’s Watson computer has been used by Memorial Sloan Kettering in New York to process images and text transcriptions from doctor’s research papers on treatments for cancer and other diseases. This has helped to more rapidly identify patterns among patients and get them on the road to treatment much sooner. The technology is complex but the formula is fairly simple—the education and experience of the doctor takes the lead, and the technology helps to expedite the process to the benefit of the patient.
Some hospitals and medical offices are already using AI and Internet of Things (IOT) in ongoing patient care, with patients wearing monitors after leaving the hospital (monitoring areas such as blood pressure, heart rates and others) so they can be tracked remotely while staying within the comfort of their homes. Without machine learning and the advances brought about by AI, this would not be possible—we are seeing much greater efficiency in how patients are cared for.
Machines will never replace doctors, and no patient should ever have to worry about that happening; there is too much advanced human learning, too much advanced education and too much hands-on experience for that to ever happen. But machines will raise awareness to health care professionals and assist them in caring for more patients more efficiently and effectively.
The advantages to both the patient and the medical communities are palpable. By using this technology to monitor patients, we are seeing evidence that hospital readmissions have been reduced, mostly by being able to identify patient risk factors and then monitor them once they are sent home. These machine learning-based advancements can also be integrated with predictive models for planning patient discharges, based on various actuarial tables and models in the insurance industry, creating a much more comprehensive system of full patient care.
The use of AI and machine learning is having a positive impact on patient care in nursing homes as well. Many patients usually come to senior-based communities after being referred there by hospitals, which inform them prior to the intake process of that patient’s conditions and risk factors. This gives the nursing home a sense of what types of care are needed, but traditionally they don’t know what specific services are required or what the long-term needs are, which presents challenges that can be burdensome. However, by using machine learning, nursing facilities can create predictive models for clusters of residents, which could create a much better idea of the services required, and this model can be adapted for future use. Once more it’s the same formula—human expertise plus machine learning equals better patient care.
This is not question anymore of if or when AI and machine learning will enter the medical profession; it’s here now—and hospitals, medical offices, nursing homes and practitioners are adapting to it. It will never be about machines replacing human know-how, but rather about how machine learning can accelerate and improve decision-making processes in patient care.
Brian C. Berry is a Director with BlumShapiro, the largest regional business advisory firm based in New England, with offices in Connecticut, Massachusetts and Rhode Island. The firm, with a team of over 500, offers a diversity of services, which include auditing, accounting, tax and business advisory services. Blum serves a wide range of privately held companies, government and non-profit organizations and provides non-audit services for publicly traded companies. To learn more visit us at blumshapiro.com.