Is Personalized Healthcare Here?
Last week, at Microsoft’s Build event, the technology company made many big announcements such as Project Volterra, Microsoft Intelligent Data Platform, and Microsoft Dev Box, among many others. I covered these in-depth yesterday on The Peggy Smedley Show and shared my thoughts about the impact it will have. Perhaps one of the best ways to explore this is with a case study, and since our topic of discussion for the month of May is healthcare, let’s look at one example.
We all know the COVID-19 pandemic has exasperated the healthcare industry. Some stats suggest more than 6 million people in England are waiting for treatment by the National Health Service, due to staff shortages and other reasons. Many recognize this is a problem—and some are taking steps to address it with innovation.
Consider what is happening in the United Kingdom. The government is investing 36 billion pounds—or $44 billion—in health and social care in the next three years to embrace innovation and cut down on these waiting lists. Some examples include virtual wards and AI (artificial intelligence).
One case comes from a team of medical professionals who are leveraging AI to reduce wait times and provide patients with better information. Orthopedic surgeons Justin Green and Mike Reed at Northumbria Healthcare NHS Foundation Trust—a hub for joint replacements—have developed an AI model that helps consultants give their patients a personalized risk assessment of upcoming hip or knee operations.
With the AI model being hosted in Microsoft’s Azure cloud and uses the Responsible AI dashboard in Azure Machine Learning, medical professionals are given a clear understanding of why the AI reaches certain conclusions. Green and Reed have used the tool in a small number of interactions with patients who need hip and knee operations, but they believe it can be applied to most areas of healthcare.
This insight is only possible because the model runs in Microsoft’s Responsible AI dashboard, which assists AI developers with the fairness, interpretability, and reliability of AI models. Within the dashboard, the tools can communicate with each other and show insights in one interactive canvas to help with debugging and decision-making.
It’s an interesting tool to consider in healthcare. Many doctors treat patients based on a general understanding of the patient and the condition—but as we know every situation is unique. This AI tool can help personalize the healthcare process, creating a custom risk assessment.