Technology Record - Issue 28: Spring 2023

VIEWPOINT Innovative life sciences start-ups are using artificial intelligence and machine learning models to better identify illnesses, manage patient care and free up clinician capacity SALLY ANN FRANK: MICROSOFT Improving patient outcomes with AI Regardless of how you experience healthcare – as a provider, staff member, patient, or family member of a patient – all of us see inefficiencies that other, mostly non-regulated industries have overcome. Trying to transform an industry focused on people’s health is more difficult and has more at stake for individuals, populations, providers, life sciences and regulators. In healthcare, we struggle with data interoperability, consistency in care, resource management and effective innovation. Fortunately, there are trailblazers using artificial intelligence and other technologies to overcome these seemingly insurmountable challenges in healthcare. Signal 1 is a health AI start-up with the mission to transform patient care through responsibly deployed AI. The company provides hospitals with an end-to-end solution for integrating AI-driven insights into existing hospital workflows. Its most popular application is a clinically validated, real-time automated patient discharge predictor that has been developed and deployed at Grand River Hospital in Waterloo, Canada. This solution helps hospitals to improve quality and flow while reducing stress on frontline care providers. Discharge planning for admitted hospital patients involves many multi-disciplinary teams, including physicians, nurses and social workers. These teams have limited resources and are difficult to co-locate to coordinate discharge planning for each patient. This frequently leads to communication gaps, misaligned priorities and delays in resolving each patient’s discharge barriers which results in delays that extend a patient’s stay. Staffing challenges have exacerbated this issue, creating a situation where stretched resources are working in a very reactive environment while hospitals struggle to free up the necessary capacity. Many of the challenges in coordinating these teams relate to understanding which patients are approaching discharge readiness ahead of time, so that resources can be allocated appropriately to remove barriers in parallel with this approaching date. This is where the power of a machine learning-based risk predictor comes in. By studying historical datasets, the ML algorithm can uncover subtle relationships between a patient’s physiological data over time and their timeline until reaching clinical stability. The evidence is mounting that ML is superior to alternative approaches when it comes to addressing this problem. Signal 1 provides an ML discharge predictor that enables hospital clinicians to be more proactive and 164