| | February 20198UTLOOK Healthcare Tech HEALTHCARE TECHNOLOGY KNOWLEDGE NETWORKBy Mark Hoffman, Chief Research Information Officer, Children's Mercy Kansas CityCurrent health practice is primarily based on heuristic decision trees that are established from centuries of medical experience and scientific evidence. A patient presents with a set of symptoms. A highly trained provider reviews the patient history, physical exam and findings from lab results, imaging studies and other evaluations. The provider compares this information to their formal education, their practical training, their personal experience, perhaps consults with their colleagues or reviews recent literature, and then recommends a course of action for the patient. If the initial recommendation doesn't resolve the concern, they may engage specialists. Human beings are present in every aspect of this process, from generating the scientific evidence included in the formal education to performing the diagnostic tests and establishing payment policies that influence health decisions.The emergence of new technologies, including the various forms of machine learning and deep learning, is challenging the healthcare paradigm described above. Recent work in machine learning has demonstrated that algorithms can process images and detect pneumonia with accuracy comparable to a radiologist. Substantial progress is being made in the development of algorithms that can process electronic health record (EHR) data and identify patients at risk of developing conditions such as sepsis. The growth of genomic medicine requires computational aids to assist in the recognition of DNA variants that may be clinically significant. Healthcare workforce issues, the rising cost of healthcare and the limits of human cognition and perception combine to justify the impressive work computational work. Often the end goal of this work is represented as "next generation decision support."The Last Mile: Policy Considerations for the Adoption of AI in HealthcareIN MY OPINION
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