| | November 20178CIOReviewIn my opinionCHANGING TECHNOLOGY PARADIGMS MACHINE LEARNING IN THE HOSPITAL SETTINGBy Dustin Roller, VP, Innovation Technology, ASD Healthcare, a part of AmerisourceBergenAs consumer technology continues to progress at an incredible rate, the healthcare field is experimenting with how these advances can shape patient care. Investment in healthcare technology is at an all-time high, with private equity and corporate venture capital in the digital health sector growing 74 percent in the first three-quarters of 2016 compared to the same time period in 2015. Consumer technologies, applied in health care, can help tackle some of the industry's toughest challenges and work in tandem with human resources to provide high-quality care.The most exciting part about how quickly technology develops is that we no longer have to wait for the hardware and software to catch up to our ideasall of the equipment exists now. For example, the mechanics of your in-home security camera, applied in the healthcare space, could prevent a nurse from accidentally providing a patient with incorrect medication. The code that helped IBM's Watson beat Ken Jennings and Brad Rutter at Jeopardy! could help care teams identify the best course of treatment for individual patients, based on their medical history and outcomes from similar patient populations. All of these programs utilize machine learning, a technology that has the power to revolutionize health care. Machine learning's ability to continually absorb information and improve itself as it gains access to more data and experiences is being used across industries to tackle some of today's biggest challenges. In healthcare, companies are beginning to utilize machine learning to help clinicians detect breast cancer metastases in lymph nodes, develop a tool to help prevent blindness in patients with diabetes and identify people at high-risk for cardiac arrest.To capitalize on machine learning technology, the healthcare field needs to continue to evolve with these innovations. One of the first places where evolution can occur is in the health system. Hospitals are excellent proving grounds for new healthcare technologies, as they are a microcosm of the greater health care system: patients, providers, payers, caregivers and manufacturers all have a presence in the hospital setting. Right now, the current technology in use by hospitals does not adapt on its own; it must first be programmed to respond to a specific trigger. For example, if a patient presses the call button for a nurse, that technology is programmed to respond to the action by triggering a light or sound at the nurses' station. Much, if not all, of the technology used in a hospital, operates in this type of closed system where tools' functions do not evolve without manual program updates. In a machine learning system, programs learn on their own and become smarter and more responsive over time. No one needs to program the computer to alert the nurses' station when a patient presses the call buttonit has learned to identify certain characteristics of a Dustin Roller
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