Machine Learning – why we don’t want to trust

Ruth Smith
April 23, 2019

Old ways of thinking are hard to let go. Growth often means moving past our old ways of thinking but sometimes we are reluctant to hand over control to machines. It is much easier to get behind leaders who trust their gut instincts than to trust that machines are capable of seeing complex relationships that we don’t and can simulate outcomes that we never can. This year has allowed me to start trusting machines a bit.  

There is an evolution in machine learning within healthcare occurring right now.  Machine learning promises to help physicians make more accurate diagnoses, predict readmissions, provide evidence based care options and improve patient care, all while reducing cost. There are many hurdles to overcome including legal, technical and medical but the biggest obstacle might be the lack of trust in machine learning.

Healthcare is an evolving frontier in the use of machine learning. Other industries such as financial and retail have long used machine learning to predict outcomes. Google is probably the best known of all examples of machine learning. Every time you search for something, Google watches your response, determining how long you stay on a site after a search and using that information to build the accuracy of future searches. Amazon and Netflix use machine learning algorithms to make recommendations for products to buy and shows to binge watch by comparing to millions of other searches. The next frontier we are reading more about is smart cars. More than 74% of top auto industry executives expect to see smart cars on the road by 2025 based upon an IBM survey.  Healthcare costs are soaring and insurance companies as well as individuals have a vested interest in machine learning and artificial intelligence to keep consumers healthier and reduce costs. There will be more disruptive technologies come out of the healthcare sector in the next ten years according to Forbes magazine.

Over the course of this past year, my experience has shown that the information provided by machine learning, combined with the skills of nurses and doctors has provided better outcomes.   Machines can never replace the eyes and hands of skilled medical professionals but the combination of highly accurate information with good medicine is a win for patients and healthcare providers.  As the cost to big data implementation and cloud computing power come down, adoption of machine learning within healthcare will go up. Machine learning can take out the drudgery for healthcare professionals, leaving them more time to do the really important work: communicate with their patients.  Imagine the ability to automate the treatment decision process, auto-populate order sets and predict readmissions so that healthcare professionals can make better use of their time. Let machine learning algorithms do what they do best and let humans do what they do best.

Ruth Smith

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