• 10May

    How Health Care Changes When Algorithms Start Making Diagnoses

    Sections from an article from Harvard Business Review:

    If this sounds like science fiction, it’s not. It’s what health care might seem like to doctors, patients, and regulators around the world as new methods in machine learning offer more insights from ever-growing amounts of data. Complex algorithms will soon help clinicians make incredibly accurate determinations about our health from large amounts of information, premised on largely unexplainable correlations in that data.

    This future is alarming, no doubt, due to the power that doctors and patients will start handing off to machines. But it’s also a future that we must prepare for — and embrace — because of the impact these new methods will have and the lives we can potentially save.

    Take, for example, a study released today by a group of researchers from the University of Chicago, Stanford University, the University of California, San Francisco, and Google. The study, which one of us coauthored, fed de-identified data on hundreds of thousands of patients into a series of machine learning algorithms powered by Google’s massive computing resources.

    With extraordinary accuracy, these algorithms were able to predict and diagnose diseases, from cardiovascular illnesses to cancer, and predict related things such as the likelihood of death, the length of hospital stay, and the chance of hospital readmission. Within 24 hours of a patient’s hospitalization, for example, the algorithms were able to predict with over 90% accuracy the patient’s odds of dying. These predictions, however, were based on patterns in the data that the researchers could not fully explain.


    This is not to suggest that machine learning models will replace physicians. Instead, what’s likely is a steady shift to ceding responsibility for more of the repetitive and programmable tasks to machines, allowing physicians to focus on issues more directly related to patient care. In some cases, doctors may have a legal obligation to use models that are more accurate than humans expertise, as legal scholars such as A. Michael Froomkin have noted. This won’t take doctors out of the loop entirely, but it will create new opportunities and new dangers as the technology evolves and becomes more powerful.

    How should we ready ourselves for a future in which the burden of diagnosis rests more and more on algorithms?

    First, medical providers, research institutions, and governments must devote more resources to the field of “explainable AI,” whose goal is to help humans better understand how to interact with complex, seemingly indecipherable algorithmic decisions. The Defense Advanced Research Projects Agency (DARPA), for example, has dedicated an entire project to the issue, and a growing research community has sprung up in recent years focused. Such research will be crucial to our ability to put these algorithms to use and to trust them when we do.

    Health care regulators must also explore new ways to govern the use of these methods. Governments must ensure that the massive amounts of data these new methods require don’t become the province of only a few companies, as has occurred in the data-intensive worlds of online advertising and credit scoring.

    Lastly, patients should be able to know when and why their doctors are relying on algorithms to make predictions. When appropriate, patients should retain the ability to request more traditional — and understandable — medical explanations. If an algorithm gives a patient a 90% chance of dying within the next week, for example, the patient should be able to learn more about the ways the algorithm was created, assessed for accuracy, and validated. And they should be able to view the diagnosis alongside a more traditional determination, even if the latter is less likely to be accurate.

    Challenges to using machine learning in health care abound. But these challenges pale in comparison with the benefits these advances will bring. Lives could depend on it.

    You can read the full article here: https://hbr.org/2018/05/how-health-care-changes-when-algorithms-start-making-diagnoses

    If you are interested in this topic, you might be interested in our own event on the topic of Digital Health Information on Tuesday 22 May 2018 at the Royal College of Anaesthetists in London – https://www.pifonline.org.uk/pif/?ee=121