Perspective: Physicians should be sole decision-makers

We often hear about various reports on the inefficacy of machine learning algorithms in healthcare – especially in the clinical arena. For instance, Epic's sepsis model was in the news for high rates of false alarms at some hospitals and failures to flag sepsis reliably at others. 

Physicians intuitively and by experience are trained to make these decisions daily. Just like there are failures in reporting any predictive analytics algorithms, human failure is not uncommon. 

As quoted by Atul Gawande in his book Complications, “No matter what measures are taken, doctors will sometimes falter, and it isn’t reasonable to ask that we achieve perfection. What is reasonable is to ask that we never cease to aim for it.” 

Predictive analytics algorithms in the electronic health record vary extensively in what they can offer, and a good percentage of them are not useful in clinical decision-making at the point of care.

While several other algorithms are helping physicians to predict and diagnose complex diseases early on in their course to impact treatment outcomes positively, how much can physicians rely on these algorithms to make decisions at the point of care? What algorithms have been successfully deployed and used by end users?