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Featured Post

by Dr. Nader Elshehabi
Published on February 12, 2018

Would you be happy to fly in a plane without a pilot? Or travel in a driverless car? Would a human backup make any difference in your comfort level?

Apply the same thought to healthcare. How comfortable would you be under a completely autonomous surgical blade? Now compare this to an overworked, sleep deprived human surgeon, at the end of a 24 hour shift.

Fatigue has been recognized as a problem in the healthcare industry for a long time. Clinicians are overworked, understaffed, sleep deprived; and are working in a very challenging environment. Making hundreds of split second decisions that will impact life every day. Not to mention the very complex nature of what they deal with; the human body!

While healthcare cannot yet be completely autonomous, technology plays a major role today in improving outcomes, increasing safety and reducing cost of care. If we look at the primary source of fatigue in clinicians, it’s not just about long hours of work, or short hours of sleep. It’s also decision fatigue. Here is where computer software can bring a lot of value as a clinical decision support system.

In its most basic form, clinical decision support (CDS) provides clinicians, staff, patients and other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and healthcare. Examples are reminders, alerts and evidence links embedded within the electronic medical record in various workflow points to guide the clinicians’ decision-making process.

A more advanced form of such systems is the analytical form. Where many of the Radiology Information Systems contain post-acquisition analysis modules that can spot pathology, perform 3D reconstruction of 2D images, and even analysis ECG waveforms to provide interpretation.

The form that takes CDS to the next level, is providing various levels of recommendations, a suggested course of action and interactive assistance personalized to the patient’s condition. When physicians document a finding, CDS would suggest a diagnosis.

When they document a diagnosis, the system would suggest a treatment plan. How the suggestion is made, and whether it’s only a proposal pending human approval, or an actual automatic ordering of management, is a debate.

Some CDSs can also monitor the patient’s vital signs and other clinical assessments and highlight subtle changes in the patient’s condition that a human can miss. It would guide the clinicians through the assessment process, start to finish, and provide recommendations at the end.

Other tools provide guided management of complex conditions across multiple visits, medical services and disciplines, to facilitate multiple clinicians providing care tailored to the patient’s condition, progress and response to treatment.

At the end, the most important decision such systems should support, is how to make them better. Such systems provide a wealth of information, such as what recommendations were given to the clinicians? How were they given? Did the human provider override the recommendations? And finally, what was the outcome of the decision? This needs to feed back into machine learning algorithms and artificial intelligence modules to learn, grow and provide design future systems that are faster, safer, more accurate and able to provide better decision support to clinicians.

We’ve seen very advanced AI modules in areas like, aviation, transportation, finance and even gaming and entertainment industries that learn, evolve and excel on their own. When are we going to see an equivalent investment in the use of AI in healthcare?

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