As a new intern, I remember frequently making my way to the Emergency Department for a new admission; “Chest pain,” the attending would tell me before sending me to my next patient. Like any good intern I would head directly to the paper chart where I knew the EKG was supposed to be waiting for me, already signed off on by the ER physician. Printed in standard block print, “Normal Sinus Rhythm, No significant ST segment changes” I would read and place the EKG back on the chart. It would be later in the year before I learned to ignore that pre-emptive diagnosis or even give a thought to about how it got there. This is one of many examples how machine learning has started to be integrated into our everyday life in medicine. It can be helpful as a diagnostic tool, or it can be a red herring.
Machine learning is the scientific discipline that focuses on how computers learn from data and if there is one thing we have an abundance of in medicine, data fits the bill. Data has been used to teach computers how to play poker, learn laws of physics, become video game experts, and provide substantial data analysis in a variety of fields. Currently in medicine, the analytical power of machine learning has been applied to EKG interpretation, radiograph interpretation, and pathology specimen identification, just to name a few. But this scope seems limited. What other instances could we be using this technology in successfully? What are some of the barriers that could prevent its utilization?
Diagnostic tools are utilized in the inpatient and outpatient setting on a regular basis. We routinely pull out our phones or Google to risk stratify patients with ASCVD scoring, or maybe MELD scoring in the cirrhotic that just got admitted. Through machine learning, these scoring systems could be applied when the EMR identifies the correct patient to apply it to, make those calculations for the physician, and present it in our results before we even have to think about making the calculation ourselves. Imagine a patient with cirrhosis who is a frequent visitor to the hospital. As a patient known to the system, a physician has at some point keyed in the diagnosis of “cirrhosis.” Now, on their next admission, this prompts this EMR to automatically calculated and provide a MELD Score, a Maddrey Discriminant Function (if a diagnosis of “alcoholic hepatitis” is included in the medical history). The physician can clinically determine relevance of the provided scores; maybe they are helpful in management, or maybe they are of little consequence depending on the reason for admission. You can imagine similar settings for many of our other risk calculators that could be provided through the EMR. While machine learning has potential far beyond this, it is a practical example where it could easily be helpful in every day workflow. However, there are some drawbacks to machine learning.
Some consequences of machine learning in medicine include reducing the skills of physician, the lack of machine learning to take data within context, and intrinsic uncertainties in medicine. One study includes that when internal medicine residents were presented with EKGs that had computer-annotated diagnoses, similar to the scenario I mentioned at the beginning of this post, diagnostic accuracy was actually reduced from 57% to 48% went compared to a control group without that assistance (Cabitza, JAMA 2017). An example that Cabitza brings up regarding taking data in context is regarding pneumonia patients with and without asthma and in-hospital mortality. The machine-learning algorithms used in this scenario identified that patients with pneumonia and asthma had a lower mortality, and drew the conclusion that asthma was protective against pneumonia. The contextual data that was missing from the machine learning algorithm was that the patient with asthma who were admitted with pneumonia were more frequently admitted to intensive care units as a precaution. Intrinsic uncertainties in medicine are present in modern medicine as physician who have different opinions regarding diagnosis and management of the same patient based on their evaluation. In a way, this seems like machine-learning could be both an advantage and disadvantage. An advantage this offers is removing physician bias. On the same line of thought, it removes the physician’s intuition.
At Wellpepper, with the Amazon Alexa challenge, machine learning was used to train a scale and camera device (named “Sugarpod“) in recognizing early changes in skin breakdown to help detect diabetic foot ulcers. Given the complications that come with diabetic foot ulcers, including infections and amputations, tools like this can be utilized by the provider to catch foot wounds earlier and provide appropriate treatment, ideally leading to less severe infections, less hospitalizations, less amputations, and lower burden on healthcare system as a whole. I believe these goals can be projected across medicine and machine learning can help assist us with them. With healthcare cost rising (3.3 Trillion dollars in 2016), most people can agree that any tools which can be used to decrease that cost should be utilized to the best of its ability. Machine learning, even in some of its simplest forms, can certainly be made to do this.