Machine learning in Health Care has recently gained more popularity. Companies like Google have developed machine learning algorithms and software (one is known as CRISPR) to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer. A recent JAMA article reported the results of a deep machine-learning algorithm that was able to diagnose diabetic retinopathy in retinal images. It’s clear that machine learning has greatly advanced clinical decision making in the healthcare industry.
Still, machine learning in healthcare lends itself to some processes better than others. Algorithms can provide immediate benefit to disciplines with processes that are reproducible or standardized. Also, those with large image datasets, such as radiology, cardiology, and pathology, are strong candidates. Machine learning can be trained to look at images, identify abnormalities, and point to areas that need attention, thus improving the accuracy of all these processes. Long-term, machine learning will benefit the family practitioner or internist at the bedside. Machine learning can offer an objective opinion to improve efficiency, reliability, and accuracy.
Providers of healthcare software like Health Catalyst, use a proprietary platform to analyze data and loop it back in real time to physicians to aid in clinical decision making. At the same time a physician sees a patient and enters symptoms, data, and test results into the EMR, there’s machine learning behind the scenes looking at everything about that patient, and promoting the doctor with useful information for making a diagnosis, ordering a test, or suggesting a preventive screening. Long term, the capabilities will reach into all aspects of medicine as we get more usable, better-integrated data. We’ll be able to incorporate bigger sets of data that can be analyzed and compared in real time to provide all kinds of information to the provider and patient.
The Ethics of Using Algorithms in Healthcare
When it comes to machine learning, there have been various conversations on the ethics of it. It’s been said before that the best machine learning tool in healthcare is the doctor’s brain. Could there be a tendency for physicians to view machine learning as an unwanted second opinion? At one point, factory workers feared that robotics would eliminate their jobs (a fear that is now coming to pass gradually). Similarly, there may be physicians who fear that machine learning is the beginning of a process that could render them obsolete. But it’s the art of medicine that can never be replaced. Patients will always need the human touch and the caring and compassionate relationship with the people who deliver care. Neither machine learning, nor any other future technologies in medicine, will eliminate this (or will it?), but will become tools that clinicians use to improve ongoing care.
The focus should be on how to use machine learning to augment patient care. A machine learning algorithm that can review the pathology slides and assist the pathologist with a diagnosis, is valuable. If I can get the results in a fraction of the time with an identical degree of accuracy, then, ultimately, this is going to improve patient care and satisfaction.
Healthcare needs to move from thinking of machine learning as a futuristic concept to seeing it as a real-world tool that can be deployed today. If machine learning is to have a role in healthcare, then we must take an incremental approach. We must find specific use cases in which machine learning capabilities provide value from a specific technological application (e.g., Google and Stanford).
Constant research into machine learning in healthcare will lead to a step-by-step pathway to incorporating more analytics, machine learning, and predictive algorithms into everyday clinical practice.