Computers are everywhere it seems, even in our healthcare. While they aren’t quite at the level of (find some movie reference with something futuristic) they are making significant contributions. One of those contributions is algorithms which are contributing in areas from imaging, diagnosing, and predicting.
To help solve dilemmas such as this, healthcare professionals are increasingly turning to algorithms, which use machine learning techniques that enable computers to learn information without human input. Algorithms create a formulaic process for healthcare professionals to evaluate patient symptomology and decide on the best course of treatment. While they cannot replace human decision-making or medical expertise, algorithms can help guide doctors and nurses through a logical thought process. An ideal algorithm is structured to help prevent flawed decisions that can harm patients.
Taking available input concerning a patient’s age, weight, risk factors, and symptoms, algorithms can offer the probability of whether a patient has a given condition. A basic example of this can be seen with online services such as WebMD that allow people to enter symptoms and quickly obtain a preliminary diagnosis. At a much higher level, algorithms can monitor hospital patients and predict when a patient is at high risk of deteriorating or going into cardiac arrest. Such algorithms are oftentimes complex versions of “decision trees,” where the algorithm’s judgment is based on answers to many yes/no questions. This method minimizes the potential for bias by using a formulaic and straightforward process to diagnose medical conditions.
Algorithms can be used for much higher-level diagnostics as well. Algorithms excel at detecting patterns and can analyze large amounts of information quickly, making them perfect for predicting diagnosing many types of conditions. For example, researchers at the U.C. San Francisco created an algorithm that scans echocardiogram images, looking for heart issues. The algorithm achieved an accuracy rate over 10% higher in detecting heart issues from these images than human doctors. Algorithms can even predict human behavior in some cases, with a Vanderbilt University Medical Center algorithm being able to predict with 84% accuracy whether a patient admitted for self-harm or suicide attempts would try again within two weeks. Algorithms such as these can be helpful predictive tools, allowing doctors to tailor their treatment to best serve patients.
Several important algorithms have been developed at Emory, including one deployed during the 2009 H1N1 pandemic that prevented unnecessary patient visits. Patients experiencing flu symptoms were asked to input their age and answer a few questions about their symptoms. The algorithm could then determine their risk of developing complications and recommend whether they seek medical attention or stay home. Emory Healthcare has deployed a similar tool during the COVID-19 pandemic. Those exhibiting COVID-symptoms are asked to visit C19check.com, which assesses their risk of serious illness based on several questions. The website has helped Emory manage emergency room capacity by allowing doctors to recommend recovery from home for low-risk patients.
Algorithms developed at Emory have had substantial impacts on other areas of healthcare as well. One algorithm allows for more precise measurements during eye scans, which can greatly improve the accuracy of such scans in picking up signs of Alzheimer’s and dementia. Another created a uniform protocol system for blood transfusions, allowing doctors to track and monitor adverse reactions. Finally, a third improves machine learning itself, creating stronger neural networks that give algorithms greater accuracy and learning ability.
With algorithms having such a substantial impact on healthcare, a question that inevitably arises is how liability is determined. If a healthcare provider misdiagnoses or mistreats a patient, it is easy to establish the party at fault. If a machine does the same, the issue becomes much more complex. While there are no laws specifically pertaining to medical algorithms, manufacturers can be held liable under general product liability law. The Consumer Protection Act of 1987 allows plaintiffs injured by defective products to sue. Under this act, patients can recover damages if they prove that an algorithm is defective in some way. However, significant legal grey areas still exist. Creators of medical algorithms may be shielded from liability their algorithms go through an FDA approval process. Under the concept of preemption, the Supreme Court has ruled that in some, but not all cases, manufacturers of medical products cannot be held liable state courts if their product received FDA approval.
More specific regulations by the Food and Drug Administration (FDA) covering algorithms may be forthcoming. Currently, most medical devices require premarket approval by the FDA. Since these regulations were implemented before the development of machine learning techniques, algorithms fall into a grey area and do not generally require such approval. In September 2019, the FDA published a draft guidance regulatory framework for algorithms, which would create an approval process for the software. Since machine-learning algorithms constantly change and adapt, FDA approval would not be needed for every modification. Instead, the manufacturer would have to provide a “predetermined change control plan” to the FDA, containing the algorithm’s underlying methodology and anticipated changes. Only software for high-risk medical conditions would be covered by these regulations, and algorithms for self-diagnosis of low-risk medical conditions would remain unregulated.
From helping prevent suicide, to diagnosing heart conditions, to calibrating treatments for patients in ICU units, algorithms are now a critical component of our healthcare system. Just like human judgment, algorithms are not infallible. Used correctly however, they can make our healthcare system safer and more efficient. Look for algorithms to take on new roles and an even more active role in patient care in the future as artificial intelligence continues to advance.
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