One of the recurring bits in Star Trek was the characters’ assessment of 20th-century medicine. They would often call treatments like chemotherapy or surgical removals of tumors barbaric as they tended to injured limbs with miracle devices that would instantly heal wounds and even the worst maladies.
Despite what the intrepid crew of the 23rd century Starfleet might tell you, healthcare has evolved over the years. Treatments have become more targeted and effective, medicine keeps improving its effectiveness, and doctors are more and more able to spot issues before they grow into serious health problems. And while we don’t have biobeds or synaptic stimulators, we do have the power of data and machine learning that would give Gene Roddenberry’s imagination a run for its money.
Let’s be clear: the healthcare industry has an obscene amount of data on each and every one of us. Every broken bone, every checkup, every blood test not only paints a picture of you and your lifestyle, but it’s data that virtually no other source can collect.
The Big Data companies like Google and Facebook might know what you like and what you’re interested in, but they would really love to know why you checked in at the urgent care clinic and suddenly stopped researching “local rugby leagues.”
While the healthcare industry has long taken extensive notes on what makes us tick, it hasn’t always done a great job of parsing that data. And that’s understandable: it’s not exactly a problem exclusive to the healthcare industry. However, now that more and more medical groups have unlocked the healing power of data thanks to machine learning, which led to the creation of predictive healthcare.
Predictive healthcare looks at a number of factors in a patient’s file and uses machine learning to help doctors create a preventative healthcare plan. Different medical systems have experimented with predictive healthcare in different ways, but some of the earliest trends and gains have been made in the cases of:
Hospitals are particularly interested in how predictive healthcare can help palliative care patients. These patients often need the most attention and resources but also experience the most severe health issues. By using predictive healthcare, hospital systems have started developing a better understanding of a patient’s future, which enables medical staff to predict when resources will be needed. With this information, medical staff can also check in on patients proactively.
Machine learning has also aided doctors in diagnosing patients, particularly in the realm of medical imaging. Advancements in medical imaging have helped detect some of our most serious diseases earlier and earlier; however, early screenings means looking for extremely small signs of disease. And as much as doctors seem like superheroes, they are human, and humans can easily mistake a small cluster of abnormal cells for something benign.
With machine learning and advanced algorithms, however, doctors can partner their powerful imaging tools with the power of data to gain confidence in their diagnoses. Medical staff ultimately make the final call, but predictive healthcare can alert doctors and technicians to areas that might need further examination. Someday, predictive healthcare might even reduce the number of biopsies or other tissue-destructive diagnostic tests.
Thanks to machine learning, healthcare systems can offer services ripped straight out of science fiction. It’s a rare instance where everyone wins: patients get better care, hospitals save money and resources, and doctors and medical staff are empowered to be more effective at their jobs. We might not have teleporters and miracle machines, but we are moving forward at warp speed to a better, healthier future.