A new patent by the Institute for Genomics and Multiscale Biology at Johns Hopkins University could help improve safety and security in surgical practices. The team’s creation, known as “Artificial Intelligence for Preventing Patient-to-Surgeon Transfusion Errors,” uses predictive analytics and machine learning. They plan on releasing their findings in October.
Essentially, artificial intelligence is the application of artificial intelligence to human problems, similar to how artificial intelligence was used in the past.
Many surgeons now employ the use of electronic laparoscopy, which provides access to the patient as they lie flat in bed. However, patients who have certain medical conditions need to be assisted when they have to be inserted into the operating room. For example, if a patient has pulmonary hypertension or needs to receive coronary bypass, the surgeon has to put the patient into a seated position, which creates new obstacles for the operating room and often adds time to the surgery.
By incorporating artificial intelligence to the procedures, surgeons would have the best access, keeping a patient safe and in the operating room. If the surgeon noticed the risk for a transfusion error, the artificial intelligence would notify the surgical staff and the surgery could be delayed or even stopped.
Another example would be for surgeons who perform hysterectomies, which may produce blood loss for the patient. When a surgeon was using a manual tool for the surgery, blood would sometimes trickle from the incision and often the patient needed to receive additional blood transfusions.
However, artificial intelligence could identify blood loss or have a method to not draw additional blood. These additional blood transfusions can have unwanted consequences to patients.
Perhaps the most interesting part of artificial intelligence is how much of it has been used in the field of medicine already, often for the greater good of the patient.
Earlier this year, researchers used artificial intelligence to predict outcomes for patients who had complex medical conditions. By using machine learning technology, doctors were able to collect large amounts of data, which would then help them learn how to better treat patients.
The artificial intelligence was able to predict outcomes for 92 percent of the patients who were diagnosed with a fatal disease within five years of diagnosis. Even better, many of these artificial intelligence results weren’t even on the team’s radar.
Manage Patients with Incidental Findings
Perhaps even more interesting, was that the artificial intelligence could predict outcomes well within five years for all of the patients diagnosed with this fatal disease within five years of being diagnosed. In light of the data generated from solutions such as Eon Patient Management, according to the researchers, this is unprecedented in the history of medicine.
In other cases, artificial intelligence was being used to help patients. Another example is providing doctors with more information about their patients to help them treat more patients more effectively.
In the past, doctors would often tell a patient they were fine and send them home. If that patient then returned to the hospital after suffering from a cold, doctors would recommend antibiotics, which often caused more problems than the original illness.