After heart disease and cancer, medical errors are the third leading cause of death in the US. In an era of where computer-aided or automated decision making is becoming the industry standard, it is staggering to learn that many quality assurance and error detection processes are done manually by humans.
The reason for this is in the perceived complexity of the tasks and systems being checked. An expert medical professional can quickly identify difficult-to-quantify errors and make the corresponding corrections. However, not every error is noticed, or even checked.
As treatment complexity increases, and medical professionals are required to spend additional time assessing more and more voluminous data, the effectiveness of having humans responsible for policing the entire treatment workflow diminishes.
At the heart of using AI for human error detection and prevention is: can we use AI techniques to identify difficult-to-quantify errors as a human expert does? Current industry efforts are being made to automate patient treatment plan checking, but are largely centered on human-programmed rule-based checks.
In order to develop for a comprehensive error checking solution, we explore more advanced learning methods. Our goal is to develop an AI-based error detection system that can reside in any electronic medical record or treatment management systems to automatically detect any medical errors.