Anomaly Detection in Healthcare: Detecting Erroneous Treatment Plans in Time Series Radiotherapy Data

Abstract

Adverse events in healthcare and medical errors result in thousands of accidental deaths and over one million excess injuries each year. Anomaly detection in medicine is an important task, especially in the area of radiation oncology where errors are very rare, but can be extremely dangerous, and even deadly. To avoid medical errors in radiation cancer treatment, careful attention needs to be made to ensure accurate implementation of the intended treatment plan. In this paper, we describe the work that resulted in a valuable predictive analytics tool for automatic detection of catastrophic errors in cancer radiotherapy, adding an important safeguard for patient safety. We designed a method for Dynamic Modeling and Prediction of Radiotherapy Treatment Deviations from Intended Plans (SmartTool) to automatically detect and highlight potential errors in a radiotherapy treatment plan, based on the data from several thousand prostate cancer treatments that were used to build the model. SmartTool determines if the treatment parameters are valid, against a previously built Predictive Model of a Medical Error (PMME). SmartTool communicates with a radiotherapy treatment management system, checking all the treatment parameters in the background prior to execution, and after the human expert QA is completed. Any anomalous treatment parameters are detected using an innovative intelligent algorithm in a completely automatic and unsupervised manner, and it flags the operator by highlighting the suspect parameter(s) for human intervention. Furthermore, the system is self-learning and constantly evolving, and the model is dynamically updated with the new treatment data.