A digital physician peer to automatically detect erroneous prescriptions in radiotherapy

Appropriate dosing of radiation is crucial to patient safety in radiotherapy. Current quality assurance depends heavily on a physician peer-review process, which includes a review of the treatment plan's dose and fractionation. Potentially, physicians may not identify errors during this manual peer review due to time constraints and caseload. A novel prescription anomaly detection algorithm is designed that utilizes historical data from the past to predict anomalous cases. Such a tool can serve as an electronic peer who will assist the peer-review process providing extra safety to the patients. In our primary model, we create two dissimilarity metrics, R and F. R defining how far a new patient's prescription is from historical prescriptions. F represents how far away a patient's feature set is from that of the group with an identical or similar prescription. We flag prescription if either metric is greater than specific optimized cut-off values. We use thoracic cancer patients (n = 2504) as an example and extracted seven features. Our testing set f1 score is between 73%-94% for different treatment technique groups. We also independently validate our results by conducting a mock peer review with three thoracic specialists. Our model has a lower type II error rate compared to the manual peer-review by physicians.

Automated summary

Appropriate dosing of radiation is crucial to patient safety in radiotherapy. A novel prescription anomaly detection algorithm is designed that utilizes historical data to predict anomalous cases, providing extra safety to the patients.