Development and Validation of a Bayesian Network Method to Detect External Beam Radiation Therapy Physician Order Errors

Abstract

Purpose: To investigate a Bayesian network (BN)-based method to detect errors in external beam radiation therapy physician orders.

Methods and materials: A total of 4431 external beam radiation therapy orders from 2008 to 2017 at the authors' institution were obtained from clinical treatment management systems and divided into 3 groups: single prescription, concurrent boost, and sequential boost. Multiple BNs were developed for each group to detect errors in new orders using joint posterior probabilities of the order parameters, given disease information. Each BN was trained with a group of orders using a Bayesian learning algorithm. A procedure was developed to select the optimal BN for each treatment site in each group and to determine site-specific parameters and error detection thresholds. Potential clinical errors, created both manually and automatically, were applied to test error detection performance.

Results: The average true-positive rate (TPR) and false-positive rate (FPR) of error detection were 95.72% and 1.99%, respectively, for the single-prescription cohort with 9 treatment sites. For the concurrent-boost cohort, the TPR and FPR were 92.94% and 14.53%, respectively. For the sequential-boost cohort, the TPR and FPR were 100% and 9.48%, respectively, for the prescribed dose values and 100% and 4.34%, respectively, for the remaining order parameters. For the patient simulation and imaging parameters for 9 treatment sites, the TPR and FPR were 100% and 4.96%, respectively.

Conclusions: The probabilistic BN method was able to perform physician order error detection at a higher accuracy than previously reported in a variety of complex prescription instances, thus warranting further development in incorporating BNs into clinical error detection tools to assist manual physician order checks.