A digital physician peer to automatically detect erroneous prescriptions in radiotherapy

Abstract

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.

A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study

“…Previous studies have shown that artificial intelligence tools using ML algorithms can improve treatment, enhance quality of care and patient safety, reduce burden on providers, and generally increase the efficiency with which resources are used, resulting in potential cost savings or health gains [ 7 32 35-38 undefined undefined undefined ]. In addition, our findings align with those of previous studies that highlight the potential of ML applications to predict individual patients’ risk of specific medical conditions and associated complications to offer specialized care programs to high-risk patients [ 39 40 ].…”

Implementation of a knowledge‐based decision support system for treatment plan auditing through automation

“…For instance, even all organ‐at‐risk constraints are met for a treatment plan, such a method may fail to identify an inappropriate planning technique, energy, or beam arrangement for a tumor type or location. Previous studies have suggested that these challenges can be overcome by knowledge‐based QA/QC models based on knowledge learned from previous treatment prescriptions, plan parameters, or EPID dosimetry 22–36 . Essentially, assuming plans with the same treatment site, technique and modality share similar clinical “knowledge,” machine learning based QA/QC methods can interpret statistical significance of plan parameters and apply learned knowledge in the process of checking new treatment plans.…”

“…Previous studies have suggested that these challenges can be overcome by knowledge-based QA/QC models based on knowledge learned from previous treatment prescriptions, plan parameters, or EPID dosimetry. [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] Essentially,assuming plans with the same treatment site, technique and modality share similar clinical "knowledge," machine learning based QA/QC methods can interpret statistical significance of plan parameters and apply learned knowledge in the process of checking new treatment plans. For instance, by interpreting statistical significance of plan parameters, a warning could be raised when inappropriate X-ray energy, monitor units per fractional dose (MU/cGy ratio), or total number of beams are used for a head and neck IMRT (intensity-modulated radiation therapy) plan, leading to inadequate dose coverage or inadequate normal tissue sparing.…”