Lessons learned from investigations of therapy misadministration events

Ostrom, Lee T.; Rathbun, P.A.; Cumberlin, Richard L.; Horton, John L.; Gastorf, Robert J.; Leahy, T.J.

doi:10.1016/0360-3016(95)02056-x

Cited by 30 publications

(15 citation statements)

References 1 publication

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“…There are many stages throughout the patient journey where incidents might occur. In addition, radiotherapy is becoming increasingly complex and automated with a greater reliance on record-and-verify (R&V) systems, and although transferred automatically, these data should still be checked (5)(6)(7). There is a high expectation of safety in radiotherapy (8).…”

Section: Introductionmentioning

confidence: 99%

The Use of Categorized Time-Trend Reporting of Radiation Oncology Incidents: A Proactive Analytical Approach to Improving Quality and Safety Over Time

Arnold

Delaney

Cassapi

et al. 2010

International Journal of Radiation Oncology*Biology*Physics

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Section: Introductionmentioning

confidence: 99%

The Use of Categorized Time-Trend Reporting of Radiation Oncology Incidents: A Proactive Analytical Approach to Improving Quality and Safety Over Time

Arnold

Delaney

Cassapi

et al. 2010

International Journal of Radiation Oncology*Biology*Physics

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“…Therefore, extreme caution is often exercised during radiotherapy [40][41][42]. However, incidents that compromise patient safety still happen occasionally, caused by either human errors or equipment malfunction [22][23][24][25][26]. The precise number of these medical errors is not known; however these accidents could be fatal and therefore have a huge impact.…”

Section: Reducing Medical Errors In Radiotherapymentioning

confidence: 99%

Anomaly Detection in Time Series Radiotherapy Treatment Data

Sipes

Karimabadi²,

Jiang

et al. 2014

2014 IEEE International Conference on Semantic Computing

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The work presented here resulted in a valuable innovative technology tool for automatic detection of catastrophic errors in cancer radiotherapy, adding an important safeguard for patient safety. We designed a tool 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 at Moore Cancer Research Center at University of California San Diego. SmartTool determines if the treatment parameters are valid, against a previously built Predictive Model of a Medical Error (PMME). SmartTool has the following main features: 1) It 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, 2) The anomalous treatment parameters, if any, are detected using an innovative intelligent algorithm in a completely automatic and unsupervised manner, 3) It is a self-learning and constantly evolving system; the model is dynamically updated with the new treatment data, 4) It incorporates expert knowledge through the feedback loop of the dynamic process which updates the model with any new false positives (FP) and false negatives (FN) ; 4) When an outlier treatment parameter is detected, SmartTool works by preventing the plan execution and highlighting the parameter for human intervention; 5) It is aimed at catastrophic errors, not small errors.

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“…Incidents that compromise patient safety do happen occasionally, caused by either human error or equipment malfunction [9][10][11][12][13]. In the Panama incident [14], 16 patients were severely overexposed to approximately twice the prescribed dose in the late 2000 and early 2001, resulting in eight treatment-related deaths.…”

Section: Introductionmentioning

confidence: 99%

Treatment Planning Validation

Jiang

2015

Machine Learning in Radiation Oncology

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In this chapter, we will discuss the use of machine learning to detect errors in radiotherapy plans and charts. We will cover some general principles and established techniques for detecting errors in radiotherapy. We will discuss the rationale for using machine learning to detect large errors or outliers in radiotherapy treatment plans. As a concrete example, an automated error detection system for radiation treatment plans will be described. The technique was based on unsupervised machine learning, i.e., data clustering, and achieved over 90 % success rates in detecting outliers in over 1,000 treatment plans. Finally, future research directions in the clinical applications of machine learning for treatment planning validation will be briefly discussed.

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Lessons learned from investigations of therapy misadministration events

Cited by 30 publications

References 1 publication

The Use of Categorized Time-Trend Reporting of Radiation Oncology Incidents: A Proactive Analytical Approach to Improving Quality and Safety Over Time

The Use of Categorized Time-Trend Reporting of Radiation Oncology Incidents: A Proactive Analytical Approach to Improving Quality and Safety Over Time

Anomaly Detection in Time Series Radiotherapy Treatment Data

Treatment Planning Validation

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