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[en] We report on the development and clinical deployment of an in-house incident reporting and learning system that implements the taxonomy of the Canadian National System for Incident Reporting – Radiation Treatment (NSIR-RT). In producing our new system, we aimed to: Analyze actual incidents, as well as potentially dangerous latent conditions. Produce recommendations on the NSIR-RT taxonomy. Incorporate features to divide reporting responsibility among clinical staff and expedite incident categorization within the NSIR-RT framework. Share anonymized incident data with the national database. Our multistep incident reporting workflow is focused around an initial report and a detailed follow-up investigation. An investigator, chosen at the time of reporting, is tasked with performing the investigation. The investigation feature is connected to our electronic medical records database to allow automatic field population and quick reference of patient and treatment information. Additional features include a robust visualization suite, as well as the ability to flag incidents for discussion at monthly Risk Management meetings and task ameliorating actions to staff. Our system was deployed into clinical use in January 2016. Over the first three months of use, 45 valid incidents were reported; 31 of which were reported as actual incidents as opposed to near-misses or reportable circumstances. However, we suspect there is ambiguity within our centre in determining the appropriate event type, which may be arising from the taxonomy itself. Preliminary trending analysis aided in revealing workflow issues pertaining to storage of treatment accessories and treatment planning delays. Extensive analysis will be undertaken as more data are accrued.
[en] We describe an electronic waiting room management system that we have developed and deployed in our cancer centre. Our system connects with our electronic medical records systems, gathers data for a machine learning algorithm to predict future patient waiting times, and is integrated with a mobile phone app. The system has been in operation for over nine months and has led to reduced lines, calmer waiting rooms and overwhelming patient and staff satisfaction.
[en] We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting in the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.
[en] Purpose: Out-of-field neutron doses resulting from photonuclear interactions in the head of a linear accelerator pose an iatrogenic risk to patients and an occupational risk to personnel during radiotherapy. To quantify neutron production, in-room measurements have traditionally been carried out using Bonner sphere systems (BSS) with activation foils and TLDs. In this work, a recently developed active detector, the nested neutron spectrometer (NNS), was tested in radiotherapy bunkers. Methods: The NNS is designed for easy handling and is more practical than the traditional BSS. Operated in current-mode, the problem of pulse pileup due to high dose-rates is overcome by measuring current, similar to an ionization chamber. In a bunker housing a Varian Clinac 21EX, the performance of the NNS was evaluated in terms of reproducibility, linearity, and dose-rate effects. Using a custom maximum-likelihood expectation–maximization algorithm, measured neutron spectra at various locations inside the bunker were then compared to Monte Carlo simulations of an identical setup. In terms of dose, neutron ambient dose equivalents were calculated from the measured spectra and compared to bubble detector neutron dose equivalent measurements. Results: The NNS-measured spectra for neutrons at various locations in a treatment room were found to be consistent with expectations for both relative shape and absolute magnitude. Neutron fluence-rate decreased with distance from the source and the shape of the spectrum changed from a dominant fast neutron peak near the Linac head to a dominant thermal neutron peak in the moderating conditions of the maze. Monte Carlo data and NNS-measured spectra agreed within 30% at all locations except in the maze where the deviation was a maximum of 40%. Neutron ambient dose equivalents calculated from the authors’ measured spectra were consistent (one standard deviation) with bubble detector measurements in the treatment room. Conclusions: The NNS may be used to reliably measure the neutron spectrum of a radiotherapy beam in less than 1 h, including setup and data unfolding. This work thus represents a new, fast, and practical method for neutron spectral measurements in radiotherapy
[en] We describe Opal (Oncology portal and application), the mobile phone app and patient portal that we have developed and are deploying for Radiation Oncology patients at our cancer centre. Opal is a novel tool to empower patients with their own personal medical data, including appointment schedules, consultation notes, test results, radiotherapy treatment planning information and wait time management. Furthermore, due to its integration with our electronic medical record and treatment planning database, Opal will allow us to collect patient reported outcomes from consenting patients and link them directly with dose volume histograms and other treatment data.
[en] We describe the measurements of neutron spectra that we undertook around a scanning proton beam at the Skandion proton therapy clinic in Uppsala, Sweden. Measurements were undertaken using an extended energy range Nested Neutron Spectrometer (NNS, Detec Inc., Gatineau, QC) operated in pulsed and current mode. Spectra were measured as a function of location in the treatment room and for various Bragg peak depths. Our preliminary unfolded data clearly show the direct, evaporation and thermal neutron peaks and we can show the effect on the neutron spectrum of a water phantom in the primary proton beam.
[en] Introduction Incident reporting, investigation and learning are core elements of quality improvement in radiation treatment. A programmatic approach to learning from one’s mistakes, and the free exchange of this information with others on a regional, national or international scale, has the potential to improve patient safety by preventing incident recurrence or propagation, identifying and correcting system vulnerabilities and promoting a ‘just’ culture of transparency and sharing. The Canadian National System for Incident Reporting in Radiation Treatment (NSIR-RT) was developed over the last three years and is presently being refined as a collaborative initiative between the Canadian Partnership for Quality Radiotherapy (CPQR) and the Canadian Institute of Health Information (CIHI). As an alliance among the key national professional associations in the delivery of radiotherapy in Canada, the CPQR is well-placed to provide the content expertise and community-level representation needed to ensure usability and utilization. CIHI are an independent not-for-profit organization that manages Canadian health data. As such, they bring to the project their technical and data-handling expertise as well as prior experience from development of a reporting system for Canadian medication incidents. We describe the development and refinement process for NSIR-RT and the results to date from a pilot deployment of the system. Methodology A key objective was to make NSIR-RT relevant to all radiation treatment programs in Canada regardless of location, size or practice orientation (academic vs. community care delivery). While participation in NSIR-RT is intended to be voluntary, development and refinement of the system was structured to motivate uptake and utilization by broadly engaging the Canadian radiation treatment community at every step of development. Figure 1 provides an overview of the development and refinement process.