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Development and implementation of a radiation therapy incident learning system compatible with local workflow and a national taxonomy.

ABSTRACT: Collaborative incident learning initiatives in radiation therapy promise to improve and standardize the quality of care provided by participating institutions. However, the software interfaces provided with such initiatives must accommodate all participants and thus are not optimized for the workflows of individual radiation therapy centers. This article describes the development and implementation of a radiation therapy incident learning system that is optimized for a clinical workflow and uses the taxonomy of the Canadian National System for Incident Reporting - Radiation Treatment (NSIR-RT).The described incident learning system is a novel version of an open-source software called the Safety and Incident Learning System (SaILS). A needs assessment was conducted prior to development to ensure SaILS (a) was intuitive and efficient (b) met changing staff needs and (c) accommodated revisions to NSIR-RT. The core functionality of SaILS includes incident reporting, investigations, tracking, and data visualization. Postlaunch modifications of SaILS were informed by discussion and a survey of radiation therapy staff.There were 240 incidents detected and reported using SaILS in 2016 and the number of incidents per month tended to increase throughout the year. An increase in incident reporting occurred after switching to fully online incident reporting from an initial hybrid paper-electronic system. Incident templating functionality and a connection with our center's oncology information system were incorporated into the investigation interface to minimize repetitive data entry. A taskable actions feature was also incorporated to document outcomes of incident reports and has since been utilized for 36% of reported incidents.Use of SaILS and the NSIR-RT taxonomy has improved the structure of, and staff engagement with, incident learning in our center. Software and workflow modifications informed by staff feedback improved the utility of SaILS and yielded an efficient and transparent solution to categorize incidents with the NSIR-RT taxonomy.

SUBMITTER: Montgomery L

PROVIDER: S-EPMC5767999 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Mobile health (mHealth) technologies are increasingly used in various medical fields. However, the potential of mHealth to improve patient care in radiotherapy by acquiring electronic patient reported outcome measures (ePROMs) during treatment has been poorly studied so far. OBJECTIVE:The aim of this study was to develop and implement a novel Web app (PROMetheus) for patients undergoing radiotherapy. Herein, we have reported our experience with a focus on feasibility, patient acceptance, and a correlation of ePROMs with the clinical course of the patients. METHODS:In the period between January and June 2018, 21 patients used PROMetheus to score side effects, symptoms, and quality of life-related parameters during and after their treatment. Items of the Patient Reported Outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) were chosen based on the primary site of disease, 27 items for head and neck tumors, 21 items for thoracic tumors, and 24 items for pelvic tumors. RESULTS:In total, 17 out of the 21 patients (81%) regularly submitted ePROMs and more than 2500 data points were acquired. An average of 5.2, 3.5, and 3.3 min was required to complete the head and neck, thorax, and pelvis questionnaires, respectively. ePROMS were able to detect the occurrence of both expected and unexpected side effects during the treatment. In addition, a gradual increase in the severity of side effects over the course the treatment and their remission afterward could be observed with ePROMs. In total, 9 out of the 17 patients (53%), mostly those with head and neck and thoracic cancers, reported PRO-CTCAE grade III or IV fatigue with severe impairments of activities of daily life. CONCLUSIONS:This study shows the successful implementation of an ePROM system and a high patient acceptance. ePROMs have a great potential to improve patient care in radiotherapy by providing a comprehensive documentation of symptoms and side effects, especially of ones that are otherwise underreported.

Project description:PurposeThe curation of images using human resources is time intensive but an essential step for developing artificial intelligence (AI) algorithms. Our goal was to develop and implement an AI algorithm for image curation in a high-volume setting. We also explored AI tools that will assist in deploying a tiered approach, in which the AI model labels images and flags potential mislabels for human review.DesignImplementation of an AI algorithm.ParticipantsSeven-field stereoscopic images from multiple clinical trials.MethodsThe 7-field stereoscopic image protocol includes 7 pairs of images from various parts of the central retina along with images of the anterior part of the eye. All images were labeled for field number by reading center graders. The model output included classification of the retinal images into 8 field numbers. Probability scores (0-1) were generated to identify misclassified images, with 1 indicating a high probability of a correct label.Main outcome measuresAgreement of AI prediction with grader classification of field number and the use of probability scores to identify mislabeled images.ResultsThe AI model was trained and validated on 17 529 images and tested on 3004 images. The pooled agreement of field numbers between grader classification and the AI model was 88.3% (kappa, 0.87). The pooled mean probability score was 0.97 (standard deviation [SD], 0.08) for images for which the graders agreed with the AI-generated labels and 0.77 (SD, 0.19) for images for which the graders disagreed with the AI-generated labels (P < 0.0001). Using receiver operating characteristic curves, a probability score of 0.99 was identified as a cutoff for distinguishing mislabeled images. A tiered workflow using a probability score of < 0.99 as a cutoff would include 27.6% of the 3004 images for human review and reduce the error rate from 11.7% to 1.5%.ConclusionsThe implementation of AI algorithms requires measures in addition to model validation. Tools to flag potential errors in the labels generated by AI models will reduce inaccuracies, increase trust in the system, and provide data for continuous model development.

Project description:BackgroundThe National Immunization Program Information System (SIPNI - Sistema de Informação do Programa Nacional de Imunização) in Brazil is a technological innovation management tool that enhances the performance of managers and health professionals in the evaluation and monitoring of immunization activities. In the country, the decentralization of the System is at an advanced stage, but it still faces challenges regarding its operation and use, impacting on its results. This study aims to evaluate the deployment of SIPNI in the state of Minas Gerais, in 2017.MethodCross-section study performed in Primary Healthcare vaccination rooms in 54 municipalities in the Brazilian state of Minas Gerais, in 2017. A multidimensional questionnaire was used with nursing professionals who work in vaccination rooms, containing questions about the structure (presence of an internet-connected computer, instruction manual, software version, IT professional for technical support, trained healthcare professional, use of communication channels to obtain system information) and the process (activities performed by the staff to operate the immunization information system) of their work. Those questions refer to the components of the information system: system management, immunized-patient records, and Movement of Immunobiological. Implementation Degree (ID) was defined by a score system with different weights for each criterion, according to the importance level observed in it, with a rating of: adequate, partially adequate, inadequate and critically inadequate. For data analysis, median was used as the summary measure, and Pearson's Chi-Squared Test was used for proportion comparison.ResultsMunicipal SIPNI is not adequately implemented and that results mainly from the actions performed in health service units, indicating problems in the use of technology by professionals working in vaccination rooms. The structure was better evaluated than the process, presenting IDs of 70.9 and 59.5%, respectively. Insufficient internet access, inadequate use of communication channels, and lack of professional qualification were some of the identified structural issues. "Movement of Immunobiological" was the best-ranked component (ID = 68.5%), followed by "immunized patient records" (ID = 59.3%) and "SIPNI management" (ID = 50.7%). Partial performance of SIPNI is independent of population size in the municipality and of FSH coverage.ConclusionsSIPNI is still an underutilized technological innovation. There are challenges that must be overcome, such as implementation of the final web version, internet connectivity, and capabilities aimed at the use of information generated by technology. Nevertheless, perspectives regarding SIPNI are positive, with functionalities to optimize activities in vaccination rooms.

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Development and implementation of a radiation therapy incident learning system compatible with local workflow and a national taxonomy.

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