Literature DB >> 28739028

Incident Learning Systems for Radiation Oncology: Development and Value at the Local, National and International Level.

T Pawlicki1, M Coffey2, M Milosevic3.   

Abstract

AIMS: To discuss the background for incident reporting and learning systems, as well as the infrastructure and operational aspects to run them.
MATERIALS AND METHODS: Information from peer-reviewed literature, online resources and the authors' experience synthesised into a concise understanding of the topic.
RESULTS: Incident learning systems can be local, national or international, each having the same basic goals but facilitating different audiences and environments. A key component of any reporting and learning system is timely and effective analysis of near-misses and incidents as well as feedback to the users of the system. It is important for staff to know that reports are acknowledged, analysed and acted upon. There is a need to comply with current European legislation and other national systems, which can be addressed together with the steps required for comprehensive management of an incident.
CONCLUSION: Reporting and learning from incidents and near-misses is a key component of quality and safety in radiotherapy. A major benefit of the national or international systems is the potential for a larger database of incidents, supporting wider analysis and comparison, and sharing of knowledge across a larger community.
Copyright © 2017. Published by Elsevier Ltd.

Keywords:  Causal analysis; incident learning system; reporting

Mesh:

Year:  2017        PMID: 28739028     DOI: 10.1016/j.clon.2017.07.009

Source DB:  PubMed          Journal:  Clin Oncol (R Coll Radiol)        ISSN: 0936-6555            Impact factor:   4.126


  5 in total

1.  Algorithm development for intrafraction radiotherapy beam edge verification from Cherenkov imaging.

Authors:  Clare Snyder; Brian W Pogue; Michael Jermyn; Irwin Tendler; Jacqueline M Andreozzi; Petr Bruza; Venkat Krishnaswamy; David J Gladstone; Lesley A Jarvis
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-02

2.  Adoption of an incident learning system in a regionally expanding academic radiation oncology department.

Authors:  Jean L Wright; Arti Parekh; Byung-Han Rhieu; David Miller; Valentina Opris; Annette Souranis; Amanda Choflet; Akila N Viswanathan; Theodore DeWeese; Todd McNutt; Stephanie A Terezakis
Journal:  Rep Pract Oncol Radiother       Date:  2019-06-01

3.  Technical Note: Use of automation to eliminate shift errors.

Authors:  Elizabeth L Covington; Richard A Popple; Rex A Cardan
Journal:  J Appl Clin Med Phys       Date:  2020-02-10       Impact factor: 2.102

4.  The impact of COVID-19 workflow changes on radiation oncology incident reporting.

Authors:  Matthew E Volpini; Katie Lekx-Toniolo; Robert Mahon; Lesley Buckley
Journal:  J Appl Clin Med Phys       Date:  2022-08-06       Impact factor: 2.243

5.  Common Error Pathways in CyberKnife™ Radiation Therapy.

Authors:  Brandon T Mullins; Lukasz Mazur; Michael Dance; Ross McGurk; Eric Schreiber; Lawrence B Marks; Colette J Shen; Michael V Lawrence; Bhishamjit S Chera
Journal:  Front Oncol       Date:  2020-07-08       Impact factor: 6.244

  5 in total

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