ESTRO 2024 - Abstract Book

S5908

RTT - Service evaluation, quality assurance and risk management

ESTRO 2024

1761

Digital Poster

Trending error and incident reports (datix) to influence workforce changes

Damian Parr, Zoe Monteith, Kirsty Farnan

NHS Tayside, Radiotherapy, Dundee, United Kingdom

Purpose/Objective:

Within the Radiotherapy setting it is vital that risks are managed to minimise the potential for adverse events(1). Local practice encourage reporting of all levels of incidents and supports a no blame culture (evidence of impact). By utilising trend analysis of errors reported(2) in the local radiotherapy setting the management team aims to: Evaluate patterns to identify areas for improvement(3) Consider information within the context both local and national Identify anomalies which may raise concerns(4) Consider appropriate actions to rectify issues. Evaluate and improve on future practice.

Material/Methods:

• It was important that all radiotherapy errors reported in the Datix system where easily grouped and identified so that reports could be created. Therefore, work was undertaken with local teams to produce a radiotherapy recording code. • Reports gathered from May 2020 and each month after. • Trends analysis was performed from the results considering the following factors: number of Datix, Datix level, method of detection, safety barrier and causative factor. • Data was compared monthly, three monthly, yearly and against national data. • Data analysed provided information on aspects such as any outliers, local patterns, and spikes within certain areas • Investigation into causes of changes and any correlation with workforce matters. • Actions taken and evaluated to rectify areas of concern.

Results:

When compared with the national data profile, consistencies in patterns were shown, for example treatment processes having the highest number of error codes due to imaging practices (25% compared with national figure of 25.5%) (1). The safety barriers data reported proved useful in highlighting areas of concern. Analysis of this data set over time (3 months periods) allowed identification of increased error activity in the pre treatment area in Sept-Dec 2020, Sep-Dec 2021, May – Sept 2023, pre-treatment planning May-Sept 2020 and treatment data entry Feb-May 2021, Feb – May 2022, Feb – May 2023 (see figure 1).