Safety and Quality in Lung Radiotherapy

UoM administered thesis: Unknown

  • Authors:
  • Martyn Gilmore

Abstract

Purpose: Investigate and appraise the use of Failure Mode Effect Analysis for routine risk assessment in lung radiotherapy and evaluate the efficacy of Quality Improvement measures introduced within the lung radiotherapy treatment planning process. Methods: A Multi-Disciplinary Team (MDT) was formed to carry out an FMEA for lung radiotherapy with one member acting as facilitator. A process map and list of failure modes was scored independently and discussed at a meeting where additional modes were identified and agreed. Timings were recorded and local incident data was compared to failure modes to determine the efficacy of the method. For the Quality Improvement (QI) event, three interventions were combined in a single change designed to improve the efficiency of lung radiotherapy plan checking. These were scripting of plan checks; the introduction of electronic questionnaires; and migration of the independent dose calculation task to the plan checker. Timings and plan rejection rates were passively gathered and compared for two separate three-month periods before and after intervention. Incident reports were also analysed to determine the number of false negatives for each period. Results: The FMEA was completed in less than 30 hours with 36 Failure modes identified. Of 38 incidents occurring since April 2017, 13 (34 %) were not predicted by the FMEA and a further four failure modes were introduced as a result. For the QI event, 105 and 96 patients were analysed pre and post intervention respectively and average overall planning timing was reduced by 2.8 hours (p 0.05). No overall change in the volume of incident reporting was found. Conclusions: FMEA can be used for routine risk assessment but should be combined with validation using incident reporting data wherever possible. Similarly, automated scripting, equestionnaires and process redesign can be used to improve efficiency in the planning process but quality improvement should include robust data collection and analysis to determine impact and inform further intervention.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
  • Carl Rowbottom (Supervisor)
  • Mike Kirby (Supervisor)
  • Mike Merchant (Supervisor)
Award date1 Aug 2020