A prioritization framework for the analysis of near misses in radiation oncology. (June 2020)

• Record Type: Journal Article
• Authors: Liszewski, Brian
• Is Part Of: Technical innovations & patient support in radiation oncology. Volume 14(2020)
• Page Start: 36
• Page End: 42
• Publication Date: 2020-06
• Subjects: Incident learning -- Near miss -- Failure modes -- Radiation therapy -- Quality -- Safety; Radiotherapy -- Periodicals; Cancer -- Radiotherapy -- Periodicals; Cancer -- Patients -- Hospital care -- Periodicals; 615.842
• DOI: 10.1016/j.tipsro.2020.04.001
• Languages: English
• ISSNs: 2405-6324

Abstract

Highlights: Near miss events provide opportunities yet limit resources for incident learning. Prioritization frameworks can triage near miss events. Event occurrence, detection and harm can inform prioritization. Objectively prioritizing events can focus a program's efforts for incident learning. Abstract: Introduction: The term near miss implies the aversion of a harm event but often there is a lack of evidence when establishing a link between a failure in process and potential harm. The focus of this study was to use reported incident data to inform a prioritization framework for the triage of near miss events in a radiation therapy program. Materials and Methods: Actual and near miss events during the study period were categorized using thematic analysis based on incident types. Near miss were characterized based upon their potential to result in harm to the patient using the concepts of failure modes and Analytic Hierarchy Process (AHP) theory. Near miss events were assessed for occurrence, detection and the potential impact and then assigned a summative normalized score reflecting prioritization recommendations, the normalized 10 point score (NTPS). Results: 107 events were reported within the study timeframe. 65% of event type categories ( n = 20) were attributed to near misses. 107 total events we analyzed using the framework with a maximum NTPS of 4 achieved across all event types. Of the 47 actual events 100% received a NTPS of 3 or greater. Of the 60 near miss invents 47% received an NTPS less than or equal to 1. Finally 15% of near miss events received a NTPS of 3 or greater. Conclusions: Near miss events provide a unique opportunity for learning however, can yield a great deal of data potentially limiting the resources for effective incident learning. A FMEA and AHP based prioritization framework for the triage of near miss events, including the likelihood of occurrence, probability of the event to go undetected and the potential impact if the incident did occur, allows for the optimal focus of programmatic resources in the analysis of these events.