Taxonometric applications in radiotherapy incident analysis.

Recent publications in both the scientific and the popular press have highlighted the risks to which patients expose themselves when entering a healthcare system. Patient safety issues are forcing us to, not only acknowledge that incidents do occur, but also actively develop the means for assessing and managing the risks of such incidents. To do this, we ideally need to know the probability of an incident's occurrence, the consequences or severity for the patient should it occur, and the basic causes of the incident. A structured approach to the description of failure modes is helpful in terms of communication, avoidance of ambiguity, and, ultimately, decision making for resource allocation. In this report, several classification schemes or taxonomies for use in risk assessment and management are discussed. In particular, a recently developed approach that reflects the activity domains through which the patient passes and that can be used as a basis for quantifying incident severity is described. The estimation of incident severity, which is based on the concept of the equivalent uniform dose, is presented in some detail. We conclude with a brief discussion on the use of a defined basic-causes table and how adding such a table to the reports of incidents can facilitate the allocation of resources.

The Equivalent Uniform Dose as a severity metric for radiation treatment incidents.

In allocating resources within a risk management program, ideally we would like to know both the probabilities and consequences of potential incidents. We simulate, on a treatment planning computer, several commonly reported incidents in radiation treatment and explore their consequences for the EUDs of targets and organs at risk.

Risk analysis in radiation treatment: application of a new taxonomic structure.

BACKGROUND AND PURPOSE: Radiation treatment (RT) for cancer is susceptible to clinical incidents resulting from human errors and equipment failures. A systematic approach to collecting and processing incidents is required to manage patient risks. We describe the application of a new taxonomic structure for RT that supports risk analysis and organizational learning. MATERIALS AND METHODS: A systematic analysis of the RT process identified five process domains. Within each domain we defined incident type groups. We then constructed a database reflecting this taxonomic structure and populated it with incidents from publicly available sources. Querying this database provides insights into the nature and relative frequency of incidents in RT. RESULTS: There are relatively few reports of incidents in the Prescription domain compared with the Preparation and Treatment domains. There are also fewer reports of systematic and infrastructure incidents in comparison to sporadic and process incidents. Infrastructure incidents are mainly systematic in nature, while process incidents are more likely to be sporadic. CONCLUSIONS: The lack of a standard, systems-oriented framework for incident reporting makes it difficult to learn from existing incident report sources. A clear understanding of the potential consequences and relationships between different incident types will guide incident reporting, resource allocation, and risk management efforts.