Concept-Based Retrieval from Critical Incident Reports.

BACKGROUND: Critical incident reporting systems (CIRS) are used as a means to collect anonymously entered information of incidents that occurred for example in a hospital. Analyzing this information helps to identify among others problems in the workflow, in the infrastructure or in processes.

OBJECTIVES: The entire potential of these sources of experiential knowledge remains often unconsidered since retrieval of relevant reports and their analysis is difficult and time-consuming, and the reporting systems often do not provide support for these tasks. The objective of this work is to develop a method for retrieving reports from the CIRS related to a specific user query.

METHODS: atural language processing (NLP) and information retrieval (IR) methods are exploited for realizing the retrieval. We compare standard retrieval methods that rely upon frequency of words with an approach that includes a semantic mapping of natural language to concepts of a medical ontology.

RESULTS: By an evaluation, we demonstrate the feasibility of semantic document enrichment to improve recall in incident reporting retrieval. It is shown that a combination of standard keyword-based retrieval with semantic search results in highly satisfactory recall values.

CONCLUSION: In future work, the evaluation should be repeated on a larger data set and real-time user evaluation need to be performed to assess user satisfactory with the system and results.