Journal article
System-wide analysis of qualitative hospital incident data: Feasibility of semi-automated content analysis to uncover insights
Health Information Management Journal, Vol.54(3), pp.247-254
2025
PMID: 39578973
Abstract
Background:
Advances in technology have increased the ease of reporting hospital incidents, resulting in large amounts of qualitative descriptive data. Health services have little experience analysing these data at scale to incorporate into routine reporting.
Objective:
We aimed to explore the feasibility of applying a semi-automated content analysis (SACA) tool (Leximancer™) to qualitative descriptions of system-wide hospital incidents to provide insights into safety issues at all health service levels.
Method:
Data from 1245 incidents reported across a network of hospitals in Australia were analysed using the SACA tool. Summaries were generated using a variety of techniques, including inductive and deductive approaches to extract key concepts in the data.
Results:
The analysis was feasible and provided an actionable summary of the types of incidents reported in the data; the visual interface allowed users to explore the underlying text for a deeper understanding. Deductive analysis was utilised to explore specific areas of interest, and stratified analysis revealed more detailed concepts. The SACA tool was more efficient than manual processes; however, due to the context present in the incident descriptions, significant time, reading and subject matter expertise is still required to refine the analysis.
Conclusion:
Semi-automated tools provide an opportunity for improving patient safety culture and practices by providing rapid content analysis of vast datasets that can be customised for specific organisational contexts and deployed at scale. Further research is required to assess usefulness with system users.
Implications:
Qualitative data abound and system-wide analysis is essential to creating actionable insights.
Details
- Title
- System-wide analysis of qualitative hospital incident data: Feasibility of semi-automated content analysis to uncover insights
- Authors
- Teyl Engstrom - The University of QueenslandDanelle Kenny - The University of QueenslandWallace Grimmett - Mater Health ServicesMary-Anne Ramis - Mater Health ServicesChris Foley - Mater Health ServicesClair Sullivan - The University of QueenslandJason D Pole (Corresponding Author) - University of Toronto
- Publication details
- Health Information Management Journal, Vol.54(3), pp.247-254
- Publisher
- Sage Publications Ltd.
- Date published
- 2025
- DOI
- 10.1177/18333583241299433
- ISSN
- 1833-3575
- PMID
- 39578973
- Copyright note
- © The Author(s) 2024. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
- Organisation Unit
- School of Health - Nursing
- Language
- English
- Record Identifier
- 991099346002621
- Output Type
- Journal article
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