System-wide analysis of qualitative hospital incident data: Feasibility of semi-automated content analysis to uncover insights.

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.