Using knowledge discovery through data mining to gain intelligence from routinely collected incident reporting in an acute English hospital.

Abstract

Purpose: Incident reporting systems are commonly deployed in healthcare but resulting datasets are largely warehoused. This study explores if intelligence from such datasets could be used to improve quality, efficiency, and safety.

Design/methodology/approach: Incident reporting data recorded in one NHS acute Trust was mined for insight (n = 133,893 April 2005-July 2016 across 201 fields, 26,912,493 items). An a priori dataset was overlaid consisting of staffing, vital signs, and national safety indicators such as falls. Analysis was primarily nonlinear statistical approaches using Mathematica V11.

Findings: The organization developed a deeper understanding of the use of incident reporting systems both in terms of usability and possible reflection of culture. Signals emerged which focused areas of improvement or risk. An example of this is a deeper understanding of the timing and staffing levels associated with falls. Insight into the nature and grading of reporting was also gained.

Practical implications: Healthcare incident reporting data is underused and with a small amount of analysis can provide real insight and application to patient safety.

Originality/value: This study shows that insight can be gained by mining incident reporting datasets, particularly when integrated with other routinely collected data.