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Patients with severe asthma were choosing not to use the emergency department (ED) in extremis and were self-medicating when experiencing severe asthma, putting their lives at risk. This local issue reflected a nationwide situation. The purpose of this paper is to better understand the reasons behind patients’ reluctance to attend ED and to consider practical solutions in a structured way.

Systems thinking (soft systems methodology) was used to examine the issues resulting in this reluctance to attend the ED. Once this tame (well-defined) problem was revealed, a potential solution was developed in co-production with patients.

Patients feared attending the ED and felt vulnerable while in the ED for several reasons. This appeared to be a well-defined and solvable problem. The solution proposed was an asthma patient passport (APP), which increased patient’s confidence in their ability to communicate their needs while in severe distress. The APP decreases (from 12 to 5 steps) the work patients had to do to achieve care. The APP project is currently being evaluated.

The APP should be offered to all people with severe asthma.

By revisiting systems thinking and identifying problems, a solution was identified. Although methods such as soft systems methodology have limitations when used in wicked (difficult or impossible to resolve) problems, such methods still have merit in tame problems and were applicable in this case to fully understand the issues, and to design practical solutions.

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.

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.

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.

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

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