The NAP7 project was a collaboration between Rapid Research Evaluation and Appraisal Laboratory (RREAL) and the NAP7 team at the Royal College of Anaesthetists’ (RCoA) which explored key findings from the 7th National Audit project's baseline survey, where 10,746 UK Anaesthetists participated.

 

The RREAL team used Big Qualitative data analysis methods to analyse the free text in the survey responses, including use of the AI analysis tools: Caplena and InfraNodus.

​

​​​​​​​​​​​​​​​​​Through this innovative approach, new methods were validated, leading to several important findings:

​​

• Variations in confidence levels between genders in managing peri-operative cardiac arrest.

• Disparities in training adequacy for adult vs. paediatric advanced life support (ALS).

• Insights into debriefing practices, including pause rates after arrests.

• The prevalence of informal vs. formal well-being support.

 

Areas of improvement were also identified from the analysis including insufficient paediatric ALS training and challenges in post-cardiac arrest management.

​​

​From the NAP7 Big Qual data analysis we have developed an innovative Human Assisted Machine Learning Approach. The infographic below outlines the key steps of our Human Assisted Machine Learning approach, highlighting how human expertise and machine learning techniques can work together to provide deep and timely insights.​​​​​​​​​​​​​