Improving ambulance care via fast feedback from Quality Care Indicators

Machine Learning
Natural Language Processing (NLP)
Sentiment Analysis
Ambulance
Author
Affiliation

Phil King

South Central Ambulance Service

Our aim is to improve Ambulance Clinical Quality Indicators (ACQI) data quality and rapidly provide feedback from all ACQI records. There is potential for this tool to be shared with other Ambulance Trusts using the same or similar data collection techniques. This would be achieved with a tool that can analyse free text fields to complete missing data, capture the sentiment of clinical notes, categorise the type of incident and assess if the treatment given was appropriate. It could be utilised as a backup process to manually reviewing data should there be a resourcing issue within the team. It will also evidence if any changes could be made to the clinical records to improve data capture.

We would like to consider how the free text could be searched to find positive and negative statements that impact if a record passes or fails a measure within each ACQI. In addition, creating a tool that would predict any changes to the ‘rules’ and their impact on the scores.