Soon after The Friends and Family Test (FFT) launch in 2013, NHS teams have been abuzz with talk of an automated tool to analyse free text comments. The FFT qualitative component is a goldmine for service improvement ideas and reinforcing great practise however, manual analysis is arduous and the opportunity cost of staff time is a constant battle.
Without feedback scrutiny though, FFT runs the risk of becoming a ‘tick the box exercise’ rather than a catalyst of change. NHS England agree and are now focusing on free text comments as the real power behind FFT.
Sounds impressive but what is it?
Sentiment Analysis or opinion mining, is the latest innovation that focuses on textual responses. It identifies and extracts subjective materials to determine the patient’s emotions, attitudes and opinions of their overall experience.
Sentiment Analysis may ask:
- Is the free text comment positive or negative?
- Are there positive and negative comments within the same textual response and what are these?
- What are the patients attitudes towards their care delivered?
So What’s all The Fuss About?
Patient Experience teams across the country can all relate to receiving numerous patient feedback where the quantitative score is not a true reflection of their experience. For example a patient rates a service ‘1’, meaning ‘Extremely likely’ to recommend, but leaves the following comment:
‘My doctor was fantastic and the nursing staff were so caring and helpful. The food was cold and discharge took over 4 hours!’
The 55 NHS Trusts we work with find that further investigation is predominantly dedicated to 4 and 5 ratings, meaning issues highlighted within positive scores are often missed.
Enter the Game Changer….
Sentiment analysis will automatically RAG rate (Red/Amber/Green) each phrase, collate and then report on each rating.
E.g. My Doctor was fantastic and the nursing staff were caring and helpful – Bright Green
The food was cold and discharge took over 4 hours – Dark Red
How powerful and the benefits are endless! This tool gives Trusts the opportunity to search and report on the ‘Very Negative’ comments (dark red) meaning a quicker reaction to potentially serious issues. It also highlights common positive and negative themes reinforcing best practise and picking up on improvement trends.
What Clients are Saying:
‘Looking forward to using sentiment analysis to drive quality improvement. Places meaning behind our patient feedback: #let’s make a difference’ – Margaret Gillian, Stockport NHS Foundation Trust
‘I love the sentiment analysis – what I really like is that it doesn’t just categorise the individual comments, but it breaks them down even further by sentence. Therefore, for example, 10 comments might actually produce 17 sentences which have all been categorised for you – amazing!!!! This frees up considerable time and allows me to actually do more with the qualitative feedback.’ – Fliss Swift, University Hospitals of Morecambe Bay