What is Sentiment Analysis?
Sentiment Analysis is a powerful feature in MEG designed to automatically analyse and interpret the emotional tone of text-based feedback—such as patient complaints, staff surveys, or incident reports. By classifying feedback as positive or negative, this tool helps healthcare organisations gain valuable insights into the emotional context behind their data, without the need for manual interpretation.
Unlike traditional feedback analysis methods, Sentiment Analysis offers a consistent and efficient way to understand the emotional landscape of responses, enabling more informed decision-making.
How Sentiment Analysis Enhances Feedback Interpretation
Automate ‘Emotional Tone’ Classification
Sentiment Analysis takes the guesswork out of understanding feedback by automatically assessing the emotional mood of text entries. Whether you're dealing with patient complaints or staff surveys, this tool streamlines the process, saving time and minimising the risk of human error. It ensures that feedback is consistently classified, so you get a reliable overview every time.
Improve Response Times
By flagging negative feedback immediately, Sentiment Analysis helps your team quickly identify and prioritise urgent issues. This leads to quicker responses and can significantly boost both patient and staff satisfaction.
Enhance Patient and Staff Satisfaction
When you understand the emotional nuances in feedback, you can address concerns or promote compliments more effectively. This leads to better outcomes, making both patients and staff feel heard and valued.
MEG’s Sentiment Analysis Technology Overview
The Sentiment Analysis feature automatically assesses the emotional tone of text, categorising it as positive, negative, neutral, or unknown. It's applied to (text box) fields like the description of a patient’s complaint in a form. Once set up, it runs in the background after a form is submitted, with the results saved to a hidden field. This ensures that the sentiment evaluation doesn’t affect user responses but provides valuable insights into the overall sentiment of the data.
Use Cases of Sentiment Analysis in Healthcare
Patient Complaint Evaluation
Sentiment Analysis can quickly sift through patient complaints to identify negative feedback, allowing your teams to step in and address issues promptly. This helps ensure that concerns are dealt with before they escalate, improving overall patient satisfaction.
Staff Sentiment Monitoring
By analysing the sentiment in anonymous staff surveys, you can get a clear sense of the mood and morale within your healthcare team. This proactive approach can help you spot and address workplace issues before they become bigger problems.
Incident Report Assessment
Use sentiment analysis to assess the sentiment of incident reports, allowing for a deeper understanding of the context and ensuring appropriate follow-up actions.
Benefits of Sentiment Analysis
Immediate Alerts for Negative Feedback
Get instant notifications when negative feedback comes in, so key team members can jump in quickly and tackle issues before they grow.
Improved Responsiveness
With automated sentiment analysis, you can swiftly pinpoint critical feedback and act on it, ensuring timely interventions that lead to better outcomes.
Enhanced Staff and Patient Satisfaction
By gaining a deeper understanding of both patient and staff sentiments, you can make informed decisions that boost care quality and improve workplace culture.
Increased Efficiency
Let the system handle identifying the sentiment and triggering workflows, freeing up your team from manual tasks so they can focus on more impactful activities.
MEG’s Sentiment Analysis Tool: Live and Ready to Make Feedback Management Easier
MEG’s Sentiment Analysis tool is now live, ready to offer you clear insights that can improve the way you interpret feedback. With flexible pricing options, we’re committed to offering this AI-powered feature at a valuable price point for your organisation.
Interested in learning more?
We’d love to chat about how MEG’s Sentiment Analysis can benefit your healthcare organisation.