Identify patient feedback sentiment
using AI
Below is a free classifier to identify patient feedback sentiment. Just input your text, and our AI will predict the sentiment of patient feedback - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("patient-feedback-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/patient-feedback-sentiment/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_text_here"}' \
https://www.nyckel.com/v1/functions/patient-feedback-sentiment/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict the sentiment of patient feedback.
This pretrained text model uses a Nyckel-created dataset and has 17 labels, including Appreciative, Content, Critical, Discontent, Dissatisfied, Enthusiastic, Mixed, Negative, Neutral and Optimistic.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of patient feedback).
Whether you're just curious or building patient feedback sentiment detection into your application, we hope our classifier proves helpful.
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Need to identify patient feedback sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Healthcare Quality Improvement: This function can be utilized to analyze patient feedback across various healthcare services. By identifying the sentiment behind the feedback, healthcare providers can pinpoint areas needing improvement and enhance overall service quality.
- Patient Experience Monitoring: Hospitals and clinics can utilize sentiment analysis to monitor ongoing patient experiences in real-time. By categorizing feedback as positive, negative, or neutral, organizations can quickly address concerns and improve patient satisfaction levels.
- Staff Performance Evaluation: This function can also be applied to evaluate staff performance based on patient feedback. By analyzing sentiment in comments related to specific staff members, hospital administrators can identify high performers and those who may require additional training.
- Service Recovery Strategies: Organizations can leverage sentiment analysis to develop effective service recovery strategies. By understanding negative feedback sentiments, healthcare providers can implement targeted interventions to address patient grievances and regain trust.
- Patient Engagement Initiatives: By understanding the sentiment behind patient feedback, healthcare providers can tailor patient engagement initiatives. Positive sentiment in feedback may guide programs that encourage similar interactions, while negative sentiment can help shape the focus of new engagement strategies.
- Market Research and Competitive Analysis: The sentiment analysis of patient feedback can provide valuable insights for market research. Healthcare organizations can compare their service sentiment against competitors to identify strengths and weaknesses in their offerings and refine their service delivery accordingly.
- Policy Development and Compliance: Regulatory bodies can use sentiment analysis to gauge public opinion on healthcare policies and compliance standards. Understanding patient sentiment can inform the creation of better policies that aim to enhance patient care and safety, ensuring alignment with public expectations.