Analyze patients feedback
to improve organization performance

Understand and improve patient experiences by gleaning insights from B2C communication

channels & customer feedback

How it works?

The Sentiment & Entity Analysis solution enables organizations in healthcare to analyze patient feedback — be it reviews, support requests, or phone calls.

Powered by NLP, it analyzes texts and converted audio-to-text data to assess message sentiment as positive, neutral, or negative and assign it to a given entity. While scanning through the text, the solution detects such keywords as brand titles, types of services, contact data, etc.

The data processed is stored in a database for further use in ML applications, or it can be displayed in a Business Intelligence dashboard for analysis and review by customer support teams.

Access to Patient Insights

Sentiment and entity analysis allows looking into patients' needs to find out how their challenges are resolved by personnel and customer support.

Reduced Manual Work

Medical personnel and customer support no longer have to look through and prioritize dozens of incoming messages and requests per day.

Improved Patient Experience

More efficient processing of incoming messages means that doctors get access to patient insights faster to provide higher-quality care.

Improved Support Performance

With all customer requests organized by sentiment and entity, customer support teams can resolve more requests at a higher rate.

Key Benefits

Key Features

Sentiment and entity classification
Object-oriented sentiment analysis
Emotion detection and recognition
Support of various data sources (chatbot messages, social network messages, support requests, emails, etc.)
Batch (schedule) or real-time forecasting
Secure, scalable, and customizable
Easy integration through RESTful API

How to deploy our software products?

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Analyze patients feedback to improve organization performance