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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