NLP Powered Review Management System

Business Impact

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Artificial intelligence has been widely employed in healthcare to provide better medical services, improve patient care, and enhance business outcomes on a large scale.

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One of the popular Natural language processing techniques, sentiment analysis is applied in healthcare to understand the patients’ experience and medical literacy.

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According to the Markets and Markets reports, the global NLP in Healthcare and Lifesciences market is expected worth USD 7.2 billion by 2027, growing at a CAGR of 27.1% during the forecast period.

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Sentiment analysis classifies sentiment from free text and puts them in different categories focused on polarity, emotions, and intentions.

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We have worked with a Healthcare clinic in helping them understand their patient experience through the feedback aggregated from posts by patients on social media and online directories.

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Information like attributes on the physicians, nurses, support staff, hospital facility, etc., are extracted from these reviews and actionable insights are provided to the stakeholders to improve the patient experience.

Talk To Our Expert Team!

Technology Stack

Solution Overview

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Trained a machine learning sentiment classifier to score entities as positive, negative and neutral from historic data.

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Built a custom NLP pipeline to identify and extract hidden entities in the review text and extract the sentences associated with the entities.

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The text related to the hidden entities is scored using the trained classifier.

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Trained a model to detect and extract the most common positive and negative attributes that has the highest correlation with review sentiment.

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The entities are ranked across these common positive and negative attributes.

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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