Improving Patient Experience with NLP-Driven Sentiment Analysis in Healthcare

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.

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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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