AI Based Patient Sentiment Analysis in Healthcare

Application Screenshots

Technology Stack

Problem Statement


Reduced patient satisfaction: Without understanding customer experience, it can be difficult to identify and address areas of dissatisfaction, leading to reduced patient satisfaction and potentially causing patients to seek care elsewhere.


Decreased patient retention: If patients are not satisfied with their experience, they may choose not to return to the clinic, leading to decreased patient retention and a loss of revenue.


Difficulty in identifying areas for improvement: Without gaining insight into customer experience, it can be difficult to identify and address issues that affect patient satisfaction and clinic growth.


Lack of trust and credibility: If patients are not satisfied with their experience, they may not recommend the clinic to others, leading to a lack of trust and credibility in the community.


Difficulty in identifying customer needs: Without understanding customer experience, it can be difficult to identify the needs of patients and how to tailor services to meet those needs.

Solution Overview


We collaborated with a healthcare clinic to develop an AI-based solution for understanding patient experience by analyzing feedback gathered from social media and online directories.


Utilized machine learning to train a sentiment classifier that scores entities as positive, negative, and neutral from historical data.


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


Scored text related to hidden entities using the trained sentiment classifier.


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

Business Impact


Improved patient satisfaction: By using sentiment analysis to understand patient experience, the clinic can identify and address areas of dissatisfaction, leading to improved patient satisfaction and potentially increased patient retention.


Increased efficiency: Automates the process of gathering and analyzing patient feedback, making it more efficient and cost-effective than manual methods.


Better understanding of patient needs: Provides insights into patient needs and preferences, helping the clinic tailor services to better meet those needs.


Increased reputation and credibility: By using sentiment analysis to understand patient experience, the clinic can improve its reputation and credibility in the community by addressing issues and concerns that patients may have.


Competitive advantage: Gain a competitive advantage over other clinics that are not using this technology.

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