Key Highlights
- OptiSol partnered with a healthcare clinic to develop an AI-driven sentiment analysis solution that empowers providers with actionable insights from patient reviews.
- Leveraging Python, spaCy, TensorFlow, Flask, and TextBlob, the solution enabled automated classification of patient feedback into positive, negative, and neutral sentiments.
- A custom NLP pipeline extracted hidden entities and correlated them with sentiment scores to reveal the strongest drivers of patient satisfaction and dissatisfaction.
- The AI-powered approach improved efficiency, reduced manual effort in analyzing feedback, and enhanced the clinic’s ability to tailor services based on patient needs.
Problem Statement
01
Low Satisfaction: Limited visibility into patient experiences hindered timely identification of dissatisfaction factors, leading to reduced patient satisfaction.
02
Poor Retention: Lack of insight into feedback resulted in patients seeking alternative care providers, reducing retention and impacting revenue.
03
No Improvement Path: The absence of structured analysis made it difficult to identify and act on areas requiring improvement.
04
Weak Trust: Negative experiences without corrective action risked damaging trust, credibility, and community reputation.
Solution Overview
01
Collaborated with the healthcare clinic to design an AI-based sentiment analysis platform focused on patient feedback collected from social media and online directories.
02
Developed a machine learning classifier capable of categorizing feedback into positive, negative, and neutral sentiments using historical review data.
03
Built a custom NLP pipeline to detect hidden entities within patient reviews and connect them with specific sentiment-driven insights.
04
Scored entity-level text with the trained sentiment model to uncover correlations between patient perceptions and service attributes.
05
Trained the system to automatically highlight the most common positive and negative attributes influencing overall patient sentiment, enabling continuous improvement.
Business Impact
Technology Stack:
FAQs:
How does AI analyze patient reviews?
AI uses Natural Language Processing (NLP) and machine learning models to categorize reviews as positive, negative, or neutral, and extract key themes from the text.
What technologies were used?
The solution was built using Python, spaCy, TensorFlow, Flask, and TextBlob for data processing, NLP, and sentiment classification.
How does it improve clinic efficiency?
By automating the review analysis process, staff no longer need to manually read and categorize patient feedback, saving time and reducing errors.
Is the solution customizable for other clinics?
Absolutely. The system can be adapted to suit the data sources, patient demographics, and operational needs of any healthcare provider.
Can this solution be extended beyond healthcare?
Yes, the sentiment analysis framework can be applied to industries like retail, hospitality, and education where customer feedback is critical for service improvement.
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