Case Study
Survey-to-AI Pipeline for Patient Insights
From survey to insight in minutes.
Clarivant built a serverless AI pipeline for a healthcare provider that transformed patient satisfaction surveys from a manual reading exercise into real-time structured insights. The pipeline — SurveyMonkey webhook to AWS Lambda to GCP Cloud Functions to ChatGPT API for NLP/sentiment analysis to Snowflake — processes responses automatically, extracting themes, sentiment, and actionable patterns that previously took weeks to surface.
Key Results
The Transformation
The Challenge
The healthcare provider collected patient satisfaction surveys but extracted almost no value from them due to a completely manual analysis process:
- Survey responses accumulated in SurveyMonkey with no automated processing — someone had to manually read and categorize each response
- Insights surfaced weeks or months after patients submitted feedback, far too late to influence care decisions or operational changes
- No systematic way to detect sentiment trends, recurring complaints, or emerging quality issues across the patient base
- The volume of responses made manual analysis unsustainable — as the practice grew, the backlog grew with it
Our Approach
**Serverless Ingestion Pipeline:**
- Configured SurveyMonkey webhooks to trigger AWS Lambda functions on each new survey submission — eliminating batch processing delays
- Routed responses through GCP Cloud Functions for preprocessing and standardization before AI analysis
**AI-Powered Analysis:**
- Integrated ChatGPT API for natural language processing: sentiment classification, theme extraction, and structured categorization of free-text responses
- Designed prompts to extract specific healthcare-relevant dimensions — care quality, wait times, staff interaction, facility feedback — from unstructured patient comments
**Storage & Reporting:**
- Loaded structured results into Snowflake, creating a queryable patient feedback warehouse with sentiment scores, themes, and trend data
- Built reporting layer enabling the operations team to monitor patient satisfaction trends and flag emerging issues in near-real-time
The Outcome
**Speed & Automation:**
- Survey-to-insight cycle reduced from weeks of manual reading to minutes of automated processing — every response analyzed as it arrives
- Operations team shifted from periodic survey reviews to continuous monitoring of patient satisfaction trends
**Clinical & Operational Value:**
- Recurring complaint patterns and emerging quality issues now surface automatically, enabling faster operational response
- Structured sentiment data in Snowflake created a foundation for longitudinal patient experience tracking
- Pipeline scales with practice growth — processing capacity increases automatically without additional manual effort
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