Healthcare Practitioner Directory Data Pipeline
APN (Healthcare Network) Case
Date: 00.00.00
Overview
Problem:
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Practitioner data (specialties, contact info) scattered across 5+ directories with inconsistent formatting.
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Manual entry caused a 40% error rate in their central directory.
Goal:
Automate data scraping, cleaning, and structuring for a centralized directory by:
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Extracting data from different directories I cannot disclose.
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Standardizing entries using AI models.
Technologies Used:
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Python: Web scraping (Scrapy, Selenium).
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OpenAI: Data cleaning (GPT-4 for rewriting bios, standardizing specialties).
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Google Sheets: Staging area for QA.
Results (Overview):
Reduced data entry errors to 2% and cut directory update time from 2 weeks to 2 days.
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Automated Scraping: Deployed Scrapy bots to extract practitioner names, locations, and specialties.
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AI-Powered Standardization: Used GPT-4 to rewrite bios into consistent formats (e.g., “[Name] specializes in [X] with [Y] years of experience”).
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Data Structuring: Exported cleaned data as CSV/JSON for seamless integration into APN’s directory.
Final Solution:
A scalable data pipeline that:
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Enabled APN to onboard 200+ new practitioners/month.
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Improved search accuracy for end-users by 60%.
Client testimonial
5.0
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John Doe
Co-Founder at Google