DataFramed #124 Using AI to Improve Data Quality in Healthcare
Jan 30, 2023
Nate Fox is the CTO and Co-Founder of Ribbon Health, dedicated to improving healthcare data quality, while Sunna Jo is a former pediatrician turned data scientist at the same firm. They delve into the chaotic landscape of healthcare data, emphasizing the importance of context for accurate interpretation. The duo discusses the challenges of data cleaning and standardization, and how AI can enhance provider information, improving patient access to quality care. Their insights on innovative solutions and the power of data-driven decisions highlight a transformative approach to healthcare.
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Healthcare Data Woes
- Inaccurate healthcare data leads to wasted time and resources, like calling incorrect numbers or going to wrong locations.
- Ribbon Health aims to address this by improving healthcare data accuracy and accessibility for patients.
Data Cleaning
- Ribbon Health ingests messy provider data from various sources, including web scraping, public data, claims, and partners' data.
- They normalize data schemas and structures, resolving data into a knowledge graph of providers, locations, and organizations.
Context is Key
- Understanding the context behind data, including its source and intent, is crucial for effective data cleaning and analysis.
- Context helps in interpreting input values, dealing with missing data, and avoiding over-indexing on specific use cases.