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Linhai Ma
Yale University
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Ganesh Puthiaraju
Yale University
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Srivani Talakokkul
Yale University
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Founder's Pitch
"Enable extraction of patient voice from text to improve health insights."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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4/4 signals
Series A Potential
2/4 signals
Sources used for this analysis
arXiv Paper
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Why It Matters
Patient-generated text contains crucial insights about patient experiences, which are often unstructured and underutilized in healthcare. Extracting and structuring these insights can significantly aid in patient-centered healthcare improvement.
Product Angle
The product could be a cloud-based healthcare service that integrates with hospital systems, offering a dashboard to track and analyze patient voice data analytics.
Disruption
This solution could disrupt traditional patient surveys and manual data extraction methods, offering a more scalable and insightful approach to understanding patient experiences.
Product Opportunity
The healthcare analytics market is vast, with growing demand for tools that can leverage unstructured data to improve outcomes. Hospitals, insurers, and research institutions would be likely customers.
Use Case Idea
A hospital system could use this technology to analyze patient communications in their portals, identifying systemic issues in patient care and improving personalized treatment plans.
Science
This research creates a framework using fine-tuned large language models to extract structured data from patient-generated text. It establishes a process to identify and categorize expressions related to patient-provider interactions and social determinants of health.
Method & Eval
The method uses fine-tuned models to achieve structured extraction with F1 scores around 80%. This approach is validated on a diverse dataset from healthcare institutions, showing its effectiveness without needing extremely large models.
Caveats
Reliability could vary with different patient populations and linguistic styles. The flexibility of models might produce errors if not carefully fine-tuned and could miss nuances in patient text.
Author Intelligence
Samah Fodeh
LEAD Yale University
samah.fodeh@yale.edu
Linhai Ma
Yale University
Ganesh Puthiaraju
Yale University
Srivani Talakokkul
Yale University
Afshan Khan
Yale University
Ashley Hagaman
Yale University
Sarah Lowe
Yale University
Aimee Roundtree
Texas State University