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Linhai Ma
Yale School of Medicine
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Yan Wang
Yale School of Medicine
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Srivani Talakokkul
Yale School of Medicine
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References (68)
[3]
Identifying the mechanisms of patient-centred communication in secure messages between clinicians and cancer patients
Founder's Pitch
"PVminer efficiently identifies and analyzes patient voices in healthcare data for enhanced patient-centered care."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
Quick Build
4/4 signals
Series A Potential
2/4 signals
Sources used for this analysis
arXiv Paper
Full-text PDF analysis of the research paper
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Why It Matters
Understanding the patient voice using tools like PVminer allows healthcare providers to better tailor their approaches to meet individual patient needs, reinforcing patient-centered care, improving outcomes, and reducing healthcare costs.
Product Angle
PVminer can be productized as a software tool for healthcare systems to analyze patient-generated data, aiding in patient-centric analysis and care improvement strategies.
Disruption
PVminer could replace manual, labor-intensive qualitative analysis of patient communications, offering scalable and precise automated insights aligned with patient and provider needs.
Product Opportunity
The healthcare sector faces challenges in patient communication and data management. Tools that extract meaningful insights from patient data are in demand for improving care outcomes, with hospitals and clinics as primary buyers.
Use Case Idea
Develop an API enabling healthcare providers to integrate patient voice analysis into existing e-health solutions, aiding improved patient-provider communication and decision-making processes.
Science
PVminer uses a patient-tailored BERT model for detecting and categorizing patient voices in text data, including surveys and secure messages, splitting the task into label prediction for structured representation of both communicative and social aspects.
Method & Eval
The tool was evaluated using pre-trained, patient-specific BERT models fine-tuned on a dataset of patient-generated messages. It demonstrated superior performance over existing models for predicting communications and social determinants.
Caveats
Potential limitations include model biases due to demographic variations, and the need for regular updates to handle evolving linguistic preferences and new data types.
Author Intelligence
Samah Fodeh
LEAD Yale School of Medicine
samah.fodeh@yale.edu
Linhai Ma
Yale School of Medicine
Yan Wang
Yale School of Medicine
Srivani Talakokkul
Yale School of Medicine
Ganesh Puthiaraju
Yale School of Medicine
Afshan Khan
Yale School of Medicine
Ashley Hagaman
Yale School of Public Health
Sarah Lowe
Yale School of Public Health
Aimee Roundtree
Texas State University