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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.05701 |
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| _version_ | 1866913725109239808 |
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| author | Santamaria-Pang, Alberto Tuan, Frank Campbell, Ross Zhang, Cindy Jindal, Ankush Surapur, Roopa Holloman, Brad Hanisch, Deanna Buckley, Rae Cooney, Carisa Tarapov, Ivan Peairs, Kimberly S. Hasselfeld, Brian Greene, Peter |
| author_facet | Santamaria-Pang, Alberto Tuan, Frank Campbell, Ross Zhang, Cindy Jindal, Ankush Surapur, Roopa Holloman, Brad Hanisch, Deanna Buckley, Rae Cooney, Carisa Tarapov, Ivan Peairs, Kimberly S. Hasselfeld, Brian Greene, Peter |
| contents | The COVID-19 pandemic has accelerated the adoption of telemedicine and patient messaging through electronic medical portals (patient medical advice requests, or PMARs). While these platforms enhance patient access to healthcare, they have also increased the burden on healthcare providers due to the surge in PMARs. This study seeks to develop an efficient tool for message triaging to reduce physician workload and improve patient-provider communication. We developed OPTIC (Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations), a powerful message triaging tool that utilizes GPT-4 for data labeling and BERT for model distillation. The study used a dataset of 405,487 patient messaging encounters from Johns Hopkins Medicine between January and June 2020. High-quality labeled data was generated through GPT-4-based prompt engineering, which was then used to train a BERT model to classify messages as "Admin" or "Clinical." The BERT model achieved 88.85% accuracy on the test set validated by GPT-4 labeling, with a sensitivity of 88.29%, specificity of 89.38%, and an F1 score of 0.8842. BERTopic analysis identified 81 distinct topics within the test data, with over 80% accuracy in classifying 58 topics. The system was successfully deployed through Epic's Nebula Cloud Platform, demonstrating its practical effectiveness in healthcare settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OPTIC: Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations using GPT-4 Data Labeling and Model Distillation Santamaria-Pang, Alberto Tuan, Frank Campbell, Ross Zhang, Cindy Jindal, Ankush Surapur, Roopa Holloman, Brad Hanisch, Deanna Buckley, Rae Cooney, Carisa Tarapov, Ivan Peairs, Kimberly S. Hasselfeld, Brian Greene, Peter Machine Learning Computation and Language The COVID-19 pandemic has accelerated the adoption of telemedicine and patient messaging through electronic medical portals (patient medical advice requests, or PMARs). While these platforms enhance patient access to healthcare, they have also increased the burden on healthcare providers due to the surge in PMARs. This study seeks to develop an efficient tool for message triaging to reduce physician workload and improve patient-provider communication. We developed OPTIC (Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations), a powerful message triaging tool that utilizes GPT-4 for data labeling and BERT for model distillation. The study used a dataset of 405,487 patient messaging encounters from Johns Hopkins Medicine between January and June 2020. High-quality labeled data was generated through GPT-4-based prompt engineering, which was then used to train a BERT model to classify messages as "Admin" or "Clinical." The BERT model achieved 88.85% accuracy on the test set validated by GPT-4 labeling, with a sensitivity of 88.29%, specificity of 89.38%, and an F1 score of 0.8842. BERTopic analysis identified 81 distinct topics within the test data, with over 80% accuracy in classifying 58 topics. The system was successfully deployed through Epic's Nebula Cloud Platform, demonstrating its practical effectiveness in healthcare settings. |
| title | OPTIC: Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations using GPT-4 Data Labeling and Model Distillation |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2503.05701 |