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.

Examples of correct and incorrect visual identifications of the erythema migrans (EM) rash commonly seen in patients with Lyme disease. The images in the top right quadrant actually are EM (true positives). The upper right photos are false negatives, the lower left are false positives and the lower right were correctly ruled out as EM (true negatives). A new AI/deep learning technique from Johns Hopkins Medicine and the Johns Hopkins Applied Research Laboratory greatly increases the chances of correctly identifying EM in photographs. Johns Hopkins Medicine and Johns Hopkins Applied Research Laboratory (APL) researchers have shown that cell phone images of rashes taken by patients can be evaluated using artificial intelligence (AI) and deep learning (DL) technologies to more accurately detect and identify the erythema migrans (EM) skin redness associated with acute Lyme disease.

Do you think we're gonna be replaced?

A team at Johns Hopkins Medicine in Baltimore is developing a tumor-detecting algorithm for detecting pancreatic cancer. But first, they have to train computers to distinguish between organs. A team at Johns Hopkins Medicine in Baltimore is developing a tumor-detecting algorithm for detecting pancreatic cancer. But first, they have to train computers to distinguish between organs. Artificial intelligence, which is bringing us everything from self-driving cars to personalized ads on the web, is also invading the world of medicine.

One of the formidable challenges healthcare providers face is putting medical data to maximum use. Somewhere between the quest to unlock the mysteries of medicine and design better treatments, therapies, and procedures, lies the real world of applying data and protecting patient privacy. "Today, there are many barriers to putting data to work in the most effective way possible," observes Drew Harris, director of health policy and population health at Thomas Jefferson University's College of Population Health in Philadelphia, PA. "The goals of protecting patients and finding answers are frequently at odds." It is a critical issue and one that will define the future of medicine. Medical advances are increasingly dependent on the analysis of enormous datasets--as well as data that extends beyond any one agency or enterprise.