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Timothy Keyes, Alison Callahan, Abby Pandya, Nerissa Ambers et autres
Postdeployment monitoring of artificial intelligence (AI) systems in health care is essential to ensure their safety, quality, and sustained benefit - and to support governance decisions about which systems to update, modify, or decommission. Motivated by these needs, the authors developed a …
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Nigam H. Shah, Nerissa Ambers, Abby Pandya, Timothy Keyes et autres
While large language models (LLMs) can support clinical documentation needs, standalone tools struggle with "workflow friction" from manual data entry. We developed ChatEHR, a system that enables the use of LLMs with the entire patient timeline spanning several years. ChatEHR enables automations …
Nigam H. Shah, Nerissa Ambers, Abby Pandya, Timothy Keyes et autres
While large language models (LLMs) can support clinical documentation needs, standalone tools struggle with "workflow friction" from manual data entry. We developed ChatEHR, a system that enables the use of LLMs with the entire patient timeline spanning several years. ChatEHR enables automations …
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Suhana Bedi, Hejie Cui, Miguel Fuentes, Alyssa Unell et autres
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Timothy Keyes, Alison Callahan, Abby Pandya, Nerissa Ambers et autres
assesses whether a deployed system continues to have value in the form of benefit to clinicians, staff, and patients. Drawing on examples of deployed AI systems at our academic medical center, we provide practical guidance for creating monitoring plans based on these …
Timothy Keyes, Alison Callahan, Abby Pandya, Nerissa Ambers et autres
Post-deployment monitoring of artificial intelligence (AI) systems in health care is essential to ensure their safety, quality, and sustained benefit-and to support governance decisions about which systems to update, modify, or decommission. Motivated by these needs, we developed a framework for monitoring …
Suhana Bedi, Hejie Cui, Miguel Fuentes, Alyssa Unell et autres
While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinical practice. We introduce MedHELM, an extensible evaluation framework for assessing LLM performance for medical tasks with three key contributions. …
Crystal Chang, Hodan Farah, Haiwen Gui, Shawheen J. Rezaei et autres
Red teaming, the practice of adversarially exposing unexpected or undesired model behaviors, is critical towards improving equity and accuracy of large language models, but non-model creator-affiliated red teaming is scant in healthcare. We convened teams of clinicians, medical and engineering students, and …
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Obbina Abani, Amr E. Abbas, Fatima Abbas, Jaffar Abbas et autres
Background: Low dose corticosteroids (e.g., 6 mg dexamethasone) have been shown to reduce mortality for hypoxic COVID-19 patients. We have previously reported that higher dose corticosteroids cause harm in patients with clinical hypoxia but not receiving ventilatory support (the combination of non-invasive …
April S. Liang, Juan M. Banda, Thomas Savage, Abby Pandya et autres
This study evaluates the feasibility of using GPT-4 to automate precharting for specialty referrals, focusing on new patients referred to an otolaryngology clinic for nasal congestion. We describe the design decisions and strategies tested in creating this precharting utility, including methods for …
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Ramya Tekumalla, Juan M. Banda
Electronic phenotyping involves a detailed analysis of both structured and unstructured data, employing rule-based methods, machine learning, natural language processing, and hybrid approaches. Currently, the development of accurate phenotype definitions demands extensive literature reviews and clinical experts, rendering the process time-consuming and …
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Alison Callahan, Duncan C. McElfresh, Juan M. Banda, Gabrielle Bunney et autres
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