Using large language model to guide patients to create efficient and comprehensive clinical care message.

Objective: This study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and the associated delays and frustrations.

Methods: We collected a dataset of patient messages sent between January 1, 2022 to March 7, 2023 at Vanderbilt University Medical Center. Two internal medicine physicians identified 7 common scenarios.

Leveraging large language models for generating responses to patient messages-a subjective analysis.

Objective: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal.

Materials And Methods: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism.

How the other half screens: A model for partnerships between student-run free clinics and genetic counseling programs to address disparities in hereditary cancer evaluation.

Genetic medicine is considered a major part of the future of preventative care, offering evidence-based, effective interventions to improve health outcomes and reduce morbidity and mortality, especially regarding hereditary cancer screening. Identification of individuals who would benefit from screening is key to improving their cancer-related healthcare outcomes. However, patients without insurance, of historically underserved races, of lower socioeconomic status, and in rural communities have lower access to such care.

Leveraging Large Language Models for Generating Responses to Patient Messages.

Objective: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal.

Methods: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism.

Results of Leveraging Pharmaceutical Patient Assistance Programs to Expand Access to High Cost Medications in a Student-Run Free Clinic.

High costs make many medications inaccessible to patients in the United States. Uninsured and underinsured patients are disproportionately affected. Pharmaceutical companies offer patient assistance programs (PAPs) to lower the cost-sharing burden of expensive prescription medications for uninsured patients.

Patients and Guns: Florida Physicians Are Not Asking.

Objectives: The number of deaths from gun violence continues to increase in the United States. Despite multiple studies demonstrating that counseling patients leads to safer gun storage, it is not routinely practiced by physicians. There are multiple barriers to discussing firearms with patients.