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Full-Text Articles in Medicine and Health Sciences

Patient Perceptions Of Audio-Only Versus Video Telehealth Visits: A Qualitative Study Among Patients In An Academic Medical Center Setting, Ryan Kruis, Elizabeth A. Brown, Jada Johnson, Kit N. Simpson, James Mcelligott, Jillian Harvey Jan 2024

Patient Perceptions Of Audio-Only Versus Video Telehealth Visits: A Qualitative Study Among Patients In An Academic Medical Center Setting, Ryan Kruis, Elizabeth A. Brown, Jada Johnson, Kit N. Simpson, James Mcelligott, Jillian Harvey

Community & Environmental Health Faculty Publications

Introduction: Telehealth utilization surged during the COVID-19 pandemic, offering expanded health care access. Audio-only visits emerged as a crucial tool for patients facing technology or connectivity barriers to still use telehealth. This qualitative study aims to better understand patient perceptions of audio-only versus video telehealth visits during the COVID-19 pandemic, and how patients perceive the role of each in their overall health care. Methods: Semi-structured interviews were conducted with 14 adult patients seeking care at an academic medical center located in the Southeast region of the United States. Patients had experienced both an audio-only and video telehealth visit within the …


Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede Jan 2024

Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the …