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Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study

Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study

However, in routine clinical care, there is a significant practice gap, and most hospitals have not consistently implemented best practices [19-21]. A key barrier in using delirium as a quality indicator is the lack of a reliable and scalable method for early identification of delirium cases. Clinicians are not good at recognizing delirium using clinical gestalt, with corresponding recognition rates ranging between 16% and 35% [22].

Lu Wang, Yilun Zhang, Mark Chignell, Baizun Shan, Kathleen A Sheehan, Fahad Razak, Amol Verma

JMIR Med Inform 2022;10(12):e38161

Understanding the Use and Perceived Impact of a Medical Podcast: Qualitative Study

Understanding the Use and Perceived Impact of a Medical Podcast: Qualitative Study

For example, 1 listener felt a benefit of the conversational style was that it created a sense of “eavesdropping on a conversation” (P2, staff). A conversational style also engendered a sense of familiarity with the hosts. In addition, several participants reported that they either knew one or more of the hosts because of their local reputation or because they were personally acquainted with the hosts.

Sarah L Malecki, Kieran L Quinn, Nathan Zilbert, Fahad Razak, Shiphra Ginsburg, Amol A Verma, Lindsay Melvin

JMIR Med Educ 2019;5(2):e12901