Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing.

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms.

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls.

A Standardized Peer Review Program Improves Assessment and Documentation of Child Sexual Abuse.

Introduction: The objective was to assess the impact of interventions associated with ongoing expert peer review on the quality of child abuse medical providers' written and photograph documentation in child sexual abuse cases.

Methods: Pediatricians participated in a HIPAA compliant blind peer review process on a web platform developed to provide the American Board of Pediatrics maintenance of certification. Participants submit sequential photograph and written documentation of child sexual abuse examinations over 1 year.