Publications by authors named "A Kimia"
Background And Objectives: Patient and family violent outbursts toward staff, caregivers, or through self-harm, have increased during the ongoing behavioral health crisis. These health care-associated violence (HAV) episodes are likely under-reported. We sought to assess the feasibility of using nursing notes to identify under-reported HAV episodes.
View Article and Find Full Text PDFBuilding clinical registries is an important step in clinical research and improvement of patient care quality. Natural Language Processing (NLP) methods have shown promising results in extracting valuable information from unstructured clinical notes. However, the structure and nature of clinical notes are very different from regular text that state-of-the-art NLP models are trained and tested on, and they have their own set of challenges.
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- - The study aimed to evaluate how effective nursing handoff notes are in identifying underreported cases of hospital-acquired pressure injuries (HAPI) in a pediatric hospital setting.
- - Researchers developed a workflow, using natural language processing and machine learning models, to analyze over 70,000 nursing notes, achieving high sensitivity and providing accurate identification of HAPI events.
- - Their findings indicate that this method of surveillance for HAPI is not only feasible but also effective, identifying a notable incidence of injuries that may otherwise go unreported.
Article Synopsis
- Pediatric status epilepticus is a critical emergency with high risks, and electronic health records (EHRs) can help analyze treatment and outcomes efficiently.
- A study compared traditional manual review of EHRs with a natural language processing (NLP) tool called Document Review Tool (DrT) to identify cases of refractory status epilepticus (rSE).
- The NLP-assisted method proved more effective, identifying a higher number of rSE cases (31 out of 32) than manual review (20 out of 32), indicating its potential for improving patient identification and subsequent care.