A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Exploring the Documentation of Delirium in Patients After Cardiac Surgery: A Retrospective Patient Record Study
Tekijät: Poikajärvi, Satu; Peltonen, Laura-Maria; Siirala, Eriikka; Heimonen, Juho; Moen, Hans; Salanterä, Sanna, Junttila; Kristiina
Kustantaja: Lippincott Williams and Wilkins
Julkaisuvuosi: 2024
Journal: CIN: Computers Informatics Nursing
Tietokannassa oleva lehden nimi: Home Healthcare Now
Vuosikerta: 42
Numero: 1
Aloitussivu: 27
Lopetussivu: 34
eISSN: 1538-9774
DOI: https://doi.org/10.1097/CIN.0000000000001039
Verkko-osoite: https://journals.lww.com/cinjournal/fulltext/2024/01000/exploring_the_documentation_of_delirium_in.5.aspx
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/386865755
Delirium is a common disorder for patients after cardiac surgery. Its manifestation and care can be examined through EHRs. The aim of this retrospective, comparative, and descriptive patient record study was to describe the documentation of delirium symptoms in the EHRs of patients who have undergone cardiac surgery and to explore how the documentation evolved between two periods. Randomly selected care episodes were annotated with a template, including delirium symptoms, treatment methods, and adverse events. The patients were then manually classified into two groups: nondelirious (n = 257) and possibly delirious (n = 172). The data were analyzed quantitatively and descriptively. According to the data, the documentation of symptoms such as disorientation, memory problems, motoric behavior, and disorganized thinking improved between periods. Yet, the key symptoms of delirium, inattention, and awareness were seldom documented. The professionals did not systematically document the possibility of delirium. Particularly, the way nurses recorded structural information did not facilitate an overall understanding of a patient's condition with respect to delirium. Information about delirium or proposed care was seldom documented in the discharge summaries. Advanced machine learning techniques can augment instruments that facilitate early detection, care planning, and transferring information to follow-up care. © 2023 The Authors.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
