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Exploring the Documentation of Delirium in Patients After Cardiac Surgery: A Retrospective Patient Record Study
Satu Poikajärvi1, Laura-Maria Peltonen, Eriikka Siirala
1Author Affiliations: Faculty of Medicine, Department of Nursing Science, University of Turku (Prof Poikajärvi, Dr Peltonen, Prof Salanterä, and Dr Junttila); Department of Perioperative and Intensive Care, University of Helsinki and Helsinki University Hospital (Prof Poikajärvi); Research Services, Turku University Hospital (Dr Siirala); Faculty of Technology, Department of Computing, University of Turku (Dr Heimonen); Department of Computer Science, School of Science, Aalto University (Dr Moen); Turku University Hospital (Prof Salanterä); Nursing Research Center, Helsinki University Hospital and University of Helsinki (Dr Junttila), Finland.
Abstract:
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 (2005-2009 and 2015-2020). 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.
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