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BackgroundDelirium is a common clinical problem with acute and fluctuating onset. Early notification of its symptoms can lead to earlier detection and management of this state. Valid and reliable instruments are required for successful nursing practice. The purpose of the study was to psychometrically test the Finnish versions of the Neecham Confusion Scale (NEECHAM) and the Nursing Delirium Screening Scale (Nu-DESC) in surgical nursing care, utilizing the Confusion Assessment Method (CAM) algorithm as a comparison scale.MethodsThis randomized, blinded, instrument testing study was conducted at one university hospital in one surgical unit. Study patients (n = 112) meeting the pre-set criteria were assessed by the principal investigator (PI) and a registered nurse (RN, n = 18). Internal consistency, inter-rater reliability, and concurrent validity of the scales were calculated and face validity and usability evaluated.ResultsInternal consistency was from .76 to .86 for all three scales. Inter-rater reliability between PI and RNs was .87 with NEECHAM, .60 with CAM and .47 with Nu-DESC. Concurrent validity was .56 and .59 between CAM and NEECHAM, and .68 and .72 between NEECHAM and Nu-DESC. In the PI group, the correlation between CAM and Nu-DESC was .91, in the RN’s group .42. Nu-DESC was evaluated as the most usable scale.ConclusionThe findings strengthen the earlier research on the scales and indicate that the Finnish NEECHAM and Nu-DESC correlates with CAM algorithm and with each other. They seem to be clinically viable in assessing patients’ delirium in surgical wards but more validity testing is needed.

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 aug-ment instruments that facilitate early detection, care planning, and transferring information to follow-up care.

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