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      Assessment of Nursing Workload and Adverse Events Reporting among Critical Care Nurses in the United Arab Emirates

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          Abstract

          Background

          Nursing is a demanding job, and excessive workloads have been demonstrated to negatively impact patient care. This study aimed to determine the associations between nursing workload on the days of intensive care unit (ICU) admission and discharge and adverse events among patients ( i.e. ICU readmission and medication errors).

          Methods

          This study used a retrospective cohort design. We reviewed medical records for 270 patients admitted to the ICU from three hospitals in the United Arab Emirates between February and April 2023. Collected data included patients’ demographics, diagnosis, acuity score on ICU admission/discharge days, Nursing Activities Score (NAS) on ICU admission/discharge days and adverse events reported ( i.e. occurrence of medication errors and re-admission to ICU after discharge).

          Results

          The nursing workload on ICU admission and discharge days was high (NAS=72.61 and NAS=52.61, respectively). There were significant associations between ICU readmission and nursing workload at ICU admission and discharge. Moreover, there was a significant relationship between the occurrence of medication errors and nursing workload on the day of ICU admission, with more medication errors occurring in patients with higher NAS scores.

          Conclusion

          The complexity of nursing activities and the severity of patients’ conditions directly impact the nursing workload and patient outcomes. A practical strategy to reduce the nursing workload may be calculating the NAS to clarify the actual time spent by nurses to provide the required care based on the patient’s condition. Adoption of new technologies to enhance medication safety and minimise errors may be another strategy to reduce the impact of the high nursing workload in ICU settings.

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          Most cited references44

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          Nursing activities score.

          The instruments used for measuring nursing workload in the intensive care unit (e.g., Therapeutic Intervention Scoring System-28) are based on therapeutic interventions related to severity of illness. Many nursing activities are not necessarily related to severity of illness, and cost-effectiveness studies require the accurate evaluation of nursing activities. The aim of the study was to determine the nursing activities that best describe workload in the intensive care unit and to attribute weights to these activities so that the score describes average time consumption instead of severity of illness. To define by consensus a list of nursing activities, to determine the average time consumption of these activities by use of a 1-wk observational cross-sectional study, and to compare these results with those of the Therapeutic Intervention Scoring System-28. A total of 99 intensive care units in 15 countries. Consecutive admissions to the intensive care units. Daily recording of nursing activities at a patient level and random multimoment recording of these activities. A total of five new items and 14 subitems describing nursing activities in the intensive care unit (e.g., monitoring, care of relatives, administrative tasks) were added to the list of therapeutic interventions in Therapeutic Intervention Scoring System-28. Data from 2,041 patients (6,451 nursing days and 127,951 multimoment recordings) were analyzed. The new activities accounted for 60% of the average nursing time; the new scoring system (Nursing Activities Score) explained 81% of the nursing time (vs. 43% in Therapeutic Intervention Scoring System-28). The weights in the Therapeutic Intervention Scoring System-28 are not derived from the use of nursing time. Our study suggests that the Nursing Activities Score measures the consumption of nursing time in the intensive care unit. These results should be validated in independent databases.
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            The Impact of Heavy Perceived Nurse Workloads on Patient and Nurse Outcomes

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              Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data

              Rationale: Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients. Objectives: We aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms. Methods: This observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database ( n  = 42,303 intensive care unit transfers). Results: Eleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning–derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort. Conclusions: A machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.
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                Author and article information

                Journal
                TONURSJ
                Open Nurs J
                The Open Nursing Journal
                Open Nurs. J.
                Bentham Science Publishers
                1874-4346
                26 December 2023
                2023
                : 17
                : e18744346281511
                Affiliations
                [1 ]Departemnt of Nursing, College of Health Sciences, University of Sharjah, Sharjah, UAE
                [2 ]Critical Care and Emergency Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt
                [3 ]Clinical Nursing Department, Faculty of Nursing, Applied Science Private University, Amman, Jordan
                Author notes
                [* ]Address correspondence to this author at the Department of Nursing, College of Health Sciences, University of Sharjah, Sharjah, UAE; Tel: +971-552095902; E-mails: U21102894@ 123456sharjah.ac.ae and muna.al-hosani@ 123456hotmail.com
                Article
                e18744346281511
                10.2174/0118744346281511231120054125
                73dbe908-5516-4be8-a45c-5770652441ae
                © 2023 The Author(s). Published by Bentham Open.

                This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 September 2023
                : 31 October 2023
                : 08 November 2023
                Categories
                Health Care

                Medicine,Chemistry,Life sciences
                Medication errors,ICU nurses,Workload,Adverse events,ICU readmission
                Medicine, Chemistry, Life sciences
                Medication errors, ICU nurses, Workload, Adverse events, ICU readmission

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