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Acta Anaesthesiol Scand ; 58(4): 478-86, 2014 Apr.
Article in English | MEDLINE | ID: mdl-24571536

ABSTRACT

BACKGROUND: The nine equivalents of nursing manpower use score (NEMS) is used to evaluate critical care nursing workload and occasionally to define hospital reimbursements. Little is known about the caregivers' accuracy in scoring, about factors affecting this accuracy and how validity of scoring is assured. METHODS: Accuracy in NEMS scoring of Swiss critical care nurses was assessed using case vignettes. An online survey was performed to assess training and quality control of NEMS scoring and to collect structural and organizational data of participating intensive care units (ICUs). Aggregated structural and procedural data of the Swiss ICU Minimal Data Set were used for matching. RESULTS: Nursing staff from 64 (82%) of the 78 certified adult ICUs participated in this survey. Training and quality control of scoring shows large variability between ICUs. A total of 1378 nurses scored one out of 20 case vignettes: accuracy ranged from 63.7% (intravenous medications) to 99.1% (basic monitoring). Erroneous scoring (8.7% of all items) was more frequent than omitted scoring (3.2%). Mean NEMS per case was 28.0 ± 11.8 points (reference score: 25.7 ± 14.2 points). Mean bias was 2.8 points (95% confidence interval: 1.0-4.7); scores below 37.1 points were generally overestimated. Data from units with a greater nursing management staff showed a higher bias. CONCLUSION: Overall, nurses assess the NEMS score within a clinically acceptable range. Lower scores are generally overestimated. Inaccurate assessment was associated with a greater size of the nursing management staff. Swiss head nurses consider themselves motivated to assure appropriate scoring and its validation.


Subject(s)
Critical Care , Intensive Care Units , Nurses/supply & distribution , Adult , Critical Care/standards , Critical Care/statistics & numerical data , Data Collection , Female , Humans , Intensive Care Units/standards , Intensive Care Units/statistics & numerical data , Linear Models , Male , Middle Aged , Nurses/statistics & numerical data , Nursing Staff, Hospital , Quality Assurance, Health Care , Quality Control , Sex Distribution , Switzerland , Workforce
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