title: Automatic calculation of the nine equivalents of nursing manpower use score (NEMS) using a patient data management system.
abstract: OBJECTIVE
  The most recent approach to estimate nursing resources consumption has led to the generation of the Nine Equivalents of Nursing Manpower use Score (NEMS). The objective of this prospective study was to establish a completely automatically generated calculation of the NEMS using a patient data management system (PDMS) database and to validate this approach by comparing the results with those of the conventional manual method.
  
  
  METHODS
  Prospective study.
  
  
  METHODS
  Operative intensive care unit of a university hospital.
  
  
  METHODS
  Patients admitted to the ICU between 24 July 2002 and 22 August 2002. Patients under the age of 16 years, and patients undergoing cardiovascular surgery or with burn injuries were excluded.
  
  
  METHODS
  None.
  
  
  RESULTS
  The NEMS of all patients was calculated automatically with a PDMS and manually by a physician in parallel. The results of the two methods were compared using the Bland and Altman approach, the interclass correlation coefficient (ICC), and the kappa-statistic. On 20 consecutive working days, the NEMS was calculated in 204 cases. The Bland Altman analysis did not show significant differences in NEMS scoring between the two methods. The ICC (95% confidence intervals) 0.87 (0.84-0.90) revealed a high inter-rater agreement between the PDMS and the physician. The kappa-statistic showed good results (kappa>0.55) for all NEMS items apart from the item "supplementary ventilatory care".
  
  
  CONCLUSIONS
  This study demonstrates that automatical calculation of the NEMS is possible with high accuracy by means of a PDMS. This may lead to a decrease in consumption of nursing resources.
authors.affiliation: Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Giessen, Rudolf-Buchheim-Strasse 7, 35392 Giessen, Germany.
authors.forename: Axel
authors.initials: A
authors.lastname: Junger
authors.forename: Florian
authors.initials: F
authors.lastname: Brenck
authors.forename: Bernd
authors.initials: B
authors.lastname: Hartmann
authors.forename: Joachim
authors.initials: J
authors.lastname: Klasen
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Quinzio
authors.forename: Matthias
authors.initials: M
authors.lastname: Benson
authors.forename: Achim
authors.initials: A
authors.lastname: Michel
authors.forename: Rainer
authors.initials: R
authors.lastname: Röhrig
authors.forename: Gunter
authors.initials: G
authors.lastname: Hempelmann
mesh_terms: D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D003695:Delivery of Health Care; D006751:Hospital Information Systems / Q000458:organization & administration / Q000706:statistics & numerical data*; D006785:Hospitals, University; D006801:Humans; D007362:Intensive Care Units; D009741:Nursing Staff, Hospital / Q000600:supply & distribution*; D011446:Prospective Studies; D011787:Quality of Health Care; D012044:Regression Analysis; D012307:Risk Factors; D000078329:Workforce