title: Non-linear relationships between nurse staffing and patients' length of stay in acute care units: Bayesian dependence modelling.
abstract: OBJECTIVE
  This study sought to analyse relationships between nurse staffing and patients' length of stay in acute care units and to determine whether non-linear relationships exist between variables.
  
  
  BACKGROUND
  Healthcare systems are complex and it could be assumed that they comprise non-linear associations. However, current planning and evaluation of nurse staffing are based primary on linear reasoning.
  
  
  METHODS
  This quantitative study adopted a retrospective longitudinal design.
  
  
  METHODS
  Retrospective register data, consisting of information relating to 35,306 patient episodes and administrative information concerning 381 nurses, were used. Data were collected in 2009 from 20 somatic inpatient units at a university hospital in Finland as a monthly time series of 2008 data and analysed using Bayesian dependency modelling.
  
  
  RESULTS
  Patients' acuity was the most important agent that connected all eleven variables in the dependency network of nurse staffing and short length of stay. Non-linear associations were found between short length of stay and the proportion of Registered Nurses. Skill mix consisting of an average proportion of Registered Nurses (65-80%) was conducive to a short length of stay and predicted a 66% likelihood of short length of stay. Higher and lower percentages of Registered Nurses predicted lower likelihood of short length of stay.
  
  
  CONCLUSIONS
  Flexible nurse staffing is preferable to fixed staffing to provide patients with shorter length of stay in acute care units. In the present research, the Bayesian method revealed non-linear relationships between nurse staffing and patient and care outcomes.
authors.affiliation: Department of Nursing Science, University of Eastern Finland, Kuopio, Finland.
authors.forename: Taina
authors.initials: T
authors.lastname: Pitkäaho
authors.forename: Pirjo
authors.initials: P
authors.lastname: Partanen
authors.forename: Merja
authors.initials: M
authors.lastname: Miettinen
authors.forename: Katri
authors.initials: K
authors.lastname: Vehviläinen-Julkunen
mesh_terms: D000208:Acute Disease / Q000451:nursing*; D001499:Bayes Theorem; D005387:Finland; D006757:Hospital Units; D006785:Hospitals, University; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D009741:Nursing Staff, Hospital / Q000600:supply & distribution*; D062072:Patient Acuity; D063868:Patient Outcome Assessment; D010561:Personnel Staffing and Scheduling / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D000078329:Workforce
keywords: Bayesian theorem; acute care; complex adaptive system; health services research; length of stay; nurse staffing; quantitative approaches; research methods; time series