Login / Signup

Perceptions of interdisciplinary bedside rounds by head nurses in Flanders: a cross-sectional exploration.

Tine HeipSimon MalfaitWim Van BiesenAnn Van HeckeKristof Eeckloo
Published in: Acta clinica Belgica (2020)
ObjectivesInterdisciplinary bedside rounds is gaining ground as a method to improve patient centredness and involvement, quality of care and team collaboration. An exploratory study was conducted in Flemish hospitals to (1) map and (2) examine the current form of rounds and the extent to which these were bedside, patient and family participatory and interdisciplinary.MethodsIn February 2020, a quantitative cross-sectional self-reporting web-based survey was conducted in 23 hospitals in Flanders, 213 head nurses of 213 wards completed the survey. A self-reporting 19-item questionnaire was developed in Lime Survey®. The questionnaire contained a mix of closed-ended questions an open-ended questions. The data were analysed using SPSS 26.0.ResultsMost of the wards in Flanders organise a form of daily rounds at the bedside. In only half of the wards these rounds are organised at a fixed time. The rounds most often include a physician and a nurse. Other disciplines are rarely actively involved. Only a minority of wards uses checklists, structures or protocols to standardise the rounds. The majority of the wards reports that patients (and family) get sufficient time to ask questions and say they are actively stimulated to do so.ConclusionIn current practice, most rounds are (partially) bedside, open for patient and family participatory and often include only a physician and a nurse. However, these elements of interdisciplinary rounds are not yet well integrated and vary strongly amongst ward. Most rounds should be considered as an extended form of physician rounds, rather than being interdisciplinary.
Keyphrases
  • cross sectional
  • primary care
  • healthcare
  • emergency department
  • palliative care
  • quality improvement
  • mental health
  • minimally invasive
  • mass spectrometry
  • machine learning
  • health insurance
  • patient reported outcomes