Towards effective capacity planning in a perinatal network centre

Md Asaduzzaman, Thierry J Chaussalet, Shola Adeyemi, Salma Chahed, Jane Hawdon, Daniel Wood, Nicola J Robertson

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVE: To study the arrival pattern and length of stay (LoS) in a neonatal intensive care/high dependency unit (NICU/HDU) and special care baby unit (SCBU) and the impact of capacity shortage in a perinatal network centre, and to provide an analytical model for improving capacity planning.

METHODS: The data used in this study have been collected through the South England Neonatal Database (SEND) and the North Central London Perinatal Network Transfer Audit between 1 January and 31 December 2006 for neonates admitted and refused from the neonatal unit at University College London Hospital (UCLH). Exploratory data analysis was performed. A queuing model is proposed for capacity planning of a perinatal network centre.

OUTCOME MEASURES: Predicted number of cots required with existing arrival and discharge patterns; impact of reducing LoS.

RESULTS: In 2006, 1002 neonates were admitted to the neonatal unit at UCLH, 144 neonates were refused admission to the NICU and 35 to the SCBU. The model shows the NICU requires seven more cots to accept 90% of neonates into the NICU. The model also shows admission acceptance can be increased by 8% if LoS can be reduced by 2 days.

CONCLUSIONS: The arrival, LoS and discharge of neonates having gestational ages of <27 weeks were the key determinants of capacity. The queuing model can be used to determine the cot capacity required for a given tolerance level of admission rejection.

Original languageEnglish
Pages (from-to)F283-7
JournalArchives of Disease in Childhood. Fetal and Neonatal Edition
Volume95
Issue number4
DOIs
Publication statusPublished - Jul 2010

Keywords

  • Bed Occupancy/statistics & numerical data
  • Gestational Age
  • Health Care Rationing/organization & administration
  • Health Planning/methods
  • Health Services Research/methods
  • Humans
  • Infant, Newborn
  • Intensive Care Units, Neonatal/organization & administration
  • Length of Stay/statistics & numerical data
  • London
  • Models, Organizational
  • Needs Assessment/organization & administration
  • Patient Discharge/statistics & numerical data
  • Seasons

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