title: Discharge timeliness and its impact on hospital crowding and emergency department flow performance.
abstract: OBJECTIVE
  The objective of this research is to identify optimal inpatient discharge time targets to help hospitals reduce crowding, improve patient flow through the ED and balance staff workload.
  
  
  METHODS
  Fifteen months of emergency and inpatient records from a large quaternary teaching hospital were used to reconstruct patient pathways from hospital presentation to discharge. Discrete event simulation was used to assess operationally realistic discharge scenarios on flow performance. Main output measures included National Emergency Access Target (NEAT) performance (an ED performance metric), time spent waiting for a bed, hospital length of stay (LOS) and occupancy.
  
  
  RESULTS
  Similar levels of improvement in NEAT performance (16%), and reductions in average bed occupancy (1.5%) and inpatient bed wait time (25%) were observed across the simulation that discharged 80% patients before 11 a.m. and one that spread the target between 10 a.m. and 2 p.m. Individual inpatient wards returned potential improvements in NEAT performance (median 10%, interquartile range (IQR) 7%), and reductions in hospital LOS (median 1%, IQR 1%) and average occupancy (median 1%, IQR 2%) across the discharge scenarios.
  
  
  CONCLUSIONS
  Conventional discharge targets like '80% by 11 a.m.' and others that spread targets across the day to balance staff workload freed up the equivalent of nine available beds for incoming patient flow, significantly reducing time spent waiting for an inpatient bed, hospital LOS and occupancy, and delivering much needed improvements in NEAT performance. While different strategies and workload distributions may suit individual hospital services, the study makes a strong case for improving 'early in the day' discharge timeliness to deliver better ED flow.
authors: CSIRO Australian e-Health Research Centre, Brisbane, Queensland, Australia.
authors: Sankalp
authors: S
authors: Khanna
authors: CSIRO Digital Productivity Flagship, Melbourne, Victoria, Australia.
authors: David
authors: D
authors: Sier
authors: CSIRO Australian e-Health Research Centre, Brisbane, Queensland, Australia.
authors: Justin
authors: J
authors: Boyle
authors: Mental Health Directorate, Central Adelaide Local Health Network, Adelaide, South Australia, Australia.
authors: Kathryn
authors: K
authors: Zeitz
mesh_terms: D001509:Bed Occupancy / Q000706:statistics & numerical data; D019091:Critical Pathways / Q000458:organization & administration; D003441:Crowding; D017598:Efficiency, Organizational / Q000592:standards* / Q000706:statistics & numerical data; D004636:Emergency Service, Hospital / Q000458:organization & administration* / Q000706:statistics & numerical data; D006784:Hospitals, Teaching / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000592:standards*; D011785:Quality Assurance, Health Care; D012189:Retrospective Studies; D013997:Time Factors; D014850:Waiting Lists; D057188:Workflow
keywords: bed occupancy; crowding; discharge planning; emergency medicine; hospital administration