An evaluation of ‘fast track’ in A&E: a discrete event simulation approach

This longitudinal study provides primary evidence on the impact that a fast-track strategy in a hospital Emergency Department has on patient wait time. The study uses a discrete event simulation model to predict output within a variety of triage categories and compares these with post-implementation results. The results of the study indicate a significant reduction in patient wait time with 13.2% of the population waiting longer than 4 h prior to implementation compared with 1.4% post-implementation. However, while this fast-track strategy significantly improves service delivery to patients with minor conditions, service for patients with more acute conditions is not proportionately improved.

[1]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[2]  George S. Fishman,et al.  On validation of simulation models , 1973, AFIPS National Computer Conference.

[3]  F. Hashimoto,et al.  A computer simulation program to facilitate budgeting and staffing decisions in an intensive care unit. , 1987, Critical care medicine.

[4]  H W Meislin,et al.  Fast Track: urgent care within a teaching hospital emergency department: can it work? , 1988, Annals of emergency medicine.

[5]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[6]  S. Wright,et al.  Fast Track in the emergency department: a one-year experience with nurse practitioners. , 1992, The Journal of emergency medicine.

[7]  J. Christenson,et al.  Use of continuous quality improvement to facilitate patient flow through the triage and fast-track areas of an emergency department. , 1995, The Journal of emergency medicine.

[8]  D Braly,et al.  Seek alternatives via simulation software. , 1995, Health management technology.

[9]  D. Smith,et al.  Benefits of Hospital Capacity Reduction: Estimates from a Simulation Model , 1996, Health services management research.

[10]  John T. Blake,et al.  An Analysis Of Emergency Room Wait Time Issues Via Computer Simulation , 1996 .

[11]  J. Sitzia,et al.  Patient satisfaction: a review of issues and concepts. , 1997, Social science & medicine.

[12]  Timothy D. Brandt,et al.  Use of physician extenders and fast tracks in United States emergency departments. , 1997, The American journal of emergency medicine.

[13]  X M Huang.  Decision making support in reshaping hospital medical services , 1998, Health care management science.

[14]  Y Kambayashi,et al.  A study of medical emergency workflow. , 1998, Computer methods and programs in biomedicine.

[15]  A. K. Shahani,et al.  Capacity planning for intensive care units , 1998, Eur. J. Oper. Res..

[16]  A. Shahani,et al.  Classification treesA possible method for iso‐resource grouping in intensive care , 1998, Anaesthesia.

[17]  D. Healy,et al.  The meaning of patient satisfaction: an explanation of high reported levels. , 1998, Social science & medicine.

[18]  J. Posnett,et al.  Dynamics of bed use in accommodating emergency admissions: stochastic simulation model , 1999, BMJ.

[19]  Hon-Shiang Lau,et al.  Evaluating the impact of operating conditions on the performance of appointment scheduling rules in service systems , 1999, Eur. J. Oper. Res..

[20]  F. Dexter,et al.  Scheduling Surgical Cases into Overflow Block Time— Computer Simulation of the Effects of Scheduling Strategies on Operating Room Labor Costs , 2000, Anesthesia and analgesia.

[21]  E. Boudreaux,et al.  Emergency department personnel accuracy at estimating patient satisfaction. , 2000, The Journal of emergency medicine.

[22]  M. Kennedy.  Access to care: What should we be measuring? , 2000 .

[23]  F. Counselman,et al.  Patient satisfaction with physician assistants (PAs) in an ED fast track. , 2000, The American journal of emergency medicine.

[24]  W. Wilson.  An A&E nurse's fast-track for potential miscarriage patients. , 2000, Accident and emergency nursing.

[25]  N. Barber,et al.  Can simulation be used to reduce errors in health care delivery? The hospital drug distribution system , 2001, Journal of health services research & policy.

[26]  Timothy J Coats,et al.  Mathematical modelling of patient flow through an accident and emergency department , 2001 .

[27]  P. Harper.  A Framework for Operational Modelling of Hospital Resources , 2002, Health care management science.

[28]  M. Cooke,et al.  The effect of a separate stream for minor injuries on accident and emergency department waiting times , 2002, Emergency medicine journal : EMJ.

[29]  S. Bolton.  Consumer as king in the NHS , 2002 .

[30]  Marilyn M. Anderson,et al.  Research Paper: Evaluating the Capability of Information Technology to Prevent Adverse Drug Events: A Computer Simulation Approach , 2002, J. Am. Medical Informatics Assoc..

[31]  Seung-Chul Kim,et al.  Scheduling hospital services: the efficacy of elective-surgery quotas , 2002 .

[32]  Marilyn Anderson,et al.  Evaluating the Capability of Information Technology to Prevent Adverse Drug , 2002 .

[33]  W. Thalheimer,et al.  How to calculate effect sizes from published research: A simplified methodology , 2002 .

[34]  S. Moore,et al.  Capacity planning – modelling unplanned admissions in the UK NHS , 2003 .

[35]  P R Harper,et al.  Mathematical modelling and simulation for planning critical care capacity * , 2003, Anaesthesia.

[36]  K Mackway-Jones,et al.  Don’t throw triage out with the bathwater , 2003, Emergency medicine journal : EMJ.

[37]  S. Brailsford,et al.  Treating ailing emergency departments with simulation: an integrated perspective , 2003 .

[38]  E Clyde Buchanan,et al.  Computer Simulation as a Basis for Pharmacy Reengineering , 2003, Nursing administration quarterly.

[39]  Syi Su,et al.  Modeling an emergency medical services system using computer simulation , 2003, Int. J. Medical Informatics.

[40]  K. Castille,et al.  See and Treat: a management driven method of achieving targets or a tool for better patient care? One size does not fit all , 2003, Emergency medicine journal : EMJ.

[41]  A. Leaman.  See and Treat: a management driven method of achieving targets or a tool for better patient care? One size does not fit all , 2003, Emergency medicine journal : EMJ.

[42]  Louise Parker.  Making see and treat work for patients and staff. , 2004, Emergency nurse : the journal of the RCN Accident and Emergency Nursing Association.

[43]  L. Connelly,et al.  Discrete event simulation of emergency department activity: a platform for system-level operations research. , 2004, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[44]  Frank Zilm.  Estimating Emergency Service Treatment Bed Needs , 2004, The Journal of ambulatory care management.

[45]  S. Brailsford,et al.  Reviewing emergency care systems 2: measuring patient preferences using a discrete choice experiment , 2004, Emergency Medicine Journal.

[46]  N. D. Glick,et al.  Extending Simulation Modeling to Activity-Based Costing for Clinical Procedures , 2000, Journal of Medical Systems.

[47]  S. Brailsford,et al.  Reviewing emergency care systems I: insights from system dynamics modelling , 2004, Emergency Medicine Journal.

[48]  S Saint Lamont,et al.  "See and Treat": spreading like wildfire? A qualitative study into factors affecting its introduction and spread , 2005, Emergency Medicine Journal.

[49]  N. Stafford.  Turbulent times , 2006, Nature.

[50]  Marvin K. Nakayama,et al.  Statistical analysis of simulation output , 2008, 2008 Winter Simulation Conference.

[51]  P. Dimitrijevic.  10 High Impact Changes for service improvement and delivery: a guide for NHS leaders , 2008 .

[52]  Marian Kelley,et al.  One size does not fit all. , 2011, Bioanalysis.