Table 4

Summary of simulation results

Study name Conclusions Conclusions detail Reported the changes implemented? Result implementation Barriers
Anagnostou et al 21 NoneNoneNoNANone
Au-Yeung et al 22 Supported the changes consideredPrioritisation of treatment for patient with minor problems over major problems could lead to improved outcomeNoNASimplified assumptions
Baboolal et al 23 Supported the changes consideredA change in staffing levels could lead to substantial cost savings and reduce the 4-hour breachesNoNANone
Bowers et al 24 NoneNoneNoNAModel runtime; high expectancy
Brailsford et al 10 Opposed the changes consideredStreaming of patients by triage category was not an efficient use of clinical resourcesNoNANone
Coats and Michalis25 Supported the changes consideredShift pattern that best matches patient arrivals would give shorter waiting timesNoNASimplified model structure and assumptions; poor data quality
Codrington-Virtue et al 26 NoneNoneNoNANone
Codrington-Virtue et al 27 NoneNoneNoNANone
Coughlan et al 28 Proposed differential changesAdding an emergency nurse practitioner would not reduce the waiting times. Resource reallocation would improve throughput timesNoNAGeneralisability
Davies29 Supported the changes consideredThe separation of see and treat would be beneficialNoNAPoor data quality
Eatock et al 11 NoneNoneNoNASystem complexity; model runtime
Fletcher et al 12 Proposed differential changesDeflecting demand away from A&E would lead to improvement around waiting for beds, specialists and assessment processesYesUnknown as other interventions were introduced in parallelPoor data quality; poor stakeholder engagement
Günal and Pidd13 NoneNoneNoNAExplaining the causes of change in performance
Günal and Pidd30 Proposed differential changesMore senior doctors, less X-ray requisitions and more cubicles would reduce waiting timesNoNAModelling multitasking behaviour of staff
Hay et al 31 NoneNoneNoNASystem complexity
Komashie and Mousavi32 Proposed differential changesAdding a nurse or doctor to minors would reduce the waiting times by 28%. Increasing the cubicles/beds would make smaller changeNoNANone
Lane et al 33 Proposed differential changesChanging bed numbers led to no noticeable change in waiting times but a substantial difference to elective cancellationsNoNAShort timescale; simplified assumptions
Lattimer et al 14 Proposed differential changesSystem would not be able to cope with increasing demand from scenario 1*, but scenarios 2, 3 and 4§ could improve thisNoNASimplified model structure; system complexity; generalisability
Maull et al 34 Supported the changes consideredSee and treat reduced the 4-hour breaches from 13.2% to 3.4%YesMarked reduction in no. of breaches from 13.2% to 1.4%. No. of patients waiting less than 1 hour increased from 12% to 23%. No. of patients with major problems waiting between 3 and 4 hours increasedPoor data availability and quality; system complexity
Meng and Spedding35 Proposed differential changesReduced times to see a consultant would reduce the waiting times. Access to 24-hour X-ray would reduce the waiting times tooNoNASimplified assumptions
Mould et al 36 Supported the changes consideredA new staff roster would reduce the waiting timesYesMean time for minor problems dropped from 100 to 94 min, for major problems it dropped from 200 to 195 min. Mean time for minor problems fell by 16 min after adjusting other factorsPoor data quality; limited analytical skills; impact of simulation
  • *Five-year model run assuming 4% year-on-year growth in emergency admissions and 3% year-on-year growth in general practitioner (GP) referral for planned admissions.

  • †Impact of increase in demand for front door services.

  • ‡Reducing emergency admissions of patients with respiratory or coronary problems, ill-defined conditions and over 65 years.

  • §Effects of earlier discharge of patients admitted as emergencies and subsequently discharged to nursing or residential homes.

  • A&E, accident & emergency department; NA, not applicable.