Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department

Emergency department crowding: towards an agenda for evidence-based intervention: Figure 1

“…Lack of inpatient beds which force EDs to keep patients who are ready for discharge is the most mentioned output constraint 28 29 33 37 38 40 49 50. Lack of inpatient beds was reported to result from general lack of provision, delays in cleaning,38 40 lack of staff to care for new patients,40 delays in inpatient discharge,40 or increased demand for beds 41.…”

“…Lack of inpatient beds was reported to result from general lack of provision, delays in cleaning,38 40 lack of staff to care for new patients,40 delays in inpatient discharge,40 or increased demand for beds 41. In the USA, it is argued that financial incentives serve to prioritise elective surgery over emergency admissions 33 49.…”

“…Admitting patients to wards before all investigations are completed and without assessment by specialists is common in the UK, but proposed as a solution in the US literature 34. Cancelling elective care can create extra capacity,1 38 but has negative implications for the wider hospital. It is also helpful for in-patient teams not to admit elective patients at peak times and again match inpatient resources to demand by offering a 24/7 operating schedule 1…”

Square peg in a round hole: re-thinking our approach to evaluating health system strengthening in low-income and middle-income countries

“…ABM and SDM are well-known methods for modelling complex sociotechnical dynamic systems, including the dynamic behaviour of an accident and emergency department23 24; accountable care organisations25; accountable care organisations25; policies to make care more affordable26; vaccine distribution systems27; and neonatal health policies28among many others (eg,  29). These models can be used to evaluate and quantify ex ante the response of a health system to any stimulus, including potential adverse events,30 be it for a new programme, or additional resources, a sudden disease outbreak or a natural disaster.…”

“…These models can be used to evaluate and quantify ex ante the response of a health system to any stimulus, including potential adverse events,30 be it for a new programme, or additional resources, a sudden disease outbreak or a natural disaster. The models would provide a computational experimental framework for optimising the performance of a health system23 31 32 prior to in vivo testing of pilot programmes, and for enhancing the resilience of a health system to potential exogenous disturbances. Models can also be used to inform the design of subsequent empirical evaluations.…”