Hospital's instability wedges.

In this study, we define a hospital congestion episode as a situation where the number of new patients needing admission is greater than the number of available beds in the hospital, and investigate the likelihood that the current day's midnight occupancy will exceed any specified threshold level. We demonstrate that this measure of risk exhibits a characteristic sensitivity phenomenon that we have named as hospital's instability wedge. In particular, it is seen that frequently even small changes in the numbers of patients admitted or discharged can dramatically change the risk of exceeding the threshold, thereby changing the risk of subsequent congestion episodes. While this finding captures a salient difficulty of operating a modern public hospital, it also opens up an opportunity for monitoring and alleviating the above defined risk with only small changes in admission, discharge, and cancellation rates. A case study with recent patient journey data from Flinders Medical Centre in South Australia is presented.

Hospital occupancy and discharge strategies: a simulation-based study.

BACKGROUND: Increasing demand for hospital services has resulted in more arrivals to emergency department (ED), increased admissions, and, quite often, access block and ED congestion, along with patients' dissatisfaction. Cost constraints limit an increase in the number of hospital beds, so alternative solutions need to be explored. AIMS: To propose and test different discharge strategies, which, potentially, could reduce occupancy rates in the hospital, thereby improving patient flow and minimising frequency and duration of congestion episodes. METHODS: We used a simulation approach using HESMAD (Hospital Event Simulation Model: Arrivals to Discharge) - a sophisticated simulation model capturing patient flow through a large Australian hospital from arrival at ED to discharge. A set of simulation experiments with a range of proposed discharge strategies was carried out. The results were tabulated, analysed and compared using common hospital occupancy indicators. RESULTS: Simulation results demonstrated that it is possible to reduce significantly the number of days when a hospital runs above its base bed capacity. In our case study, this reduction was from 281.5 to 22.8 days in the best scenario, and reductions within the above range under other scenarios considered. CONCLUSION: Some relatively simple strategies, such as 24-h discharge or discharge/relocation of long-staying patients, can significantly reduce overcrowding and improve hospital occupancy rates. Shortening administrative and/or some treatment processes have a smaller effect, although the latter could be easier to implement.