PubMed · 3634970
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S H Salmen, B Hagen, M Patacky. 1986. Managing down-staffing.. https://pubmed.ncbi.nlm.nih.gov/3634970/
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STUDY OBJECTIVE: We measure the effect of various input, throughput, and output factors on daily emergency department (ED) mean length of stay per patient (daily mean length of stay). METHODS: The study was a retrospective review of 93,274 ED visits between April 15, 2002, and December 31, 2003. The association between the daily mean length of stay and the independent variables was assessed with autoregressive moving average time series analysis (ARIMA). The following independent variables were measured per 24-hour period: number of elective surgical admissions, ED volume, number of ED admissions, number of ED ICU admissions, number of ED clinical attending hours, hospital medical-surgical occupancy (hospital occupancy), and day of the week. RESULTS: Three factors were independently associated with daily mean length of stay in time series analysis: number of elective surgical admissions, number of ED admissions, and hospital occupancy. The daily mean length of stay increased by 0.21 minutes for every additional elective surgical admission, 2.2 minutes for every additional admission, and 4.1 minutes for every 5% increase in hospital occupancy. Elective surgical admissions were associated with a maximum of 35 hours of additional ED dwell time. The model accounted for 31.5% of the variability in daily mean length of stay. The final model parameters for the ARIMA analysis were autoregressive term (1) moving average (1). CONCLUSION: Hospital occupancy and the number of ED admissions are associated with daily mean length of stay. Every additional elective surgical admission prolonged the daily mean length of stay by 0.21 minutes per ED patient. Autocorrelation exists between the daily mean length of stay of the current day and the previous day.
Hospital occupancy is a key metric in hospital-capacity planning in Germany, even though this metric neglects important drivers of economic efficiency, for example treatment costs and case mix. We suggest an alternative metric, which incorporates economic efficiency explicitly, and illustrate how this metric can be used in the hospital-capacity planning cycle. The practical setting of this study is the hospital capacity planning process in the German federal state of Rheinland-Pfalz. The planning process involves all 92 acute-care hospitals of this federal state. The study is based on standard hospital data, including annual costs, number of cases--disaggregated by medical departments and ICD codes, respectively--length-of-stay, certified beds, and occupancy rates. Using the developed metric, we identified 18 of the 92 hospitals as inefficient and targets for over-proportional capacity cuts. On the upside, we identified 15 efficient hospitals. The developed model and analysis has affected the federal state's most recent medium term planning cycle.
This paper is the second of two articles discussing the execution and evaluation of implementing a Fast Track program in a West Australian outer metropolitan hospital Emergency Department. The first paper in the series outlined the implementation process over a 12-month period. This current paper presents the findings of the 12-month evaluation utilising Statistical Process Charts. The object of this evaluation was to undertake analysis of data throughout the change process and demonstrate the effectiveness of implementing Fast Track into the Department. Following the implementation of Fast Track, the Department's "Did not wait" rate decreased over a 12-month period from 10% to 5.4%, without a detrimental impact on treatment times for the patients with serious illnesses and injuries. Furthermore, Fast Track resulted in patients with minor injuries or illnesses being seen, treated and discharged within 2 hours of presentation. Indeed the journey time decreased for all Emergency Department patients. As a result of Fast Track, the Emergency Department waiting area is less congested and staff moral has increased. A further consequence of Fast Track is that nurses are providing more advanced clinical services to patients.