Journal article
Estimating the incidence of prolonged turnover times and delays by time of day
Anesthesiology, Vol.102(6), pp.1242-1248
06/2005
DOI: 10.1097/00000542-200506000-00026
PMID: 15915039
Abstract
Prolonged turnover times cause frustration and can thereby reduce professional satisfaction and the workload surgeons bring to a hospital. The authors analyzed 1 yr of operating room information system data from two academic, tertiary hospitals and Monte-Carlo simulations of a 15-operating room hospital surgical suite. Confidence interval widths for the mean turnover times at the hospitals were negligible when compared with the variation in sample mean turnover times among 31 hospitals. The authors developed a statistical method to estimate the proportion of all turnovers that were prolonged (> 15 min beyond mean) and that occurred during specified hours of the day. Confidence intervals for the proportions corrected for the effect of multiple comparisons. Statistical assumptions were satisfied at the two studied hospitals. The confidence intervals achieved family-wise type I error rates accurate to within 0.5% when applied to between five and nineteen 4-week periods of data. The diurnal pattern in the proportions of all turnovers that were prolonged provided different, more managerially relevant information than the time course throughout the day in the percentage of turnovers at each hour that were prolonged. Benchmarking sample mean turnover times among hospitals, without the use of confidence intervals, can be valid and useful. The authors successfully developed and validated a statistical method to estimate the percentage of turnover times at a surgical suite that are prolonged and occur at specified times of the day. Managers can target their quality improvement efforts on times of the day with the largest percentages of prolonged turnovers.
Details
- Title: Subtitle
- Estimating the incidence of prolonged turnover times and delays by time of day
- Creators
- Franklin Dexter - Division of Management Consulting, Department of Anesthesia, The University of Iowa, Iowa City, IA 52242, USA. franklin-dexter@uiowa.eduRichard H EpsteinEric MarconJohannes Ledolter
- Resource Type
- Journal article
- Publication Details
- Anesthesiology, Vol.102(6), pp.1242-1248
- Publisher
- United States
- DOI
- 10.1097/00000542-200506000-00026
- PMID
- 15915039
- ISSN
- 0003-3022
- eISSN
- 1528-1175
- Language
- English
- Date published
- 06/2005
- Academic Unit
- Statistics and Actuarial Science; Health Management and Policy; Anesthesia; Business Analytics
- Record Identifier
- 9983806281602771
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