Division of Management Consulting, Department of Anesthesia, University of Iowa, 200 Hawkins Drive, 6-JCP, Iowa City, IA 52242, United States.
Department of Anesthesiology, Pain Management and Perioperative Medicine, University of Miami, United States.
J Clin Anesth. 2018 Sep;49:118-125. doi: 10.1016/j.jclinane.2018.04.008. Epub 2018 May 18.
Recent studies have made longitudinal assessments of case counts using State (e.g., United States) and Provincial (e.g., Canada) databases. Such databases rarely include either operating room (OR) or anesthesia times and, even when duration data are available, there are major statistical limitations to their use. We evaluated how to forecast short-term changes in OR caseload and workload (hours) and how to decide whether changes are outliers (e.g., significant, abrupt decline in anesthetics).
Observational cohort study.
Large teaching hospital.
35 years of annual anesthesia caseload data. Annual data were used without regard to where or when in the year each case was performed, thereby matching public use files. Changes in caseload or hours among four-week periods were examined within individual year-long periods using 159 consecutive four-week periods from the same hospital.
Series of 12 four-week periods of the hours of cases performed on workdays lacked trend or correlation among periods for 49 of 50 series and followed normal distributions for 50 of 50 series. These criteria also were satisfied for 50 of 50 series based on counts of cases. The Pearson r = 0.999 between hours of anesthetics and cases.
For purposes of time series analysis of total workload at a hospital within 1-year, hours of cases and counts of cases are interchangeable. Simple control chart methods of detecting sudden changes in workload or caseload, based simply on the sample mean and standard deviation from the preceding year, are appropriate.
最近的研究使用州(例如,美国)和省(例如,加拿大)数据库对病例进行了纵向评估。这些数据库很少包含手术室(OR)或麻醉时间,即使有持续时间数据,其使用也存在重大统计限制。我们评估了如何预测 OR 工作量和工作量(小时)的短期变化,以及如何确定变化是否为异常值(例如,麻醉量的显著、突然下降)。
观察性队列研究。
大型教学医院。
35 年的年度麻醉病例数据。每年的数据都没有考虑到每个病例是在一年中的何时何地进行的,因此与公共使用文件匹配。在同一家医院的 159 个连续四周的时间段内,在个人为期一年的时间段内检查了四周内的工作量或小时数的变化。
在工作日进行的 12 个为期四周的病例小时系列中,有 49 个系列的各时间段之间没有趋势或相关性,并且 50 个系列中的 50 个系列遵循正态分布。50 个系列的病例计数也满足这些标准。麻醉小时数和病例数之间的 Pearson r = 0.999。
对于在 1 年内分析医院总工作量的时间序列,病例的小时数和病例数可以互换。基于前一年的样本平均值和标准差,简单的控制图方法适用于检测工作量或病例量的突然变化。