Statistical method for predicting when patients should be ready on the dayof surgery

Citation
F. Dexter et Rd. Traub, Statistical method for predicting when patients should be ready on the dayof surgery, ANESTHESIOL, 93(4), 2000, pp. 1107-1114
Citations number
21
Categorie Soggetti
Aneshtesia & Intensive Care","Medical Research Diagnosis & Treatment
Journal title
ANESTHESIOLOGY
ISSN journal
00033022 → ACNP
Volume
93
Issue
4
Year of publication
2000
Pages
1107 - 1114
Database
ISI
SICI code
0003-3022(200010)93:4<1107:SMFPWP>2.0.ZU;2-4
Abstract
Background: Previously, mathematical Cheery was developed for determining w hen a patient should be ready for surgery on the day of surgery. To apply t his theory, a method is needed to predict the earliest start time of the ca se. Methods: The authors calculated a time estimate such that the probability i s 0.05 that the preceding case in the patient's operating room (OR) will be finished before the patient is ready for surgery. This implies there will be a 5% risk of OR personnel being idle and waiting for the patient. This 0 .05 value was chosen by considering the relative cost valuation of an avera ge patient's time to that of an average surgical team based on national sal ary data. Case duration data from a surgical services information system we re used to test different statistical methods to estimate earliest start ti mes. Results: Simulations found that 0.05 prediction bounds, calculated assuming case durations followed log-normal distributions, achieved actual risks fo r the OR staff to wait for patients of 0.050 to 0.053 (SEM = 0.001). Nonpar ametric prediction bounds performed no better than the parametric method. H aving patients ready a fixed number of hours before the scheduled starts of their operations is not reliable. If the preceding case In an OR had been underway for 0.5 to 1.5 h, the parametric 0.05 prediction bounds for the ti me remaining achieved actual risks for OR staff waiting of 0.055 to 0.058 ( SEM = 0.001). Conclusion: The earliest start time of a case can be estimated using the 0. 05 prediction bound for the duration of the preceding case. The authors sho w 0.05 prediction bounds can be estimated accurately assuming that case dur ations follow log-normal distributions.