Name
Abstract | ||
|---|---|---|
Operating rooms (ORs) are one of the most expensive and profitable resources within a hospital system. OR managers strive to utilize these resources in the best possible manner. Traditionally, surgery durations are estimated using a moving average adjusted by the scheduler (adjusted system prediction or ASP). Other methods based on distributions, regression and data mining have also been proposed. To overcome difficulties with numerous procedure types and lack of sufficient sample size, and avoid distributional assumptions, the main objective is to develop a hybrid method of duration prediction and demonstrate using a case study. |
Year | Venue | Keywords |
|---|---|---|
2015 | AMIA | Classification,hybrid method,prediction,regression,surgery times |
Field | DocType | Volume |
Data mining,Regression,Regression analysis,Computer science,Organizational Case Studies,Procedure types,Statistics,Moving average,Sample size determination | Conference | 2015 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Narges Hosseini | 1 | 0 | 0.34 |
| Mustafa Sir | 2 | 42 | 9.57 |
| Christopher Jankowski | 3 | 0 | 0.34 |
| Kalyan S. Pasupathy | 4 | 21 | 9.19 |