Study on Using Linear Regression Modeling to Better Estimate Total Surgical Procedure Time

Abstract

To make the most of the resources available in the operating room, careful planning of the available block time and the patient case sequences is required (OR). These planning methods will only help if the forecasts provided by TPT can be relied upon. This study makes use of linear regression models to improve TPT forecasts. These models are given estimated surgeon-controlled time (eSCT) in addition to other parameters of relevance. Surgical procedures at six Dutch institutions between 2012 and 2016 were evaluated using data from a benchmarking database. There were 79,983 contributors, which resulted in 199,772 hours of OR being reported. We analyzed the risk variables for TPT, which revealed that eSCT, patient age, surgical type, ASA physical status category, and anesthetic procedure were all contributors. During calculating the projected TPT for each file, the eSCT was multiplied by a fixed ratio model (discussed in further detail elsewhere) by a factor of 1.33. According to the findings of the research conducted by van Veen-Berkx et al., a conservative estimate of the period during which anesthesia is stable is around 33 percent of the SCT (ACT). In conclusion, we analyzed different linear regression models to predict TPT. These models included eSCT as one of the independent factors among the other covariates. In the end, each of the regression models was retested, this time to see whether or not it was possible to predict ACT reliably without using eSCT as a covariate (which leads to TPT by adding SCT). The most accurate projections of TPT were generated by a linear regression model that used eSCT, the kind of operation, the ASA classification, and the type of anesthesia. Compared to the fixed ratio model and the ACT prediction approach, the performance of this model was very satisfactory. Significant cost savings and increases in overall productivity may be realized if more accurate estimates were included in the planning and sequencing algorithms used to enhance operating room usage.