Background: Operative suites (OS) are highly specialized areas of a hospital where complex surgical procedures are performed. OS utilization has a direct impact on the hospital budget as it influences the allocation of a large array of resources. Within the VHA system the annual OS related expenses have been increasing by 5.4% annually for the last six years. Official data indicate the presence of substantial variability in OS utilization throughout the VHA and indicate that maximizing OS efficiency is of paramount importance for the VHA mission. Streamlined workflow and successful prediction of time length for individual operations are the two key determinants of OS efficiency. Operative time length has been traditionally calculated as the mean historic operative time for a particular intervention. However, this value accurately predicts the surgical length in only a small fraction of cases. To address this issue we used patient, surgeon, and OR characteristics and developed a regression model based methodology that can predict the operative and anesthetic case length for all major types of vascular surgical procedures. We validated this method in out of sample patient populations and showed that it can greatly improve the precision of predicting surgical case duration. Use of such a predictive modeling to assess improvement in OS efficiency has not been previously reported. Objectives: The goal of the proposed study is to address the efficacy of a scheduling methodology that uses a regression-based predictive modeling system (PMS) to calculate operative and anesthetic time length. We hypothesize that compared to the traditional scheduling system (TSS) that calculate operative length using historic means, case allocation in an operating room using the PMS will improve scheduling precision, increase operative volume and increase OS personnel satisfaction, without having adverse impact on patient outcomes. Methods: We will use a randomized block design according to which blocks of weekly schedules will be randomly structured using either the TSS or the PMS methods for a total of 100 operative days.
Specific Aim 1. Evaluate the impact of the PMS vs. TSS on operating room utilization. Hypothesis 1.1. PMS results in greater scheduling precision compared to TSS. The primary endpoint will be the overall time (in minutes) of scheduling imprecision, defined as the room over- or under-utilization; the anticipated end of the operative day will be determined each morning using either the TSS or the PMS methods; the length of time in minutes of over- or under-utilization will be calculated at the end of the day to determine the daily scheduling imprecision. Hypothesis 1.2. PMS is associated with increased operating room productivity. We anticipate that the improved scheduling precision will result in increased operative volume when the operative schedule is constructed based on the PMS. A clinically relevant increase by at least 10% in throughput using the PMS is hypothesized.
Specific Aim 2. Assess the impact of the PMS on OS personnel satisfaction Hypothesis 2.1. The PMS is associated with superior OS personnel satisfaction. We anticipate that improved scheduling precision that reduces uncertainty during the day and decrease the need for overtime will increase OS staff satisfaction, as this is captured with the Maslach Burnout Inventory that will be given to providers during the last day of the operative week.
Specific Aim 3. Assess the impact of PMS utilization on important perioperative outcomes Hypothesis 3.1. The PMS will not result in increased rate of a composite endpoint of perioperative death, myocardial infarction, stroke, bleeding, early reoperation, and wound infection. Complications will be documented prospectively to assure that introduction of PMS does not place pressures on surgeons to maintain consistent operative time at the expense of quality of provided care.
Costs related to operative suite utilization are increasing at an alarming rate. Maximizing operating suite efficiency via improved scheduling can reduce costs and improve patient satisfaction. There is currently an unfulfilled need for the development of an efficient frontier that optimizes OS functionality and reduces cost. The proposed study is unique in that it directly examines improvements in efficiency due to utilization of a scheduling system that utilizes a model-based methodology to estimate the length of surgical cases. Optimizing scheduling using this approach will be a significant step in reducing cost and decreasing operative backlogs, thus substantially improving patient access to healthcare services.
