A. Ramamurthi1, X. Li1, P. Deshpande2, R. Swanson1, T. Duris1, K. R. Brown1, K. K. Christians1, D. B. Evans1, A. N. Kothari1 1Medical College Of Wisconsin, Department Of Surgery, Milwaukee, WI, USA 2Marquette University, Electrical And Computer Engineering, Milwaukee, WI, USA
Introduction:
Optimal utilization of time in the operating room (OR) is crucial for ensuring consistent high-quality surgical care while also minimizing costs to the healthcare system. Accurate case length estimation is an essential aspect of surgical scheduling, but the state of the current system at our institution has not yet been assessed. The aim of this study was to quantitatively characterize the accuracy of case length estimation at a single center and identify opportunities for improvement.
Methods:
We queried our institutional National Surgical Quality Improvement Program (NSQIP) database for all patients overthe age of 18 who underwent elective surgery between 2017-2022. We subsequently collected case-specific informationfrom the Clinical Research Data Warehouse (CRDW) including EPIC EHR-generated estimated case length, and actual case length defined as wheels in to wheels out time. Multispecialty cases were categorized according to the primary surgeon that posted the case. All cases not performed in main operating rooms (e.g., interventional/GI and labor/delivery procedures) were removed from the analysis.
Results:
A total of 6581 cases were included in the analysis with 53.87% classified as general surgery cases. The average error in estimated case length was 71 minutes with a standard deviation (SD) of 79 minutes. Cases were considered accurately predicted if estimated case length was within 20% of the actual case length. 26.0%, 64.0%, and 10.0% of cases were accurately, under, and over-predicted, respectively. Multi-specialty surgeries accounted for 19.1% of all cases with 25.2%of those cases predicted accurately while 57.6% were under-predicted and 17.2% were over-predicted. Subgroup analysis by surgical specialty revealed that case length was more often underpredicted than overpredicted across all specialties (Figure 1). Vascular surgery cases were the most poorly predicted with 12.1% accuracy while plastic surgery had the highest percentage of accurately predicted cases at 31.6%. Cardiac and plastic surgery had the most significant average error in estimated case length at 106mins (SD 73mins) and 97mins (SD 111mins) respectively, followed by neurosurgery at 87mins (SD 75mins) and ENT at 76mins (SD 138mins).
Conclusion:
Descriptive analysis of existing case length estimates compared to actual case lengths demonstrates significant inaccuracies with a tendency to underpredict across all subspecialties and subgroups. Certain specialties had worse predictive accuracy but constituted a smaller percentage of the population. Further analysis on a larger retrospective cohort would be beneficial in better characterizing these trends. Improved case length estimation algorithms could dramatically improve OR utilization while reducing surgeon and support staff idle time and optimizing hospital finances.
