Matching Clinicians to Operative Cases

Background: Patient and surgical case complexity are important considerations in creating appropriate clinical assignments for trainees in the operating room (OR). The American Society of Anesthesiologists (ASA) Physical Status Classification System is the most commonly used tool to classify patient illness severity, but it requires manual evaluation by a clinician and is highly variable. A Risk Stratification System for surgical patients was recently published which uses administrative billing codes to calculate four Risk Stratification Indices (RSIs) and provides an objective surrogate for patient complexity that does not require clinical evaluation. This risk score could be helpful when assigning operating room cases. Objective: This is a technical feasibility study to evaluate the process and potential utility of incorporating an automatic risk score calculation into a web-based tool for assigning OR cases. Methods: We created a web service implementation of the RSI model for one-year mortality and automatically calculated the RSI values for patients scheduled to undergo an operation the following day. An analysis was conducted on data availability for the RSI model and the correlation between RSI values and ASA physical status. Results: In a retrospective analysis of 46,740 patients who received surgery in the year preceding the web tool implementation, RSI values were generated for 20,638 patients (44%). The Spearman’s rank correlation coefficient between ASA physical status classification and one-year mortality RSI values was 0.404. Conclusions: We have shown that it is possible to create a web-based tool that uses existing billing data to automatically calculate risk scores for patients scheduled to undergo surgery. Such a risk scoring system could be used to match patient acuity to physician experience, and to provide improved patient and clinician experiences. The web tool could be improved by expanding the input database or utilizing procedure booking codes rather than billing data.