Using Return on Investment Operational and Monte Carlo Modeling Techniques to Predict Financial Performance in a Tertiary Care Outpatient Clinic

Introduction: The vast majority of health care quality improvement studies provide inadequate financial analysis to accurately predict a return on investment. We hypothesized that using return on invested capital operational mapping combined with a Monte Carlo simulation financial model could accurately predict institutional costs and operational metrics within an outpatient urology clinic. Methods: A process map of a typical outpatient clinic visit was developed, and time studies were performed by following a sample of patients while considering all operational and financial variables that contributed to patient care. this process map was adapted into a return on invested capital-tree for financial modeling. Stochastic modeling using Monte Carlo simulation was performed to estimate financial metrics based on these operational and financial inputs for both the 2017-2018 and 2018-2019 fiscal years. These were then compared to the actual performance measures of those fiscal years. Results: Combined return on invested capital-Monte Carlo simulation modeling generated financial and operational estimates that characterized the clinic's performance based on multivariable inputs. Most financial estimates for 2017-2018 differed by <4.31% from the actual financial values from that year. In predicting financial performance for 2018-2019, most of the estimated values were <7.67% different from their actual financial statement line items. Conclusions: As a proof of concept, this study demonstrated that a combined return on invested capital-operational mapping and Monte Carlo simulation modeling can predict key financial metrics in a tertiary care clinic. As such, common business tools can be useful in a health care setting when clinicians are evaluating how investments in quality improvement will influence their financial and operational performance.