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

Abstract 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.