DiCesare R, Toor J, Wolfstadt J et al: Using return on investment operational and Monte Carlo modeling techniques to predict financial performance in a tertiary care outpatient clinic. Urol Pract 2021; 8: 487.

As the costs of care rise and health systems gravitate toward value-based care models, quality improvement (QI) initiatives are becoming increasingly important to optimize both care and value.¹ However, one of the major challenges in addressing the value of QI interventions is the lack of financial and operational assessment.² Unfortunately, clinicians are given scarce guidance on how to empirically calculate costs to understand how investments in QI will impact their clinical practice or institution.³ Thus, this project sought to explore how financial and operational modeling can be utilized by physicians and health care decision makers to understand the financial and operational measures that influence their practice.

Figure. Return on invested capital (ROIC) tree derived from operational and financial parameters from process mapping. RN, registered nurse. NP, nurse practitioner. FC, flow coordinator.

Return on invested capital (ROIC) trees are used in business to measure the anticipated return of a business unit against invested capital.⁴ Furthermore, Monte Carlo simulation (MCS) is a method of probability analysis performed by substituting a probability distribution for multiple variables that have inherent uncertainty.⁵ According to the academic literature, given the unpredictable nature of health care, combining an ROIC with an MCS analysis can be useful when evaluating the impact of health care investments on costs and outcomes.⁵ We believed that translating these business tools for use in health care could provide the opportunity to illustrate not only the cost of implementing a QI project, but also the changes in revenue and expenses that occur as a result of the project. The primary aim of this project was to determine, as a proof of concept, if ROIC operational mapping followed by an MCS financial model would accurately predict realizable financial impact within an organization.⁶

To accomplish this goal, we first process mapped a typical outpatient clinic visit, while considering all operational and financial variables that contributed to patient care. The map and its variables were adapted into an ROIC tree for financial modeling (see figure). An MCS analysis was then incorporated, conducting 1,000 iterations based on the mean, range, and standard deviation of these variables. The average value of the simulation was then placed back into the ROIC tree to generate predictions for the various financial parameters, which were then compared to the actual financial statements from 2 of the clinic’s fiscal years. The model’s estimations compared quite favorably to documented expenses and revenue, with most values differing less than 5% for 2017–2018. In predicting financial performance for 2018–2019, most of the estimated values were less than 8% different from their actual financial statement line items.

In this proof-of-concept study, we have shown how ROIC and MCS techniques can be leveraged to characterize and quantify costs in our pediatric outpatient urology clinic. Our results demonstrate how the combination of these higher level financial and mathematical models can, in theory, allow for the appropriate assessment of QI interventions within a health care setting that rely on numerous, interrelated variables. We believe that this can become an important tool for clinicians to empirically assess the fiscal impact of QI programs, as well as to define and articulate the total value of care to involved stakeholders. In doing so, we can take another step forward to financial sustainability in health care.