Using Return on Investment Operational and Monte Carlo Modeling Techniques to Predict Financial Performance in a Tertiary Care Outpatient Clinic
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".