Using Return on Investment Operational and Monte Carlo Modeling Techniques to Predict Financial Performance in a Tertiary Care Outpatient Clinic
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it