Optimizing Healthcare Flexible Spending Account Contributions Using Inventory Management Theories: An Interdisciplinary Study
Bibliographic record
Abstract
The healthcare Flexible Spending Account (FSA) offers employees significant tax benefits by allowing the use of pre-tax funds for healthcare expenses, but it also carries the risk of forfeiting unspent funds. To address the challenge of maximizing tax savings while minimizing the risk of forfeiture, this study applies inventory management and economic theories to develop a heuristic for optimizing FSA contributions. Both basic and extended versions of FSAs are examined, alongside the impact of marginal tax rates on decision-making. A simulation model demonstrates the effectiveness of the proposed heuristic, with results showing minimal deviations from the optimal solutions—less than 0.42% for the basic version and 3.11% for the extended version, and performance differences of less than 0.005% and 0.08%, respectively. By integrating operations management, economic theories, and personal finance research, this study introduces a novel decision-support tool for optimizing FSA contributions, while also laying the groundwork for future research in this interdisciplinary field.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".