Billing and insurance-related administrative costs in United States’ health care: synthesis of micro-costing evidence
Bibliographic record
Abstract
BACKGROUND: The United States' multiple-payer health care system requires substantial effort and costs for administration, with billing and insurance-related (BIR) activities comprising a large but incompletely characterized proportion. A number of studies have quantified BIR costs for specific health care sectors, using micro-costing techniques. However, variation in the types of payers, providers, and BIR activities across studies complicates estimation of system-wide costs. Using a consistent and comprehensive definition of BIR (including both public and private payers, all providers, and all types of BIR activities), we synthesized and updated available micro-costing evidence in order to estimate total and added BIR costs for the U.S. health care system in 2012. METHODS: We reviewed BIR micro-costing studies across healthcare sectors. For physician practices, hospitals, and insurers, we estimated the % BIR using existing research and publicly reported data, re-calculated to a standard and comprehensive definition of BIR where necessary. We found no data on % BIR in other health services or supplies settings, so extrapolated from known sectors. We calculated total BIR costs in each sector as the product of 2012 U.S. national health expenditures and the percentage of revenue used for BIR. We estimated "added" BIR costs by comparing total BIR costs in each sector to those observed in existing, simplified financing systems (Canada's single payer system for providers, and U.S. Medicare for insurers). Due to uncertainty in inputs, we performed sensitivity analyses. RESULTS: BIR costs in the U.S. health care system totaled approximately $471 ($330 - $597) billion in 2012. This includes $70 ($54 - $76) billion in physician practices, $74 ($58 - $94) billion in hospitals, an estimated $94 ($47 - $141) billion in settings providing other health services and supplies, $198 ($154 - $233) billion in private insurers, and $35 ($17 - $52) billion in public insurers. Compared to simplified financing, $375 ($254 - $507) billion, or 80%, represents the added BIR costs of the current multi-payer system. CONCLUSIONS: A simplified financing system in the U.S. could result in cost savings exceeding $350 billion annually, nearly 15% of health care spending.
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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 | 0.020 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".