A NATIONWIDE EXAMINATION OF MEDICARE PART B UTILIZATION DURING HOSPICE ELECTION
Bibliographic record
Abstract
Abstract This research characterizes trends in two hospice-specific modifiers: (1) “GV” indicating services for the terminal/related conditions by an attending physician not an employee of the hospice and (2) “GW” indicating physician services unrelated to terminal/related conditions. We identified Part B (carrier/physician supplier) claims during hospice elections in Federal Fiscal Year (FY)2020, and replicated an Office of Inspector General (OIG) approach calculating potentially “questionable” Part B claims, where the physician and diagnosis codes match between the hospice and Part B claims (without a GW modifier listed). Using logistic regression, we calculated adjusted odds ratio (AOR) and 95% confidence intervals (CI) to characterize this billing. Overall, $372.8 million in physician services occurred during hospice elections in FY2020. Of this, two-thirds ($247.8 million) included a GW modifier, one-quarter ($86.5 million) a GV modifier, $2.2 million both modifiers, and $40.8 million neither modifier. Replicating the OIG methodology, we calculated $19.4 million (5.2%) as “questionable”. Beneficiaries electing hospice for 180+ days were three times more likely (95% CI 2.99-3.12) to have questionable billing as a beneficiary electing hospice 14-29 days, and facility residents were more likely to have questionable billing, relative to beneficiaries in their own homes. Questionable billing rates were also highest in the northeastern quadrant of the country. Lastly, we found ten percent of physicians accounted for almost three-quarters of all questionable billing. CMS should further monitor physician services during hospice to maintain the integrity of the benefit and ensure beneficiaries receive adequate care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".