Factors Associated With Hospices’ Nonparticipation in Medicare’s Hospice Compare Public Reporting Program
Bibliographic record
Abstract
BACKGROUND: To enhance the quality of hospice care and to facilitate consumers' choices, the Centers for Medicare and Medicaid Services (CMS) began the Hospice Quality Reporting Program, in which CMS posted the quality measures of participating hospices on its reporting website, Hospice Compare. Little is known about the participation rate and the types of nonparticipating hospices. OBJECTIVE: To examine the factors associated with hospices' nonparticipation in Hospice Compare. RESEARCH DESIGN: We analyzed data from the CMS 2016 Hospice Compare. "Nonparticipants" were those who did not submit any quality measure. With the data of the Provider of Service file, the Healthcare Cost Report Information System, and the Area Health Resources File, multivariate logistic regressions estimated the association between nonparticipants and hospice and market characteristics, including ownership, size, nurse staffing ratio, and market competition intensity. RESULTS: Among the 4123 certified hospices subject to penalty from nonparticipation, 259 did not participate in Hospice Compare. California, New Mexico, Texas, and Wyoming had participation rates lower than 80%. Hospices that were for-profit, had no accreditation, had few nurses per patient day, provided no inpatient care, and were located in competitive markets were less likely to participate than other hospices. CONCLUSIONS: Hospice Compare successfully motivated hospice in participating in the quality report program in most of states. For-profit hospices, hospices with less quality, and hospices located in competitive markets were less likely to participate. Further research is warranted to examine the quality of these nonparticipants, especially in the 4 states with a lower participation rate.
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 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".