How the ESRD Quality Incentive Program Could Potentially Improve Quality of Life for Patients on Dialysis
Bibliographic record
Abstract
For over 20 years, the quality of medical care of the Medicare ESRD Program has been a concern. The Centers for Medicare and Medicaid Services have implemented the ESRD Quality Incentive Program, which uses the principles of value-based purchasing; dialysis providers are paid for performance on predefined quality measures, with a goal of improving patient outcomes and the quality of patient care. The ESRD Quality Incentive Program measures have been criticized, because they are largely disease oriented and use easy-to-obtain laboratory-based indicators, such as Kt/V and hemoglobin, that do not reflect outcomes that are most important to patients and have had a minimal effect on survival or quality of life. A key goal of improving quality of care is to enhance quality of life, a patient-important quality measure that matters more to many patients than even survival. None of the ESRD Quality Incentive Program measures assess patient-reported quality of life. As outlined in the National Quality Strategy, the Centers for Medicare and Medicaid Services are holding providers accountable in six priority domains, in which quality measures have been and are being developed for value-based purchasing. Three measures-patient experience and engagement, clinical care, and care coordination-are particularly relevant to quality care in the ESRD Program; the 2014 ESRD Quality Incentive Program includes six measures, none of which provide data from a patient-centered perspective. Value-based purchasing is a well intentioned step to improve care of patients on dialysis. However, the Centers for Medicare and Medicaid Services need to implement significant change in what is measured for the ESRD Quality Incentive Program to be patient centered and aligned with patients' values, preferences, and needs. This paper provides examples of potential quality measures for patient experience and engagement, clinical care, and care coordination, which if implemented, would be much more likely to enhance quality of life for patients with ESRD than present ESRD Quality Incentive Program measures.
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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".