Association of Health Plans' Healthcare Effectiveness Data and Information Set (HEDIS) Performance With Outcomes of Enrollees With Diabetes
Bibliographic record
Abstract
BACKGROUND: Few quality of care evaluations examine the relationship between clinical processes and patient outcomes. OBJECTIVE: To determine the association between health plan performance on Healthcare Effectiveness Data and Information Set (HEDIS) clinical processes and intermediate outcome measures and Health Outcomes Survey (HOS) self-reported physical and mental health scores among Medicare plan enrollees with diabetes. RESEARCH DESIGN: Secondary data analysis of 2002 HEDIS and 2001-2003 HOS data. SUBJECTS: This study focused on Medicare plan enrollees with self-reported diabetes (N = 8184). MEASURES: Plan-level HEDIS diabetes care measures for 2002 and longitudinal, patient-level 2001-2003 HOS physical and mental health outcomes scores. Hierarchical linear models estimated the relationship between plan HEDIS performance on diabetes process of care and intermediate outcome measures and 2-year changes in enrollee HOS physical and mental health scores. RESULTS: Each 10% point improvement in plan performance on HEDIS intermediate outcomes (ie, the proportion of well-controlled diabetes) was related to significant positive increase in the probability of being healthy as measured by both enrollee physical health scores (7 percentage point increase, P < 0.05) and mental health scores (11 percentage point increase, P < 0.01). Similar increases in plan process of care measures were associated with increases in the probability of being healthy as measured by enrollee mental health scores (11 percentage point increase, P < 0.001). CONCLUSIONS: This study represents one of the first attempts to link plan HEDIS performance to changes in enrollee health. The results suggest that improved quality of care, as measured by process and intermediate outcomes measures for diabetes, can result in better health among patients with diabetes. Further research should address whether this relationship exists in other quality measures, clinical conditions, and populations.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".