Are patient ratings of interactions with providers and health plans associated with technical quality of care in systemic lupus erythematosus?
Bibliographic record
Abstract
Prior research has shown that the technical quality of SLE care is associated with the degree of subsequent accumulated damage. However, it is not known whether the nature of interactions between patients and providers and health systems is associated with the technical quality of care. We analyzed data from the UCSF Lupus Outcomes Study (LOS), a national sample of persons with SLE interviewed annually using a structured telephone survey. The survey includes batteries from the Consumer Assessment of Health Plans developed by the US Agency for Healthcare Research and Quality and the Interpersonal Processes of Care Scales to rate care along six dimensions about providers (patient-provider communication, shared decision-making, and trust) and health systems (promptness/timeliness of care, care coordination, and assessment of health plans) from 0 to 100. Due to the fact that the ratings were not normally distributed, we dichotomized the measures at the lowest versus the highest three quartiles. The survey also includes the 13 quality indicators (QIs) for SLE that can be reliably reported by patients. The QIs were aggregated into a pass rate, defined as the number of QIs received as a proportion of those for which individuals are eligible. We used generalized estimating equations to model the relationship of the QI pass rate with being in the lowest quartile of ratings of each individual dimension and with being in the lowest quartile on zero, one to three, and four to six of the dimensions. Models were adjusted for age, race/ethnicity, education, poverty status, presence and kind of health insurance, specialty of principal SLE physician, disease duration, disease activity (SLAQ), and disease damage (BILD). A total of 640 LOS participants with ≥1 visit to their principal SLE provider in the year prior to interview were eligible for analysis. Mean age was 52.8 ± 12.6 years and mean disease duration was 20.1 ± 8.8 years; 38% were nonwhites, and 14% were in poverty. Being in the lowest quartile of ratings on any one individual dimension was not associated with a statistically significant difference in QI pass rates (data not shown). Being in the lowest quartile of ratings on four to six dimensions was associated with significantly lower pass rates (0.63 vs. 0.71 for those in the lowest quartile on no dimensions, P = 0.02) (Table 1 ). Low ratings on multiple dimensions of interactions may be a sentinel for poor technical quality of care. In the USA, ratings of providers and health plans are in the public domain and this information can help persons with SLE choose providers and health plans more likely to achieve high technical quality of care.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".