Measuring the Quality of Diabetes Care Using Administrative Data: Is There Bias?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Health care organizations often measure processes of care using only administrative data. We assessed whether measuring processes of diabetes care using administrative data without medical record data is likely to underdetect compliance with accepted standards for certain groups of patients. DATA SOURCES/STUDY SETTING: Assessment of quality indicators during 1998 using administrative and medical records data for a cohort of 1,335 diabetic patients enrolled in three Minnesota health plans. STUDY DESIGN: Cross-sectional retrospective study assessing hemoglobin A1c testing, LDL cholesterol testing, and retinopathy screening from the two data sources. Analyses examined whether patient or clinic characteristics were associated with underdetection of quality indicators when administrative data were not supplemented with medical record data. DATA COLLECTION/EXTRACTION METHODS: The health plans provided administrative data, and trained abstractors collected medical records data. PRINCIPAL FINDINGS: Quality indicators that would be identified if administrative data were supplemented with medical records data are often not identified using administrative data alone. In adjusted analyses, older patients were more likely to have hemoglobin A1c testing underdetected in administrative data (compared to patients <45 years, OR 2.95, 95 percent CI 1.09 to 7.96 for patients 65 to 74 years, and OR 4.20, 95 percent CI 1.81 to 9.77 for patients 75 years and older). Black patients were more likely than white patients to have retinopathy screening underdetected using administrative data (2.57, 95 percent CI 1.16 to 5.70). Patients in different health plans also differed in the likelihood of having quality indicators underdetected. CONCLUSIONS: Diabetes quality indicators may be underdetected more frequently for elderly and black patients and the physicians, clinics, and plans who care for such patients when quality measurement is based on administrative data alone. This suggests that providers who care for such patients may be disproportionately affected by public release of such data or by its use in determining the magnitude of financial incentives.
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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 | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it