An Introduction to Health Care Administrative Data
Bibliographic record
Abstract
Health care administrative data are generated at every encounter with the health care system, whether through a visit to a physician’s office, a diagnostic procedure, an admission to hospital, or receipt of a prescription at a community pharmacy. The terms “health care utilization data”, “administrative health care billing records”, “administrative claims data”, or simply “claims data” are synonymous with “health care administrative data”. These data are collected for administrative or billing purposes, yet may be leveraged to study health care delivery, benefits, harms, and costs. Pharmacists play a key role in the health care system and may be uniquely attuned to identify important pharmacy practice and pharmacotherapy questions that can be answered with health care administrative data. However, before embarking on a new research study, a funda mental understanding of the strengths and limitations of these data for research is imperative. In this primer, we introduce the common types of health care administrative data and how they may be used to understand professional community pharmacy services, drug utilization, and drug safety and effectiveness.
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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 | 0.046 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.028 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".