A method to report utilization for quality initiatives in medical facilities.
Bibliographic record
Abstract
OBJECTIVE: We undertook this project to outline a methodology for quantifying aggregate health care utilization of medical "technologies" that could be rank ordered by volume. The identification of specific high-volume technologies could guide future efforts for quality initiatives such as program planning, preventive services implementation, quality improvement activities, and innovative and cost-effective technology development. DESIGN: This study utilized a retrospective cross-sectional study design. METHODS: We generated combined ranks for the top 200 high-volume procedures from three data sources that incorporated in- and outpatient procedures. Data were collected using primarily ICD-9 and CPT-4 codes; all codes were translated into CPT-4 codes and collapsed into categories using truncated three-digit CPT-4 codes. Frequencies for each collapsed code were determined with each dataset; procedures were reranked based on the mean rank of the three sources. MAIN OUTCOME MEASURES: We itemized the individual procedure codes making up each of the top 20 categories and reported the unique codes making up at least 80% of the procedure code category. RESULTS: The top five procedure categories identified in this study were patient visits (inpatient and outpatient), chest x-rays, mammograms, ophthalmological services, and electrocardiograms. CONCLUSION: The methodology described provides a new way to combine and concisely report on utilization of procedures that is relevant to data obtained from different sources. This methodology may be of potential benefit to health care administrators, technology developers, and other planners as they contemplate ways to identify quality and technology development initiatives that can have a broad impact on populations served by health care organizations.
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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 | 0.048 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".