A Dashboard for Tracking Mortality After Cardiac Surgery Using a National Administrative Database
Bibliographic record
Abstract
BACKGROUND: Mortality after cardiac surgery is publicly reportable and used as a quality metric by national organizations. However, detailed institutional comparisons are often limited in publicly reported ratings, while publicly reported mortality data are generally limited to 30-day outcomes. Dashboards represent a useful method for aggregating data to identify areas for quality improvement. METHODS: We present the development of a dashboard of cardiac surgery performance using cardiac surgery admissions in a national administrative dataset, allowing institutions to better analyze their clinical outcomes. We identified cardiac surgery admissions in the Medicare Limited Data Sets from April 2016 to March 2017 using diagnosis-related group (DRG) codes for cardiac valve and coronary bypass surgeries. RESULTS: Using these data, we created a dashboard prototype to enable hospitals to compare their individual performance against state and national benchmarks, by all cardiac surgeries, specific cardiac surgery DRGs and by specific surgeons. Mortality rates are provided at 30, 60 and 90 days post-operatively as well as 1 year. Users can filter results by state, hospital and surgeon, and visualize summary data comparing these filtered results to national metrics. Examples of using the dashboard to examine hospital and individual surgeon mortality are provided. CONCLUSIONS: We demonstrate how this database can be used to compare data between comparator hospitals on local, state and national levels to identify trends in mortality and areas for quality improvement.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".