Abstract W P284: Stroke CORE Measure Data Submission to CMS: Process Improvement Initiative
Bibliographic record
Abstract
Background: As of January 2013, CMS required hospitals to submit Stroke CORE Measure data on all discharged patients in order to be eligible for full reimbursement in Federal Fiscal Year (FFY) 2014. Failure to do so results in a payment reduction to hospitals. Furthermore, rapid-change-cycle and continuous quality improvement programs suggest that a rapid communication feedback approach can improve compliance with evidence-based practice. To meet impending data-reporting and fiscal challenges, the 5 campus hospital organization implemented strategies in Aug-Dec 2012 to: reduce labor costs related to stroke core measure reporting without increasing risk to full CMS reimbursement; and decrease the FY 2012 lag time between patient discharge, and communication of core measure compliance to clinicians. The purpose of this study was to determine if: (1) savings in labor dollars were achieved; (2) risk of CMS payment reduction did not increase; and (3) reduction in time from discharge to internal reporting was achieved. Methods: Minutes/case abstraction of CORE Measures were directly measured for all inpatient stroke cases discharged in October, 2012/60 minutes. Number of hours was multiplied by the cost of labor/hour to establish a labor cost/case baseline. Time/case was reassessed in April, 2013. Successful submission of data to CMS Jan-March, 2013 was determiner of risk. Retrospective review of May -July, 2012 was used to establish a comparative baseline (equal to 65 days). Days from discharge until data resulted to clinician were audited from January-March, 2013 using quality control tool. Goal was set at 21 days or less. Results: In the time period analyzed, the labor cost related to time spent abstracting each case was reduced by 20%. Risk of failure to follow CMS public reporting rules was not increased, and the first quarter (Jan-March 2013) results were submitted/accepted by CMS with no quality control issues. During the time period reviewed, the days from patient discharge to internal reporting of compliance results was reduced from 65 to 21 or less. Conclusion: The process improvement strategies achieved all goals and clinical improvements during the period reviewed. The sustainability of these results is the subject of on-going, quarterly reassessment.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".