Abstract TP364: Optimization of Data Abstraction and Quality Measure Reporting using EHR
Bibliographic record
Abstract
Background and Purpose: Despite significant investment in EHR’s, most stroke centers continue to populate stroke registries, such as Get with the Guidelines - Stroke using manual abstraction. This is necessary because of a lack of EHR integration and persistence of non-discrete required data elements. Methods: American Heart Association (AHA) identified the need to offer a more interoperable version of Get with the Guidelines - Stroke (GWTG-S) as a strategic priority for 2016. AHA engaged a consultant to work with two pilot hospital locations to optimize stroke data collection in the EHR and allowing for automated data population into GWTG-S. The pilot work included: 1) Analysis of current documentation and abstraction methods to support reporting of performance data; 2) Design and implementation of improvement opportunities for a) Discrete capture of data elements for electronic reporting b) Improving clinical documentation and ordering c) Clinical decision support and d) Data structures and transmission. 3) Proof-of-concept data extract report; 4) ROI analysis for improvement opportunities and 5) Proposed implementation strategies and timelines for improvement opportunities. Results: The pilot sites implemented Epic tools including Orders, Notes, Narrators and Flowsheets to increase the percentage of discrete elements available for uploading into GWTG-S and to populate hospital quality reporting systems Conclusion: The optimization process improved stroke team ability to:1) Decrease manual abstraction, and allow the stroke team to conduct more concurrent review and handle additional patients without additional staff; 2) Quantify data collection; abstraction; and analysis processes for the emergency department team; neurology; interventional team; and stroke unit; 3) Leverage EHR tools for the clinical team to improve utilization; and 4) Demonstrated the critical collaboration between clinical operations and the information technology team.
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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 | 0.064 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".