Abstract 266: No Impact of Electronic Health Records on Quality of Care and Outcomes for Ischemic Stroke
Bibliographic record
Abstract
Background: Electronic Health Records (EHRs) may be a key tool for improving the quality of healthcare. They may be particularly important for conditions such as ischemic stroke, in which guidelines are rapidly evolving and timely care of the patient is critical. Methods: We used data from 1,236 hospitals participating in Get With The Guidelines-Stroke, representing 626,473 ischemic strokes between 2007 and 2010, and linked this with the American Hospital Association annual survey to characterize which study hospitals had an EHR. We conducted regression analyses to determine whether hospitals with an EHR demonstrated better performance on quality metrics, length of stay, discharge to home, and mortality. Results: 511 hospitals had an EHR by the end of the study period. Stroke patients at hospitals with EHRs were younger, more often male and non-white, and had a lower burden of medical comorbidities. Hospitals with EHRs were larger, and more often teaching hospitals and stroke centers than hospitals without EHRs. In unadjusted analyses, patients at hospitals with EHRs were more likely to receive “all-or-none” care (87.9% versus 82.6%, p<0.001), and less likely to have a length of stay over 4 days (42.4% versus 43.9%, p<0.001). However, there were no differences in discharge to a site other than home (50.9% versus 51.1%, p=0.12) or in-hospital mortality (5.3% versus 5.2%, p=0.40). In multivariate analyses, after controlling for patient and hospital characteristics, the presence of an EHR was no longer associated with better quality care, and continued to have no association with clinical outcomes (Table). Conclusions: In our sample of GWTG-Stroke hospitals, EHRs were not associated with higher-quality care or better clinical outcomes. Given that these systems often create significant added burden for clinicians, further work to ensure that they are better integrated with care and fully evidence-driven is critical.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".