Improving performance in the ED through laboratory information exchange systems
Bibliographic record
Abstract
BACKGROUND: The accessibility of laboratory test results is crucial to the performance of emergency departments and to the safety of patients. This study aims to develop a better understanding of which laboratory information exchange (LIE) systems emergency care physicians (ECPs) are using to consult their patients' laboratory test results and which benefits they derive from such use. METHODS: A survey of 163 (36%) ECPs in Quebec was conducted in collaboration with the Quebec's Department of Health and Social Services. Descriptive statistics, chi-square tests, cluster analyses, and ANOVAs were conducted. RESULTS: The great majority of respondents indicated that they use several LIE systems including interoperable electronic health record (iEHR) systems, laboratory results viewers (LRVs), and emergency department information systems (EDIS) to consult their patients' laboratory results. Three distinct profiles of LIE users were observed. The extent of LIE usage was found to be primarily determined by the functional design differences between LIE systems available in the EDs. Our findings also indicate that the more widespread LIE usage, the higher the perceived benefits. More specifically, physicians who make extensive use of iEHR systems and LRVs obtain the widest range of benefits in terms of efficiency, quality, and safety of emergency care. CONCLUSIONS: Extensive use of LIE systems allows ECPs to better determine and monitor the health status of their patients, verify their diagnostic assumptions, and apply evidence-based practices in laboratory medicine. But for such benefits to be possible, ECPs must be provided with LIE systems that produce accurate, up-to-date, complete, and easy-to-interpret information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".