Data Quality Implications in Research when Transitioning to a New Electronic Medical Record
Bibliographic record
Abstract
The province of Alberta has been implementing a new hospital based electronic medical record (EMR) over the past several years based on the Epic system. With this implementation comes challenges related to consistent mapping of data to ensure consistency of data over time before and after implementation, even for standardized and routinely used elements. In a research study evaluating potential disparities in access to Emergency Department (ED) care, particularly in wait times and patient boarding (the time spent in the ED awaiting transfer to an inpatient bed), between mental health (MH) and non-MH patients during the COVID-19 pandemic. An interrupted time series analysis was used to evaluate the impact of the pandemic on boarding time. While there initially appeared to be a large decrease in boarding time near the start of the pandemic, it was later determined to be a data quality issue corresponding to the rollout of the new EMR at one hospital, despite the data coming from a secondary standardized dataset that is used for provincial and national reporting and expected to be reliable. This presentation will highlight how the issue was identified, what steps were taken to confirm the underlying cause, and how it was corrected to ensure accurate results in this research study.
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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 | 0.064 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.014 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".