Data migration, validation and implementation of a new laboratory information system (LIS) in an academic pathology department, using Ellkay data archive, and Epic Beaker anatomic and clinical pathology modules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Implementation of a new laboratory information system (LIS) poses a significant challenge, amplified when synchronous with launch of a new electronic medical record (EMR) system. Our institution made an executive decision to switch to Epic EMR and Epic Beaker LIS from Cerner Soarian/Altera Sunrise EMR and Cemer CoPath Plus LIS in anatomic pathology and molecular genetic pathology, with a simultaneous go-live date. This synchronous migration required a complete overhaul in our department of laboratory medicine, impacting all standard operating procedures (SOPs). In our efforts to minimize potential risks, we pursued a phased approach to comprehensive validation, starting with iterative rounds of optimization, ending with the final round of validation assessing 45 consecutive pathology cases, simulating the entire workflow in a dry-lab setting, from ordering to reporting, including addenda, with additional cases tested for specific workflow steps. In addition, we pursued validation of result component migration, in form of legacy pathology results to the Epic EMR, and the Ellkay archiving system. We found that our SOP adaptations for Epic Beaker reproduced >99% of the workflows previously established using CoPath Plus. The validation performed was limited to Epic Beaker LIS functionality, and, post-go-live, deficiencies were uncovered largely upstream of the LIS. Based on our experience, we formed a framework for systematic validation of LIS workflows, and share our comprehensive handbook, detailing all workflows built before go-live.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it