P‐IT‐7 | Implementation of a New Laboratory Information System in a Blood Center Based Immunohematology Reference Laboratory: A Single‐Center's Experience
Bibliographic record
Abstract
Study Design/Methods: Experts in Transfusion Medicine and Informatics collaborated to develop a userfriendly tool to automate blood bank data analytics for a group of hospitals using the same LIS.Initially, a limited volume of detailed blood product and patient history was extracted from LIS and validated by two different parameters: comparing product counts defined in a certain period to utilization reports generated by manual tallying and comparing the extracted data parameters to the actual data within the LIS.The dataset was expanded by repetitive data extracts and validations.The QADB was then designed, and the validated blood bank data was uploaded and revalidated to show that it accurately reflected the dataset.Once this was confirmed, an extract-transform-load (ETL) was created to allow for daily data flow from the LIS to the QADB.This daily extract was then imported into Microsoft Power BI to create various dashboards and visualizations for data review and analysis for end users.Results/Findings: Results: The capability to easily manipulate, analyze and share the data has made a great impact on our data collection and quality management programs.Blood bank personnel are now able to retrieve accurate and timely statistics by using Microsoft Power BI, Excel pivot tables and analytical tools available in any computer with access to Microsoft 365.Interactive dashboards allow filtering and color-coding to enhance visualization.The workflow and examples of dashboards can be seen in Figure 1.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".