Interface software can markedly reduce time and improve accuracy for clinical trial data transfer from EMR to EDC: The results of two measure of work time studies comparing commercially available clinical data transfer software to current practice manual data transfer.
Bibliographic record
Abstract
1573 Background: Clinical studies and new drug approvals are delayed by slow data transfer and transcription errors from site entered data. These delays have been compounded by a shortage of data entry personnel at sites such that data entry approaches 10-30 days post visit. Data transfer is largely performed by manual keyboard entry from electronic medical records (EMR) into electronic case report forms (ECRF). Methods: We conducted two separate measure-of-work time studies to compare a commercially available interface software product, ProXimity to the current manual data entry. The clinical trial data from two different EMRs (ARIA and IKM G2) to an EDC (Medidata Rave). The EDC mirrored an IRB approved clinical study. Time to transfer data and error rates were the primary and secondary endpoints, respectively. For study 1 Aria EMR data from 3 subjects and 1497 data fields including demographics, vital signs, ECOG PS, physical findings, adverse events, and lab results including CBC, CMP, urinalysis, coagulation, serology were selected for visits from Screening and C2D1. For Study 2 IKM G2 data from 6 subjects and 834 data fields included demographics, vitals, and lab results. The data entry personnel were aware of the timed nature of the study. Results: Study 1 ProXimity took 13.2 min to transfer the data compared to 73.4 min for manual entry, with error rates of 0.8% compared to 3.5%, respectively. In Study 2 Proximity took 6.5 min compared to 29 min for manual entry, with error rates of 1.4% each due to non- conformant data (text). Conclusions: Software data transfer interfaces can markedly shorten the time for data entry, reduce error rates and reduce operational costs.
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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 | 0.035 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".