ArticleJournal of medical systems2026
Automated data extraction from electronic medical records for pragmatic clinical trials.
Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data collection in randomized trials is expensive and labor intensive. With the rise in ongoing pragmatic trials, the use of electronic medical records (EMR) as a source of data has increased. Although potentially faster and cheaper, EMR use can lead to errors. Therefore, to ensure accurate data collection and to avoid systematic errors we performed a study comparing automated data extraction (ADE) with manual data extraction (MDE). We performed a retrospective cohort study to compare the accuracy of ADE using Structured Query Language with MDE by blinded physicians from our EMR. We tested the interrater agreement and intraclass correlation coefficient of clinical baseline data and outcomes of a random sample of 30 patients admitted to the ICU, on mechanical ventilation, requiring opioids for analgosedation for an upcoming pragmatic clinical trial. Key data compared included, but not limited to, patient's demographics, laboratory and vital signs, daily morphine milligram equivalent (MME), days alive and free of mechanical ventilation, days alive and free of hospitalization, days alive and free of ICU, days alive and free of vasopressors, and death. Among 238 patients screened over 1-month period, 72 fulfilled inclusion criteria and 30 were randomly selected to be included in the evaluation. We blindly collected 1320 baseline data, 2160 categorical outcomes and 705 continuous outcomes for a total of 4185 data points. The intraclass correlation coefficient and the Cohen's Kappa were perfect or almost perfect for all data, including outcomes such as daily MME, days alive and free of mechanical ventilation, days alive and free of ICU and days alive and free of hospital with p < 0.001. Among all rechecked data, the ADE was correct in 53 (77.9%) of cases, while MDE in 15 (22.1%). The inaccurate data collected by ADE accounted for 0.36% of the total data-points. The performance of ADE had almost perfect agreement for all outcomes and when rechecking for disagreements, it was more accurate than MDE.
Indexed as
Identifiers
42062587What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.