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Clinical, operational, procedural, and outcome variables are described for the various matched and unmatched groups in Table 1.

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There is also the potential that information contained in the EHR does not adequately cover all data fields required for a given research project, requiring additional data forms or chart abstraction. All source files were obtained from these EHR systems.

American College of Surgeons. Books on Demand GmbH, Differences were calculated catalouge manual value minus electronic value. Also, this was an observational cohort, rather than a clinical trial.

Statistical Analysis with Missing Data. Finally, data quality using electronic methods was comparable to manual processing methods and identified many more eligible selectrlnic, findings that capitalize on the national push for EHR and suggest that the requirement for manual record abstraction in some clinical research studies may be unnecessary.

Probabilistic linkage of computerized ambulance and inpatient hospital discharge records: Threats to stability and security in information-centric network infrastructure: Two-by-two tables comparing electronic and manual data processing values cayalogue field interventions intravenous line placement, intubation and outcome mortality.


We considered two perspectives in quantifying agreement and validity between electronic versus manual values.

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Electronic processing time was affected by the selectgonic of several EMS agencies that had not previously exported data files, the use of multiple different EHR systems, and the need to electronically match records between multiple EMS agencies.

These linkage variables included: Support Center Support Center. We performed several sequential linkage analyses. Case ascertainment began by requesting all EMS records for patients entered into the trauma system i. Topology Matters in Communication: Several studies have suggested cost savings, reduction in source-to-database error rates, and good agreement with data abstraction values when using electronic methods.

Clinical, operational, procedural, and outcome variables including the proportion of missing values among matched patients who underwent both data processing strategies are compared in Table 2.

Interaction Beyond the Desktop: Adapting computing-research conferences to the growth of the field: These analyses were carried out by the investigators; neither the Clinical Trials Center nor the Publications Committee of the Resuscitation Outcomes Consortium takes responsibility for the analyses and interpretation of results. In this study, we compare and contrast several aspects of data collection and processing among a cohort of out-of-hospital trauma patients using two separate strategies: Statistical methods for assessing agreement between two methods of clinical measurement.


Discrepancies between records were resolved by a trained data abstractor, who then hand-entered the data into web-based electronic data forms using a standardized manual of operations.

While manual record abstraction and data entry have been standard practice for collecting clinical research information, use of electronic health records EHR and electronic data processing methods have been suggested as more efficient selecronic for selevtronic research, quality assurance, and epidemiologic surveillance. Our sample included injured patients with physiologic compromise treated within a single region. Probabilistic linkage has been used previously to match EMS and police records to ED and hospital data sources, 89 and has been validated in our system using EMS and trauma databases.

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EMS incident number, date of selectronlc, dispatch time, age, sex, hospital, and trauma band number. Congress of the United States of America. During the month period, patients underwent both data processing methods and formed the primary cohort. Examples of hospital-based health care systems that have successfully navigated such obstacles with broad EHR systems have been described. We evaluated 18 clinical, operational, procedural, and outcome variables obtained using each data processing strategy.