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This was a secondary analysis of two sets of data sellectronic for a prospective, population-based, out-of-hospital trauma cohort evaluated by 10 emergency medical services EMS agencies transporting to 16 hospitals, from January 1, through October 2, Eighteen clinical, operational, procedural, and outcome variables were collected sekectronic processed separately and independently using two parallel data processing strategies, by personnel blinded to patients in the other group.

The electronic approach included electronic health record data exports from EMS agencies, reformatting and probabilistic linkage to outcomes from local trauma registries and state discharge databases. The manual data processing approach included chart matching, data abstraction, and data entry by a trained abstractor.

Descriptive statistics, measures of agreement, and validity were used to compare the two approaches to selectronicc processing. During the month period, patients underwent both data processing methods and formed the primary cohort.

Agreement was good to excellent kappa 0. In this sample of out-of-hospital trauma patients, an sselectronic data processing strategy seelctronic more patients and generated values with good agreement and validity compared to traditional data collection and processing methods.

The amount of funding allocated to scientific research and development in the United States is large and has continued to climb over the past 50 years. While manual record abstraction and data entry have been standard practice for collecting clinical research information, use of electronic health caatlogue EHR and electronic data processing methods have been suggested as more efficient mechanisms for conducting research, quality assurance, and epidemiologic surveillance.

Several studies have suggested cost savings, reduction in source-to-database error rates, and good agreement with data abstraction values when using electronic methods.

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: We evaluated these strategies using three aspects of data collection and processing: We hypothesized that an all-electronic data collection and processing strategy would yield broader capture of eligible study patients and similar data quality when compared to a more conventional approach.

This was a secondary analysis comparing two separate and independent strategies manual versus electronic for collecting and processing clinical research data for a population—based, out-of-hospital, prospective cohort of trauma patients.

Each data processing strategy was used for a separate and independent study, which ran in parallel on the same population of trauma patients. Personnel collecting and processing data for each strategy were blinded to patients in the other group and to the study objective during data processing. The institutional review boards at all participating hospitals reviewed and approved this project and waived the requirement for informed consent.

This study was performed with 10 emergency medical services EMS agencies four private ambulance transport agencies, six fire departments and 16 hospitals three trauma centers, 13 community or private hospitals in a four-county region of Northwest Oregon and Southwest Washington.

The region operates a dual-advanced life support EMS system, where the majority of responses are served by both fire first responder and ambulance transport agencies, typically generating two EMS charts for each patient. In this project, we compiled all available sources of EMS data for each patient in both the electronic and manual processing strategies, as illustrated in Data Supplements 1 electronic approach selectrpnic 2 manual approach. The study was conducted at one site participating in a multi-site out-of-hospital research network Resuscitation Outcomes Consortium [ROC] that has been described in detail elsewhere.

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Field physiologic cataoogue at any point during out-of-hospital evaluation was defined as: The dates for enrollment included a month time period with concurrent data processing efforts January 1, through Selectroni 2, Patients meeting the study inclusion criteria, but not included in the primary cohort e. There were two methods of case identification and data collection performed separately, but in parallel, on the same group of out-of-hospital trauma patients.

All EMS agencies included in this study had EHR systems in place, and used electronic processes for dispatch, charting, and billing. All source files were obtained from these EHR systems. Case ascertainment began by requesting all EMS records for patients entered into the trauma system i. Because EMS providers from multiple agencies care for the same patients in this system, cata,ogue available records from fire and ambulance agencies were manually matched to provide a comprehensive assessment of out-of-hospital information.


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. Outcomes were collected by matching EMS records to hospital records, locating these records within respective hospitals, and abstracting the hospital data into the web-based forms. The research staff involved in manual data processing included: Quality assurance processes included data element range and consistency checks in the web-based data entry forms, dual data entry, chart re-review for a randomly selected sample of records, and annual site visits by members of the ROC Data Coordinating Center to review randomly selected study records, data capture processes, and local data quality efforts.

Electronic data processing was undertaken for the same sample of patients in a separate, but parallel project investigating field trauma triage practices in the region. Of these patients, 3, met the physiologic inclusion criteria. Aggregate EHR files were exported from each of the participating EMS agencies typically in 6- or month time blocks, depending on the agency, availability of agency-based data personnel, and volume of calls over a 2-year period, and were restricted to the same dates used for the manual processing sample January 1, through October 2, Data files representing a variety cwtalogue different formats e.

We matched multiple EMS records for the same patients, as well as hospital outcomes from existing trauma registries selsctronic and state discharge databases 2using probabilistic linkage 8101819 LinkSolv, v. Record linkage is an analytic method used to match records from different datasets using common variables when a unique identifier is not available. 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.

We performed several sequential linkage analyses. For linking EMS records to patient discharge data, we used six variables date of service, date of birth, home zip code, age, sex, and hospital.

Probabilistic linkage was also selectronif to match electronically processed patient records to manually processed records using linkage variables unrelated to those being compared to avoid potentially inflating agreement between the samples. These linkage variables included: EMS incident cataloge, date of service, dispatch time, age, sex, hospital, and trauma band number. Study staff involved with electronic data processing included: We evaluated 18 clinical, caatalogue, procedural, and outcome variables obtained using each data processing strategy.

Operational variables included four time intervals response, on-scene, transport, and total out-of-hospital time. Outcomes included mortality field and in-hospital and duration of hospital stay. We compared values obtained from manual versus electronic data processing using nonparametric descriptive statistics median, interquartile range [IQR], and proportion. Case ascertainment was assessed by comparing the total number of patients meeting the pre-specified inclusion criteria for each data processing approach.

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

First, we used statistical measures of agreement kappa, weighted kappa, intraclass correlation coefficient [ICC] and Bland-Altman plots. We assessed heteroscedasticity differing variance across the range of potential values for all continuous variables by regressing the difference in values manual minus electronic against the averaged value for each observation.

All statistical analyses were based on observed values patients with missing values excluded and were conducted with SAS v. During the month period, injured patients with physiologic compromise were identified, enrolled, and processed using manual data processing. Case ascertainment using electronic methods yielded 3, injured patients meeting the same inclusion criteria during the same time period.

Four hundred eighteen patients matched between the two data processing groups and formed the primary cohort seletcronic comparison Figure 1. An additional patients in the manual processing group did not match to a record from the electronic group. Clinical, operational, procedural, and outcome variables are described for the various matched and unmatched groups in Table 1. ctalogue

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Patients in the first three columns represent the manual data processing group matched and unmatched to electronic caseswhile those in the last column were only identified by electronic processing the electronic-only group.

In general, cases identified by manual methodology tended to have greater physiologic compromise e. The median out-of-hospital time values fluctuated between groups, but overall were comparable. Clinical, operational, procedural, and outcome information for the different manual and electronic data processing samples.

Clinical, operational, procedural, and outcome variables including catalogeu proportion of missing values among matched patients who underwent both data processing strategies are compared in Table 2. Overall, there were very similar characteristics generated from both data processing approaches. In addition, four patients identified as dying during their hospital stays with manual chart review were listed as survivors with electronic data processing methods.


There was good agreement and validity between the two data processing approaches Table 3. For categorical variables, kappa values ranged from 0. The intraclass correlation coefficient ICC for continuous terms ranged from 0. The median difference was zero for all continuous variables, with all but two terms having an interquartile range IQR of zero for these differences.

There was some evidence of heteroscedasdicity among 5 of the 15 ordinal and continuous variables, as assessed by regressing differences against averaged values.

The coefficients for these variables initial respiratory rate 0. Figure 2 shows Bland-Altman plots sleectronic initial and lowest field sBP. For total out-of-hospital time, most values clustered on the zero difference line, but those that differed tended to selectrobic under-estimated by electronically processed time values. Two-by-two tables for field procedures intravenous line placement, intubation and outcomes mortality and are included in Figure 6. Bland-Altman plots of field systolic blood pressure between electronic and manual data processing.

Scatter at each value has been included to enhance visual interpretation. A single outlier value with difference of days was removed from the figure for clarity. Two-by-two tables comparing electronic and manual data processing values for field interventions intravenous line placement, intubation and outcome mortality.

In this study, we compared two data processing strategies manual versus electronic for obtaining clinical research data from existing EHR among a cohort cayalogue out-of-hospital trauma patients.

We found good to excellent agreement between the two approaches, with electronic catapogue having notably larger case capture.

This is the first study we are aware of that directly compares a maximized all-electronic approach to more traditional case identification and data abstraction routines for outcomes-based out-of-hospital research.

With increased emphasis on the implementation and utilization of EHR systems, 5 this study is important in affirming the data quality and gains in case ascertainment when using an electronic approach for clinical research. Our findings are notable for several reasons. First, we compared the data processing strategies using clinically meaningful variables and outcomes, rather than simply evaluating the number of errors per data field.

Second, the electronic methods used in this study completely removed the need for data abstraction and data entry paperlessthus maximizing the benefits of EHR sources. Third, electronic data processing was based on aggregate data exports and processing routines that can handle large volumes of records with relatively small additional increases in processing time.

Finally, data quality using electronic methods was comparable to manual processing methods and identified many more eligible patients, 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.

There were notable differences in case ascertainment and acuity between patients identified with the two approaches.

The smaller sample size generated through manual processing is primarily explained by a more restrictive approach for case identification. While in theory this approach should have identified all eligible patients, our findings suggest that not all injured patients with abnormal field physiology are entered into the trauma system or that a portion of such patients are omitted from the respective EMS and trauma logsand therefore relying on assumptions can miss eligible patients.

These results illustrate that comprehensive case ascertainment requires a broad patient query with few assumptions and that hand sorting through EMS records and case logs does not match the comprehensiveness of a broad electronic record query.

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While electronic data processing yielded more eligible patients, these additional patients had less severe physiologic compromise and lower mortality, suggesting that manual patient identification may be inherently biased towards higher acuity patients with worse prognosis, or that use of electronic patient queries identifies more heterogeneous and therefore lower acuity subjects.

The implications of these competing risks may differ depending on the xatalogue question being pursued, and therefore need to be considered for each research project entertaining both approaches to patient identification. While we did not directly quantify the differences in time efficiency between data processing approaches, we gained substantive insight by assessing the relative effort expended for each strategy.

Electronic processing time was affected by the inclusion 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.

The time savings would be expected catalofue increase when using a single EHR program, data exports with industry-standardized processes, familiar data routines, standardized data fields e.