Medical Registry Data Collection Efficiency: A Crossover Study Comparing Web-Based Electronic Data Capture and a Standard Spreadsheet
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| Publicado en: | Journal of Medical Internet Research vol. 18, no. 6 (Jun 2016), p. n/a |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Gunther Eysenbach MD MPH, Associate Professor
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 2512815925 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1438-8871 | ||
| 024 | 7 | |a 10.2196/jmir.5576 |2 doi | |
| 035 | |a 2512815925 | ||
| 045 | 2 | |b d20160601 |b d20160630 | |
| 100 | 1 | |a Staziaki, Pedro Vinícius | |
| 245 | 1 | |a Medical Registry Data Collection Efficiency: A Crossover Study Comparing Web-Based Electronic Data Capture and a Standard Spreadsheet | |
| 260 | |b Gunther Eysenbach MD MPH, Associate Professor |c Jun 2016 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background: Electronic medical records and electronic data capture (EDC) have changed data collection in clinical and translational research. However, spreadsheet programs, such as Microsoft Excel, are still used as data repository to record and organize patient data for research. Objective: The objective of this study is to assess the efficiency of EDC as against a standard spreadsheet in regards to time to collect data and data accuracy, measured in number of errors after adjudication. Methods: This was a crossover study comparing the time to collect data in minutes between EDC and a spreadsheet. The EDC tool used was Research Electronic Data Capture (REDCap), whereas the spreadsheet was Microsoft Excel. The data collected was part of a registry of patients who underwent coronary computed tomography angiography in the emergency setting. Two data collectors with the same experience went over the same patients and collected relevant data on a case report form identical to the one used in our Emergency Department (ED) registry. Data collection tool was switched after the patient that represented half the cohort. For this, the patient cohort was exactly 30 days of our ED coronary Computed Tomography Angiography registry and the point of crossover was determined beforehand to be 15 days. We measured the number of patients admitted, and time to collect data. Accuracy was defined as absence of blank fields and errors, and was assessed by comparing data between data collectors and counting every time the data differed. Statistical analysis was made using paired t -test. Results: The study included 61 patients (122 observations) and 55 variables. The crossover occurred after the 30th patient. Mean time to collect data using EDC in minutes was 6.2±2.3, whereas using Excel was 8.0±2.0 (P <.001), a difference of 1.8 minutes between both means (22%). The cohort was evenly distributed with 3 admissions in the first half of the crossover and 4 in the second half. We saw 2 (<0.1%) continuous variable typos in the spreadsheet that a single data collector made. There were no blank fields. The data collection tools showed no differences in accuracy of data on comparison. Conclusions: Data collection for our registry with an EDC tool was faster than using a spreadsheet, which in turn allowed more efficient follow-up of cases. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Research | ||
| 653 | |a Medical records | ||
| 653 | |a Accuracy | ||
| 653 | |a Spreadsheets | ||
| 653 | |a Tomography | ||
| 653 | |a Clinical research | ||
| 653 | |a Counting | ||
| 653 | |a Emergency services | ||
| 653 | |a Health Insurance Portability & Accountability Act 1996-US | ||
| 653 | |a Data entry | ||
| 653 | |a Data collection | ||
| 653 | |a Internet | ||
| 653 | |a Efficiency | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Patients | ||
| 653 | |a Electronic health records | ||
| 653 | |a Angiography | ||
| 653 | |a Variables | ||
| 653 | |a Computerized medical records | ||
| 653 | |a Errors | ||
| 653 | |a Time | ||
| 653 | |a Quantitative analysis | ||
| 653 | |a Information technology | ||
| 700 | 1 | |a Kim, Phillip | |
| 700 | 1 | |a Vadvala, Harshna V | |
| 700 | 1 | |a Ghoshhajra, Brian B | |
| 773 | 0 | |t Journal of Medical Internet Research |g vol. 18, no. 6 (Jun 2016), p. n/a | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2512815925/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2512815925/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2512815925/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |