A comparative effectiveness study of eSource used for data capture for a clinical research registry
| Autor: | Melissa Pressley, Iain C. Sanderson, Eric L. Eisenstein, Jeffrey Hawley, Jennifer Pennock, Amy Harris Nordo, Sai Vadakkeveedu |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
Automatic identification and data capture Pilot Projects Health Informatics computer.software_genre 01 natural sciences Article Workflow 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Data retrieval Data accuracy Humans Clinical registry Registries 030212 general & internal medicine 0101 mathematics Data collection Data Collection Records Data science Obstetrics Information extraction Gynecology Data quality Database Management Systems Female computer Software |
| Zdroj: | International Journal of Medical Informatics. 103:89-94 |
| ISSN: | 1386-5056 |
| DOI: | 10.1016/j.ijmedinf.2017.04.015 |
| Popis: | This pilot study compared eSource-enabled versus traditional manual data transcription (non-eSource methods) for the collection of clinical registry information. The primary study objective was to compare the time spent completing registry forms using eSource versus non-eSource methods The secondary objectives were to compare data quality associated with these two data capture methods and the flexibility of the workflows. This study directly addressed fundamental questions relating to eSource adoption: what time-savings can be realized, and to what extent does eSource improve data quality.The study used time and motion methods to compare eSource versus non-eSource data capture workflows for a single center OB/GYN registry. Direct observation by industrial engineers using specialized computer software captured keystrokes, mouse clicks and video recordings of the study team in their normal work environment completing real-time data collection.The overall average data capture time was reduced with eSource versus non-eSource methods (difference, 151s per case; eSource, 1603s; non-eSource, 1754s; p=0.051). The average data capture time for the demographic data was reduced (difference, 79s per case; eSource, 133s; non-eSource, 213s; p0.001). This represents a 37% time reduction (95% confidence interval 27% to 47%). eSourced data field transcription errors were also reduced (eSource, 0%; non-eSource, 9%).The use of eSource versus traditional data transcription was associated with a significant reduction in data entry time and data quality errors. Further studies in other settings are needed to validate these results. |
| Databáze: | OpenAIRE |
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