The effect of data-entry template design and anesthesia provider workload on documentation accuracy, documentation efficiency, and user-satisfaction

Currently, there are few evidence-based guidelines to inform optimal clinical data-entry template design that maximizes usability while reducing unintended consequences. This study explored the impact of data-entry template design and anesthesia provider workload on documentation accuracy, documenta...

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Veröffentlicht in:International journal of medical informatics (Shannon, Ireland) Ireland), 2018-10, Vol.118, p.29-35
Hauptverfasser: Wilbanks, Bryan A., Berner, Eta S., Alexander, Gregory L., Azuero, Andres, Patrician, Patricia A., Moss, Jacqueline A.
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Sprache:eng
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Zusammenfassung:Currently, there are few evidence-based guidelines to inform optimal clinical data-entry template design that maximizes usability while reducing unintended consequences. This study explored the impact of data-entry template design and anesthesia provider workload on documentation accuracy, documentation efficiency, and user-satisfaction to identify the most beneficial data-entry methods for use in future documentation interface design. A study using observational data collection and psychometric instruments (for perceived workload and user-satisfaction) was conducted at three hospitals using different methods of data-entry for perioperative documentation (auto-filling with unstructured data, computer-assisted data selection with semi-structured documentation, and paper-based documentation). Nurse anesthetists at each hospital (N = 30) were observed completing documentation on routine abdominal surgical cases. Auto-filling (61.2%) had the lowest documentation accuracy scores compared to computer-assisted (81.3%) and paper-based documentation (76.2%). Computer-assisted data-entry had the best documentation efficiency scores and required the least percentage of the nurse anesthetists’ time (9.65%) compared to auto-filling (11.43%) and paper-based documentation (15.23%). Paper-based documentation had the highest perceived workload scores (M = 288, SD = 88) compared to auto-filling (M = 160, SD = 93, U = 16.5, p 
ISSN:1386-5056
1872-8243
DOI:10.1016/j.ijmedinf.2018.07.006