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Cognitive workload changes for nurses transitioning from a legacy system with paper documentation to a commercial electronic health record

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Abstract

Objective: Healthcare institutions worldwide are moving to electronic health records (EHRs). These transitions are particularly numerous in the US where healthcare systems are purchasing and implementing commercial EHRs to fulfill federal requirements. Despite the central role of EHRs to workflow, the cognitive impact of these transitions on the workforce has not been widely studied. This study assesses the changes in cognitive workload among pediatric nurses during data entry and retrieval tasks during transition from a hybrid electronic and paper information system to a commercial EHR. Materials and methods: Baseline demographics and computer attitude and skills scores were obtained from 74 pediatric nurses in two wards. They also completed an established and validated instrument, the NASA-TLX, that is designed to measure cognitive workload; this instrument was used to evaluate cognitive workload of data entry and retrieval. The NASA-TLX was administered at baseline (. pre-implementation), 1, 5 and 10 shifts and 4 months post-implementation of the new EHR. Results: Most nurse participants experienced significant increases of cognitive workload at 1 and 5 shifts after "go-live". These increases abated at differing rates predicted by participants' computer attitudes scores (p=0.01). Conclusions: There is substantially increased cognitive workload for nurses during the early phases (1-5 shifts) of EHR transitions. Health systems should anticipate variability across workers adapting to "meaningful use" EHRs. "One-size-fits-all" training strategies may not be suitable and longer periods of technical support may be necessary for some workers.

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APA

Colligan, L., Potts, H. W. W., Finn, C. T., & Sinkin, R. A. (2015). Cognitive workload changes for nurses transitioning from a legacy system with paper documentation to a commercial electronic health record. International Journal of Medical Informatics, 84(7), 469–476. https://doi.org/10.1016/j.ijmedinf.2015.03.003

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