Choudhury A, Asan O. JMIR Med Inform. 2020;8:e18599.
This systematic review explored how artificial intelligence (AI) based on machine learning algorithms and natural language processing is used to address and report patient safety outcomes. The review suggests that AI-enabled decision support systems can improve error detection, patient stratification, and drug management, but that additional evidence is needed to understand how well AI can predict safety outcomes.
Fraczkowski D, Matson J, Lopez KD. J Am Med Inform Assoc. 2020;27:1149-1165.
The authors reviewed studies using qualitative and quantitative methods to describe nursing workarounds related to the electronic health record (EHR) in direct care activities. Workarounds generally fit into three categories – omission of process steps, steps performed out of sequence, and unauthorized process steps. Probable causes for workarounds were identified, including organizational- (e.g., knowledge deficits, non-formulary orders), environmental-, patient- (e.g., barcode/ID not accessible), task- (e.g., insufficient time), and usability-related factors (e.g., multiple screens to complete an action). Despite nurses being the largest workforce using EHRs, there is limited research focused on the needs of nurses in EHR design.
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