R
Raj M. Ratwani
Researcher at MedStar Health
Publications - 158
Citations - 2607
Raj M. Ratwani is an academic researcher from MedStar Health. The author has contributed to research in topics: Patient safety & Usability. The author has an hindex of 24, co-authored 133 publications receiving 1929 citations. Previous affiliations of Raj M. Ratwani include University of Washington & Georgetown University Medical Center.
Papers
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Electronic Health Record Usability Issues and Potential Contribution to Patient Harm
TL;DR: This study analyzed patient safety reports in and near Pennsylvania from 2013 through 2016 to identify those that contained explicit language associating possible patient harm with an electronic health record usability issue.
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Electronic health record usability: analysis of the user-centered design processes of eleven electronic health record vendors
TL;DR: A research team visited 11 different EHR vendors in order to analyze their UCD processes and discover the specific challenges that vendors faced as they sought to integrate UCD with their EHR development.
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Thinking graphically: Connecting vision and cognition during graph comprehension.
TL;DR: A new framework for information integration is proposed that highlights visual integration and cognitive integration and design principles to improve both visual and Cognitive integration are described.
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A usability and safety analysis of electronic health records: a multi-center study
Raj M. Ratwani,Raj M. Ratwani,Erica L. Savage,Amy Will,Ryan Arnold,Saif Khairat,Kristen Miller,Rollin J. Fairbanks,Rollin J. Fairbanks,Michael L. Hodgkins,A. Zachary Hettinger,A. Zachary Hettinger +11 more
TL;DR: There was wide variability in task completion time, clicks, and error rates of EHRs from two vendors across four healthcare systems, highlighting the need for improved implementation optimization.
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A memory for goals model of sequence errors
TL;DR: A model of routine sequence actions is developed based on the Memory for Goals framework that assumes that sequential action is guided by episodic control codes generated for each step, and that these codes decay with time and can be primed by contextual retrieval cues.