Journal of the American Medical Informatics Association : JAMIA
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The objective was to assess use of a physician handoff tool embedded in the electronic medical record by nurses and other non-physicians. We administered a survey to nurses, physical therapists, discharge planners, social workers, and others to assess integration into daily practice, usefulness, and accuracy of the handoff tool. 231 individuals (61% response) participated. 60% used the tool often or usually/always during a shift. Nurses (46%) used the tool for shift transitions and found it helpful for medical history (79%) but not for acquiring medication, allergy, and responsible physician information. ⋯ Medical nurses rated the tool more useful than surgical nurses, and pediatric nurses rarely used the tool. The tool was integrated into the daily workflow of non-physicians despite being designed for physician use. Non-physicians should be included in the design and implementation of electronic patient handoff systems.
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J Am Med Inform Assoc · Sep 2014
Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.
Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. ⋯ Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect.
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J Am Med Inform Assoc · Sep 2014
Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers.
To evaluate factors affecting performance of influenza detection, including accuracy of natural language processing (NLP), discriminative ability of Bayesian network (BN) classifiers, and feature selection. ⋯ Using a three-component evaluation method we demonstrated how one could elucidate the relative contributions of components under an integrated framework. To improve classification performance, this study encourages researchers to improve NLP accuracy, use a machine-parameterized classifier, and apply feature selection methods.
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J Am Med Inform Assoc · Jul 2014
ReviewPatient engagement in the inpatient setting: a systematic review.
To systematically review existing literature regarding patient engagement technologies used in the inpatient setting. ⋯ Examination of the current literature shows there are considerable gaps in knowledge regarding patient engagement in the hospital setting and inconsistent use of terminology regarding patient engagement overall. Research on inpatient engagement technologies has been limited, especially concerning the impact on health outcomes and cost-effectiveness.
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J Am Med Inform Assoc · Jul 2014
ReviewHow outcomes are achieved through patient portals: a realist review.
To examine how patient portals contribute to health service delivery and patient outcomes. The specific aims were to examine how outcomes are produced, and how variations in outcomes can be explained. ⋯ Patient portals may impact clinical outcomes and health service delivery through multiple mechanisms. Given the relative uniformity of evaluation contexts, we were not able to detect patterns in how patient portals work in different contexts. Nonetheless, it appears from the overwhelming proportion of patient portal evaluations coming from integrated health service networks, that these networks provide more fertile contexts for patient portals to be effective. To improve the understanding of how patient portals work, future evaluations of patient portals should capture information about mechanisms and context that influence their outcomes.