Pharmacists versus nonpharmacists in adverse drug event detection: A meta-analysis and systematic review

2007 ◽  
Vol 64 (8) ◽  
pp. 842-849 ◽  
Author(s):  
Shobha Phansalkar ◽  
Jennifer M. Hoffman ◽  
Jonathan R. Nebeker ◽  
John F. Hurdle
Keyword(s):  
Systematic Review ◽  
Adverse Drug Event ◽  
Event Detection ◽  
Meta Analysis ◽  
Drug Event

scholarly journals Correction to: Interventions to Reduce Adverse Drug Event-Related Outcomes in Older Adults: A Systematic Review and Meta-analysis

Drugs & Aging ◽  
2020 ◽  
Vol 37 (2) ◽  
pp. 147-147
Author(s):  
Sarah Tecklenborg ◽  
Catherine Byrne ◽  
Caitriona Cahir ◽  
Lamorna Brown ◽  
Kathleen Bennett
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Adverse Drug Event ◽  
Meta Analysis ◽  
Drug Event

Interventions to Reduce Adverse Drug Event-Related Outcomes in Older Adults: A Systematic Review and Meta-analysis

Drugs & Aging ◽  
2020 ◽  
Vol 37 (2) ◽  
pp. 91-98 ◽  
Author(s):  
Sarah Tecklenborg ◽  
Catherine Byrne ◽  
Caitriona Cahir ◽  
Lamorna Brown ◽  
Kathleen Bennett
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Adverse Drug Event ◽  
Meta Analysis ◽  
Drug Event

scholarly journals A systematic review to evaluate the accuracy of electronic adverse drug event detection

2012 ◽  
Vol 19 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Alan J Forster ◽  
Alison Jennings ◽  
Claire Chow ◽  
Ciera Leeder ◽  
Carl van Walraven
Keyword(s):  
Systematic Review ◽  
Adverse Drug Event ◽  
Event Detection ◽  
Drug Event

Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding

Drug Safety ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 113-122 ◽  
Author(s):  
Susmitha Wunnava ◽  
Xiao Qin ◽  
Tabassum Kakar ◽  
Cansu Sen ◽  
Elke A. Rundensteiner ◽  
...  
Keyword(s):  
Neural Networks ◽  
Electronic Health Records ◽  
Adverse Drug Event ◽  
Event Detection ◽  
Recurrent Neural Networks ◽  
Drug Event ◽  
Health Records ◽  
Electronic Health

scholarly journals Adverse drug event reporting systems: a systematic review

2016 ◽  
Vol 82 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Chantelle Bailey ◽  
David Peddie ◽  
Maeve E. Wickham ◽  
Katherin Badke ◽  
Serena S. Small ◽  
...  
Keyword(s):  
Systematic Review ◽  
Adverse Drug Event ◽  
Drug Event ◽  
Event Reporting ◽  
Reporting Systems

Evaluation of the Case–Crossover (CCO) Study Design for Adverse Drug Event Detection

Drug Safety ◽  
2017 ◽  
Vol 40 (9) ◽  
pp. 789-798 ◽  
Author(s):  
Zachary Burningham ◽  
Tao He ◽  
Chia-Chen Teng ◽  
Xi Zhou ◽  
Jonathan Nebeker ◽  
...  
Keyword(s):  
Study Design ◽  
Adverse Drug Event ◽  
Event Detection ◽  
Drug Event ◽  
Case Crossover

scholarly journals Adverse Drug Event Reporting From Clinical Care: Mixed-Methods Analysis for a Minimum Required Dataset (Preprint)

2018 ◽  
Author(s):  
David Peddie ◽  
Serena S Small ◽  
Katherin Badke ◽  
Chantelle Bailey ◽  
Ellen Balka ◽  
...  
Keyword(s):  
Systematic Review ◽  
Patient Safety ◽  
Mixed Methods ◽  
Adverse Drug Event ◽  
Improve Patient Safety ◽  
Clinical Care ◽  
Drug Event ◽  
Care Providers ◽  
Event Reporting ◽  
Improve Patient

BACKGROUND Patients commonly transition between health care settings, requiring care providers to transfer medication utilization information. Yet, information sharing about adverse drug events (ADEs) remains nonstandardized. OBJECTIVE The objective of our study was to describe a minimum required dataset for clinicians to document and communicate ADEs to support clinical decision making and improve patient safety. METHODS We used mixed-methods analysis to design a minimum required dataset for ADE documentation and communication. First, we completed a systematic review of the existing ADE reporting systems. After synthesizing reporting concepts and data fields, we conducted fieldwork to inform the design of a preliminary reporting form. We presented this information to clinician end-user groups to establish a recommended dataset. Finally, we pilot-tested and refined the dataset in a paper-based format. RESULTS We evaluated a total of 1782 unique data fields identified in our systematic review that describe the reporter, patient, ADE, and suspect and concomitant drugs. Of these, clinicians requested that 26 data fields be integrated into the dataset. Avoiding the need to report information already available electronically, reliance on prospective rather than retrospective causality assessments, and omitting fields deemed irrelevant to clinical care were key considerations. CONCLUSIONS By attending to the information needs of clinicians, we developed a standardized dataset for adverse drug event reporting. This dataset can be used to support communication between care providers and integrated into electronic systems to improve patient safety. If anonymized, these standardized data may be used for enhanced pharmacovigilance and research activities.

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