scholarly journals Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system

2012 ◽  
Vol 21 ◽  
pp. 41-49 ◽  
Author(s):  
Jeremy A. Rassen ◽  
Sebastian Schneeweiss
Keyword(s):  
Surveillance System ◽  
Propensity Scores ◽  
Medical Product ◽  
Product Safety ◽  
High Dimensional ◽  
Safety Surveillance

scholarly journals Decision support environment for medical product safety surveillance

2016 ◽  
Vol 64 ◽  
pp. 354-362 ◽  
Author(s):  
Taxiarchis Botsis ◽  
Christopher Jankosky ◽  
Deepa Arya ◽  
Kory Kreimeyer ◽  
Matthew Foster ◽  
...  
Keyword(s):  
Decision Support ◽  
Medical Product ◽  
Product Safety ◽  
Safety Surveillance

scholarly journals Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Rishi J. Desai ◽  
Michael E. Matheny ◽  
Kevin Johnson ◽  
Keith Marsolo ◽  
Lesley H. Curtis ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Science ◽  
Claims Data ◽  
The United States ◽  
Medical Product ◽  
Product Safety ◽  
Electronic Health Record Data ◽  
Data Infrastructure ◽  
Safety Surveillance ◽  
Electronic Health

AbstractThe Sentinel System is a major component of the United States Food and Drug Administration’s (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center’s initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.

Methods for observational post-licensure medical product safety surveillance

2011 ◽  
Vol 24 (2) ◽  
pp. 177-193 ◽  
Author(s):  
Jennifer C Nelson ◽  
Andrea J Cook ◽  
Onchee Yu ◽  
Shanshan Zhao ◽  
Lisa A Jackson ◽  
...  
Keyword(s):  
Medical Product ◽  
Product Safety ◽  
Safety Surveillance

Medical Product Safety Surveillance

Epidemiology ◽  
2013 ◽  
Vol 24 (5) ◽  
pp. 692-699 ◽  
Author(s):  
Judith C. Maro ◽  
Jeffrey S. Brown ◽  
Martin Kulldorff
Keyword(s):  
Medical Product ◽  
Product Safety ◽  
Safety Surveillance

Unsafe Playgrounds

PEDIATRICS ◽  
1979 ◽  
Vol 64 (6) ◽  
pp. 962-963 ◽  
Author(s):  
Thomas E. Reichelderfer ◽  
Avrin Overbach ◽  
Joseph Greensher
Keyword(s):  
Emergency Room ◽  
Surveillance System ◽  
Hazard Index ◽  
Severity Index ◽  
Product Safety ◽  
Injury Surveillance ◽  
Consumer Product ◽  
Hospital Emergency ◽  
Injury Surveillance System ◽  
Hospital Emergency Room

Pediatricians generally may not be aware that playgrounds and playground equipment present an unsuspected hazard to children. Swings, slides, and playground equipment are ranked fifth in the Consumer Product Hazard Index based on data from the National Electronic Injury Surveillance System (NEISS) of the Consumer Product Safety Commision (CPSC), with an Age Adjusted Frequency-Severity Index of 12,498,000 for 1976 to 1977.1 Last year the CPSC's NEISS estimated that 167,000 persons were administered hospital emergency room treatment on a nationwide basis for injuries associated with public (75,000), home (41,000), and unspecified (51,000) playground equipment. The majority of those injured were between 5 and 10 years of age.

The reporting completeness of a passive safety surveillance system for pandemic (H1N1) 2009 vaccines: A capture–recapture analysis

Vaccine ◽  
2012 ◽  
Vol 30 (12) ◽  
pp. 2168-2172 ◽  
Author(s):  
Wan-Ting Huang ◽  
Wei-I Huang ◽  
Yu-Wen Huang ◽  
Chien-Wen Hsu ◽  
Jen-Hsiang Chuang
Keyword(s):  
Surveillance System ◽  
Passive Safety ◽  
Pandemic H1n1 ◽  
Safety Surveillance ◽  
Capture Recapture ◽  
Pandemic H1n1 2009
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