A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data

2019 ◽  
Vol 170 ◽  
pp. 31-38
Author(s):  
Chu-Ya Huang ◽  
Phung-Anh Nguyen ◽  
Hsuan-Chia Yang ◽  
Md Mohaimenul Islam ◽  
Chia-Wei Liang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Electronic Health Records ◽  
Medication Errors ◽  
Probabilistic Model ◽  
Health Records ◽  
Electronic Health

Electronic Health Records, Electronic Prescribing and Medication Errors: A Systematic Review of Literature, 2000-2014

2015 ◽  
Vol 5 (5) ◽  
pp. 672-704 ◽  
Author(s):  
Naseem Qureshi ◽  
Dalal Al-Dossari ◽  
Ibrahim Al-Zaagi ◽  
Abdullah Al-Bedah ◽  
Abdulrahman Abudalli ◽  
...  
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Medication Errors ◽  
Electronic Prescribing ◽  
Review Of Literature ◽  
Health Records ◽  
Electronic Health

Medication Errors Despite Using Electronic Health Records

2016 ◽  
Vol 25 (1) ◽  
pp. 32-37 ◽  
Author(s):  
Sumana Alex ◽  
Ayne B. Adenew ◽  
Cherinne Arundel ◽  
David D. Maron ◽  
Jennifer C. Kerns
Keyword(s):  
Electronic Health Records ◽  
Medication Errors ◽  
Health Records ◽  
Electronic Health

scholarly journals A Modeling Framework for Exploring Sampling and Observation Process Biases in Genome and Phenome-wide Association Studies using Electronic Health Records

2018 ◽  
Author(s):  
Lauren J. Beesley ◽  
Lars G. Fritsche ◽  
Bhramar Mukherjee
Keyword(s):  
Sensitivity Analysis ◽  
Electronic Health Records ◽  
Large Scale ◽  
Association Studies ◽  
Modeling Framework ◽  
Health Records ◽  
Association Analyses ◽  
Observation Process ◽  
Special Cases ◽  
Electronic Health

AbstractLarge-scale agnostic association analyses based on existing observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, particular challenges of non-probability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. In general, the extent of the bias that may be introduced by ignoring these factors is unknown. In this paper, we develop a statistical framework for characterizing the degree of bias expected in association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis type approach, this framework can be used to obtain plausible values for parameters of interest given results obtained from standard naive analysis methods under varying degree of misclassification and sampling biases. We develop an online tool for performing this sensitivity analysis in some special cases that occur frequently. Simulations demonstrate promising properties of the proposed way of characterizing biases. We apply our approach to study bias in genetic association studies using data from the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine.

Electronic health records: Context matters!

2016 ◽  
Vol 34 (2) ◽  
pp. 163-165 ◽  
Author(s):  
William B. Ventres ◽  
Richard M. Frankel
Keyword(s):  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health
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