scholarly journals Data Rich, Information Poor: Can We Use Electronic Health Records to Create a Learning Healthcare System for Pharmaceuticals?

2018 ◽  
Vol 105 (4) ◽  
pp. 912-922 ◽  
Author(s):  
Hans‐Georg Eichler ◽  
Brigitte Bloechl‐Daum ◽  
Karl Broich ◽  
Paul Alexander Kyrle ◽  
Jillian Oderkirk ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Healthcare System ◽  
Health Records ◽  
Learning Healthcare System ◽  
Rich Information ◽  
Electronic Health

scholarly journals Drug allergies documented in electronic health records of a large healthcare system

Allergy ◽  
2016 ◽  
Vol 71 (9) ◽  
pp. 1305-1313 ◽  
Author(s):  
L. Zhou ◽  
N. Dhopeshwarkar ◽  
K. G. Blumenthal ◽  
F. Goss ◽  
M. Topaz ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Healthcare System ◽  
Health Records ◽  
Electronic Health

scholarly journals Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record

2019 ◽  
Author(s):  
Jacob J. Hughey ◽  
Seth D. Rhoades ◽  
Darwin Y. Fu ◽  
Lisa Bastarache ◽  
Joshua C. Denny ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Electronic Health Records ◽  
Healthcare System ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Right Censoring ◽  
Type I ◽  
Health Records ◽  
Wide Range ◽  
Electronic Health

AbstractBackgroundThe growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for the times at which events occur. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring).ResultsUsing simulated data, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the electronic health records of 49 792 genotyped individuals. In terms of effect sizes, the hazard ratios estimated by Cox regression were nearly identical to the odds ratios estimated by logistic regression. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog.ConclusionsAs longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.

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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