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Master of Public Health

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.

AUTHORS

Teixeira PL , Wei WQ , Cronin RM , Mo H , VanHouten JP , Carroll RJ , LaRose E , Bastarache LA , Rosenbloom ST , Edwards TL , Roden DM , Lasko TA , Dart RA , Nikolai AM , Peissig PL , Denny JC , . Journal of the American Medical Informatics Association : JAMIA. 2016 8 7; ().

ABSTRACT

Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time- and labor-intensive. We developed and evaluated 4 types of phenotyping algorithms and categories of EHR information to identify hypertensive individuals and controls and provide a portable module for implementation at other sites.

Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time- and labor-intensive. We developed and evaluated 4 types of phenotyping algorithms and categories of EHR information to identify hypertensive individuals and controls and provide a portable module for implementation at other sites.

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