With the rapidly growing adoption of patient electronic health record (EHR) systems due to Meaningful Use, and linkage of EHRs to research biorepositories, evaluating the suitability of EHR data for clinical and translational research is becoming ever more important, with ramifications for genomic and observational research, clinical trials, and comparative effectiveness studies. A key component for identifying patient cohorts in the EHR is to define inclusion and exclusion criteria that algorithmically select sets o patients based on stored clinical data. This process is commonly referred to, as "EHR-driven phenotyping" is time-consuming and tedious due to the lack of a widely accepted and standards-based formal information model for defining phenotyping algorithms. To address this overall challenge, the proposed project will design, build and promote an open-access community infrastructure for standards-based development and sharing of phenotyping algorithms, as well as provide tools and resources for investigators, researchers and their informatics support staff to implement and execute the algorithms on native EHR data.

Public Health Relevance

The identification of patient cohorts for clinical and genomic research is a costly and time-consuming process. This bottleneck adversely affects public health by delaying research findings, and in some cases by making research costs prohibitively high. To address this issue, leveraging electronic health records (EHRs) for identifying patient cohorts has become an increasingly attractive option. This proposal will investigate and implement standards based approaches for phenotype identification from multiple EHRs.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM105688-01
Application #
8476832
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Lyster, Peter
Project Start
2013-06-03
Project End
2017-03-31
Budget Start
2013-06-03
Budget End
2014-03-31
Support Year
1
Fiscal Year
2013
Total Cost
$611,683
Indirect Cost
$118,059
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
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Panahiazar, Maryam; Taslimitehrani, Vahid; Pereira, Naveen et al. (2015) Using EHRs and Machine Learning for Heart Failure Survival Analysis. Stud Health Technol Inform 216:40-4
Rasmussen, Luke V; Kiefer, Richard C; Mo, Huan et al. (2015) A Modular Architecture for Electronic Health Record-Driven Phenotyping. AMIA Jt Summits Transl Sci Proc 2015:147-51
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