Many outcome predictors used in precision medicine are based upon experience with populations of previous patients who are similar in important ways. However, standard Electronic Health Record systems (EHRs) seldom support real-time population queries and therefore cannot administer precision medicine as part of their integrated decision support for clinicians. Furthermore, the data upon which many precision medicine algorithms operate is not available in standard EMRs because it comes from advanced genomics, imaging analytics, and other data modalities which fall outside of the data domains of standard EHR platforms. We propose developing Informatics for Integrating Biology and the Bedside (i2b2), a well- established, open source, integrated, big data analytic platform that is currently used at over 140 hospitals and medical centers, to study phenotype/genotype comparisons and incorporate it into the EHR using small, connected applications named Substitutable Medical Applications and Reusable Technologies (SMART). We will take the Partners HealthCare genomics platform, GeneInsight, and integrate its data into our Epic EHR workflow using i2b2. We will then test specific decision support algorithms for inherited cardiac diseases. The resulting software will be open source and allow integration of genomics-based, big data decision support algorithms broadly into EHRs. We will also use these same data to provide decision support to laboratory professionals who classify variants relative to their clinical effects. Standard methods of representing these data will be used to make the algorithms transportable and universally applicable. The Decision Support Apps that are created will be evaluated not only for their potential impact upon clinical care, but also for their durability and adaptability to different healthcare environments.

Public Health Relevance

We propose developing Informatics for Integrating Biology and the Bedside (i2b2), a well established, open source, integrated, big data analytic platform that is currently used at over 140 hospitals and medical centers to study phenotype/genotype comparisons and incorporate it into the EHR workflow to provide real-time decision support to clinicians. We will take the Partners HealthCare genomics platform, GeneInsight, and integrate its data into our Epic EHR platform using i2b2. We will then test specific decision support algorithms for inherited cardiac diseases.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
1R01HG009174-01
Application #
9157994
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Sofia, Heidi J
Project Start
2016-09-28
Project End
2020-08-31
Budget Start
2016-09-28
Budget End
2017-08-31
Support Year
1
Fiscal Year
2016
Total Cost
$696,939
Indirect Cost
$188,515
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02114
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