# National Infrastructure for Standardized and Portable EHR Phenotyping Algorithms

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2020 · $706,851

## Abstract

PROJECT SUMMARY
With the rapidly growing adoption of patient electronic health record systems (EHRs) 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 of 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.

## Key facts

- **NIH application ID:** 10021669
- **Project number:** 5R01GM105688-09
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Yuan Luo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $706,851
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10021669

## Citation

> US National Institutes of Health, RePORTER application 10021669, National Infrastructure for Standardized and Portable EHR Phenotyping Algorithms (5R01GM105688-09). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10021669. Licensed CC0.

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