# Characterizing COVID-19 Patients through a Community Health Information Exchange and EHR databases

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2020 · $74,999

## Abstract

Project Summary/Abstract
The COVID-19 pandemic is a significant public health problem that will require novel approaches for
management and intervention. Knowledge of the disease’s transmission, symptomatology, clinical
course, treatment and outcomes is rapidly evolving based on many sources. An important source for
advancing this knowledge are data from electronic health records (EHR) and health information
exchanges (HIE) because they can provide a real-time, unvarnished view of the disease. However,
the initially “invisible” nature of the disease makes clear that clinicians and public health personnel
were at a significant disadvantage in discovering and quantifying the pandemic. There is an urgent
need to learn rapidly from EHR and other data to improve discovery and monitoring of patients
infected by the coronavirus. The evolving dynamic and understanding of the incidence and course of
COVID-19 requires that we develop new methods for discovery from data. The long-term goal of our
research is to develop collaborative filtering algorithms to facilitate access to and analysis of clinical
data. The goal of this application is to characterize COVID-19 patients through data in a community
HIE, specifically the Indiana Network for Patient Care (INPC) within Indiana’s HIE (IHIE), and
understand how that characterization differs from that within the EHRs of individual health systems.
Understanding how COVID-19 patients are represented in HIEs and EHRs will build an important
foundation for downstream computational activities, such as real-time discovery, public health
surveillance, intervention management and contact tracing. The two specific aims of this project are
to (1) extract a cohort of patients suffering from COVID-19 and similar diseases from IHIE and (2)
characterize patients according to several dimensions, such as demographics, signs and symptoms,
and disease course using both the INPC as well as separate EHR data sets. The data, going back to
1/1/2015, will be extracted from the INPC, and the clinical data warehouses at IU Health and
Eskenazi Health, two of our major health system partners. As of this writing, 230,749 individuals in
Indiana (3.4 percent of the population of 6.73m) have been tested for the coronavirus, of whom
32,078 (13.9 percent) have tested positive. We will apply computational phenotyping approaches
using both HIE and individual EHR data in order to help us evaluate to what degree data from
individual EHRs can help approximate characterizations based on HIE data. This proposal is
significant because it will help us understand how HIE and EHR data can be used to characterize
both COVID-19 and non-COVID-19 patients. It is innovative because it leverages multiple
computational phenotyping methods on both individual organizations’ EHR, as well as HIE, data to
generate a comprehensive characterization of COVID-19 and non-COVID-19 patients.

## Key facts

- **NIH application ID:** 10177252
- **Project number:** 3R01LM012605-03S1
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** XIA NING
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $74,999
- **Award type:** 3
- **Project period:** 2020-07-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10177252

## Citation

> US National Institutes of Health, RePORTER application 10177252, Characterizing COVID-19 Patients through a Community Health Information Exchange and EHR databases (3R01LM012605-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10177252. Licensed CC0.

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