# Real-time genetic diagnosis at the point of care

> **NIH NIH R01** · GEISINGER CLINIC · 2024 · $902,661

## Abstract

Summary
The burden on patients and caregivers when complex diseases arise creates a taxing toll for both families and
healthcare systems. Multiple inpatient hospitalizations and various testing procedures often bring more
unknowns and grief to an already difficult situation. Hospital visits disrupt patient schedules, and also place
unnecessary burdens on a healthcare system whose purpose is to maximize the health outcomes of the
patients. These complex diseases utilize extra visits and unnecessary testing. We want to develop a system
that would identify patients who could benefit from accessing their existing genetic information. Physicians may
struggle to understand the correct time to order genetic testing, and with the rapid pace of change within the
genetics field, many physicians are not utilizing the genetic testing that is available at an appropriate time.
Genetic testing also requires hospital resources from a limited pool of workers, thus every patient that presents
as a complex case may not be a suitable candidate for genetic testing. Identifying which patients should be
accessing their genetic information requires an innovative approach. At Geisinger, we have a cohort of
150,000 patients who have been sequenced and we currently have their genetic data. We propose starting
with the patients clinical presentations that are currently charted into an electronic health record to identify
phenotype terms that would trigger genetic resources to be available. These genetic resources would include
workflows that show optimal points of impact for the patients to improve healthcare outcomes. To realize this
vision, we have identified three areas that we would like to address. Identification of patients with a candidate
condition in real time, followed by a concurrent bioinformatic analysis of the genomic sequence data. Finally,
we want to address returning the genetic test result to the provider and patient so that both parties have the
appropriate information to guide condition-specific care. In order to address these three needs, we have
developed three specific aims with the experts at Geisinger in mind for implementation. Aim 1. Development of
a High Impact Phenotype Identification System (HIPIS). Aim 2. Develop Dynamic Virtual Genetic Panels
(DVGP) for real-time genetic diagnosis. Aim 3. Analysis of clinical workflows for optimal point of care
integration of real time genetic diagnosis. Collaboration with Geisinger experts as well as experts in human
phenotyping (Peter Robinson) will increase understanding about integrating genetic information into patient
care. This transformation will allow the work of many experts in various fields to be sitting at the fingertips of
primary care physicians while researching the best direction for complex diseases.

## Key facts

- **NIH application ID:** 10868580
- **Project number:** 5R01HG011799-04
- **Recipient organization:** GEISINGER CLINIC
- **Principal Investigator:** Marc S. Williams
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $902,661
- **Award type:** 5
- **Project period:** 2021-08-10 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10868580

## Citation

> US National Institutes of Health, RePORTER application 10868580, Real-time genetic diagnosis at the point of care (5R01HG011799-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10868580. Licensed CC0.

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