# Transforming Real-world evidence with Unstructured and Structured data to advance Tailored therapy (TRUST)

> **NIH FDA U01** · VERANTOS, INC. · 2020 · $1,895,400

## Abstract

Project Summary
There is a national desire to implement real-world evidence (RWE) within regulatory and clinical
pathways as a step toward personalized medicine, improved care, and more efficient care. This will
accelerate use of routinely collected data to refine care pathways. By influencing what is approved,
reimbursed, and selected for care, RWE will adjust the standard of care.
But, adjusting the standard of care can have unintended and dangerous consequences. Bad data
allowed into a patient’s electronic health record (EHR) has the potential to hurt one patient. Bad data
allowed into regulatory or reimbursement pathways can harm a nation.
RWE is often used to support trial recruitment, trial design, and marketing insight. As it is increasingly
used to make clinical assertions, there is reason to believe that current approaches may benefit from
greater rigor. Claims data often have accuracy below 50%. EHR problem lists often have accuracy below
60%. It is believed that low sensitivity incorporates skew since sicker patients with more touch points in
the health system have more complete documentation.
This program seeks to study data quality in the context of a potential drug launch. Leaders in the space
intend to study data quality while testing a novel and highly rigorous approach to RWE. To achieve the
goal of understanding how data quality influences RWE assertions, the proposed project includes
innovations in phenotyping, gold standard, accuracy measurement, and enhanced privacy and security.
This effort comes at a critical time, as regulators, payers, and providers are increasingly incorporating
RWE insights into their decision-making processes. By studying data quality and demonstrating safe
approaches to RWE, the country can move forward on solid footing.

## Key facts

- **NIH application ID:** 10180783
- **Project number:** 1U01FD007172-01
- **Recipient organization:** VERANTOS, INC.
- **Principal Investigator:** Daniel Jay Riskin
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2020
- **Award amount:** $1,895,400
- **Award type:** 1
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10180783

## Citation

> US National Institutes of Health, RePORTER application 10180783, Transforming Real-world evidence with Unstructured and Structured data to advance Tailored therapy (TRUST) (1U01FD007172-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10180783. Licensed CC0.

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