# Diagnostic Safety Center for Advancing E-triggers and Rapid Feedback Implementation (DISCOVERI)

> **NIH AHRQ R18** · BAYLOR COLLEGE OF MEDICINE · 2024 · $999,956

## Abstract

Progress in reducing diagnostic errors is slow partly due to poorly defined methods to identify and learn from
them. Methods to provide diagnostic safety data to clinicians and leaders can enable them to act upon these
data to prevent diagnostic harm. Health care organizations (HCOs) now have an opportunity to explore their
increasing stores of electronic health record (EHR) data for learning, research, and quality improvement
related to diagnosis. Electronic trigger (e-trigger) tools, which mine vast amounts of clinical and administrative
data to identify signals for likely adverse events, offer a promising method to do so. Our work shows e-trigger
algorithms can efficiently identify patterns of care suggestive of missed or delayed diagnoses in primary care,
emergency care and inpatient settings. Robust review and analysis methods can then uncover safety concerns
and provide information on breakdowns related to the diagnostic process, including contributory factors. This
information can generate learning and feedback that could be used for improvement by individuals and teams.
However, despite their potential use for measurement and improvement of diagnostic safety, e-triggers are still
largely confined to research and not yet translated into practice. We thus propose developing the “Diagnostic
Safety Center for Advancing E-triggers and Rapid Feedback Implementation (DISCOVERI)” with a goal of
implementing surveillance and feedback systems for diagnostic safety in HCOs. Our long-term goal is to
accelerate uptake of e-triggers for measurement of diagnostic safety across the US in organizations that value
Learning and Exploration of Diagnostic Excellence (LEDE organizations). DISCOVERI will help create
generalizable knowledge, tools, strategies and methods for an e-trigger based learning and feedback system
for improving diagnostic safety within LEDE organizations. Specific aims are:
Aim 1: Create tools, strategies, and methods to implement e-trigger algorithms for diagnostic error surveillance
and prevention in LEDE organizations.
Aim 2: Develop and evaluate Safety-I and Safety-II related methods for providing clinicians and health care
organizations with rapid diagnostic performance feedback.
Aim 3: Synthesize implementation experiences to develop a safety surveillance system “Safer Dx e-Watch” to
facilitate large-scale implementation efforts in US health systems.
We will work with 3 health systems and use multiple qualitative and quantitative methods involving hybrid in-
person and virtual participatory approaches. We will define processes for how e-trigger data can be gathered
from EHRs and delivered as feedback for learning and improvement to organizational leaders and clinicians.
We will leverage our experience in translating research to improve practice and disseminating and
implementing interventions. Knowledge and methods generated by DISCOVERI will accelerate implementation
of an e-trigger based learning and feedback system to prevent ha...

## Key facts

- **NIH application ID:** 10918176
- **Project number:** 5R18HS029347-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** HARDEEP SINGH
- **Activity code:** R18 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2024
- **Award amount:** $999,956
- **Award type:** 5
- **Project period:** 2022-09-30 → 2026-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10918176

## Citation

> US National Institutes of Health, RePORTER application 10918176, Diagnostic Safety Center for Advancing E-triggers and Rapid Feedback Implementation (DISCOVERI) (5R18HS029347-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10918176. Licensed CC0.

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