# Application of a Machine Learning to Enhance e-Triggers to Detect and Learn from Diagnostic Safety Events

> **NIH AHRQ R01** · BAYLOR COLLEGE OF MEDICINE · 2020 · $498,859

## Abstract

The frequency of diagnostic errors in emergency departments (ED) is largely unknown but likely to be
significant. There is a compelling need to create measurement methods that provide diagnostic safety data to
clinicians and leaders who in turn can act upon these data to prevent diagnostic harm. Electronic trigger (e-
trigger) tools mine vast amounts of clinical and administrative data to identify signals for likely adverse events
and have demonstrated capability to identify diagnostic errors. Such tools are more efficient and effective than
other methods and can reduce the number of records requiring human review to those at highest risk of harm.
 In prior work, we used rules-based e-trigger algorithms to identify patterns of care suggestive of missed
or delayed diagnoses in primary care and inpatient settings. For instance, a clinic visit followed several days
later by an unplanned hospitalization could be indicative of potential problems with the diagnostic process at
the clinic visit. We also proposed a knowledge discovery framework, the Safer Dx Trigger Tools Framework, to
enable health care organizations (HCOs) to develop and implement e-trigger tools to measure diagnostic
errors using comprehensive electronic health record (EHR) data. Review and analysis of these cases can
uncover safety concerns and provide information on diagnostic process breakdowns and related contributory
factors, which in turn could generate learning and feedback for improvement purposes.
 Sophisticated techniques from machine learning (ML) and data science could help inform ‘second
generation’ e-trigger algorithms that better identify diagnostic errors and/or harm than rules-based e-triggers
that require substantial manual effort and chart reviews. In contrast to rules-based systems, ML techniques
could help learn from examples and accurately retrieve charts with diagnostic error without the need for “hand
crafting” of an e-trigger. We will apply e-triggers to comprehensive EHRs that contain longitudinal patient care
data (progress notes, tests, referrals) that provide an extensive picture of patients’ diagnostic journeys. Using
national VA data, including data from 9 million veterans, and data from Geisinger health system, a pioneer
HCO that serves approximately 3 million patients, we propose the following aims:
Aim 1 – To develop, refine, test, and apply Safer Dx e-triggers to enable detection, measurement, and learning
from diagnostic errors in diverse emergency department (ED) settings. We will calculate the frequency of
diagnostic errors in the ED based on these e-triggers and describe the burden of preventable diagnostic harm.
Aim 2 - To explore machine learning techniques that yield robust, accurate models to predict diagnostic errors
using EHR-enriched data derived from expert-labeled patient records containing diagnostic errors (from Aim 1).
 To our knowledge this is the first ML application in diagnostic error measurement, which could help
scale up expert-...

## Key facts

- **NIH application ID:** 10018015
- **Project number:** 5R01HS027363-02
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** HARDEEP SINGH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $498,859
- **Award type:** 5
- **Project period:** 2019-09-30 → 2022-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10018015

## Citation

> US National Institutes of Health, RePORTER application 10018015, Application of a Machine Learning to Enhance e-Triggers to Detect and Learn from Diagnostic Safety Events (5R01HS027363-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10018015. Licensed CC0.

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