# Integrating real-time clinical activity and behavioral responses for characterizing cognitive load and errors (IGNITE)

> **NIH AHRQ R01** · WASHINGTON UNIVERSITY · 2024 · $400,000

## Abstract

PROJECT SUMMARY
 Cognitive load during the delivery of clinical care is affected by a number of factors including specialty,
practice setting, patient complexity, electronic health record (EHR) use, and clinician expertise. In the United
States, clinical care is primarily documented using EHRs: documentation burden, poor usability, and
unnecessary navigation contribute to increased cognitive load. Such increases in cognitive load, in turn,
contribute to more work hours, dissatisfaction with work, poor patient outcomes (e.g., errors), burnout and poor
clinician outcomes. Much of the prior research characterizing clinician cognitive load or its impact on errors has
relied on retrospective approaches including self-reports, time-motion studies, and focus groups. Similarly,
burnout also has been measured exclusively using surveys. EHR-based audit logs have shown considerable
promise as a viable resource for tracking and measuring clinical activities without the incremental survey
burden on clinicians. Our research team has demonstrated that workload measures based on audit logs can
be used to assess cognitive load, burnout, and errors. Based on this promising pilot work, the primary focus of
the IGNITE (Integrating real-time clinical activity and behavioral responses for characterizing cognitive load
and errors) study is to utilize EHR-based audit logs and decision support tools to objectively determine the
direct relationships between (a) cognitive load and errors, and (b) the mediating role of clinician burnout in
explaining the relationship between cognitive load and errors. We will accomplish this through a large-scale
multi-site study conducted at three large academic medical centers associated with Washington University/BJC
HealthCare, Stanford University, and University of Colorado. For the first aim, we will utilize EHR-based audit
logs collected across non-surgical, inpatient settings across three sites over a 3-year period (1/1/2019 to
12/31/2021) to develop measures of cognitive load—both intrinsic and extraneous—and assess the effect of
cognitive load on objectively measured wrong-patient errors (using the retract-and-reorder alerts). For the
second aim, we will prospectively collect data on a cohort of 300 trainees (residents, fellows) from Medicine
and Pediatrics from each study site over a 5-month period. Monthly burnout surveys, along with cognitive load
measures from the EHR-based audit logs, and wrong-patient errors during the study period will be used to
determine the mediating relationships between cognitive load, burnout and errors. In addition, for both of the
proposed aims, we will develop advanced machine learning algorithms to predict errors and burnout from
EHR-based activity sequences. Insights from this study will help in designing targeted interventions aligned
with the contextual work practices of physicians, designing clinical trials for evaluating such interventions, and
in developing informed policy guidelines for the...

## Key facts

- **NIH application ID:** 10894033
- **Project number:** 5R01HS029020-03
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Thomas George Kannampallil
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2024
- **Award amount:** $400,000
- **Award type:** 5
- **Project period:** 2022-09-30 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10894033

## Citation

> US National Institutes of Health, RePORTER application 10894033, Integrating real-time clinical activity and behavioral responses for characterizing cognitive load and errors (IGNITE) (5R01HS029020-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10894033. Licensed CC0.

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