# Heeding patient voices: Patient, nurse, and event characteristics associated with nurse judgments about safety concerns conveyed by inpatients in minority and other health disparity populations

> **NIH NIH R21** · UNIVERSITY OF IOWA · 2022 · $213,224

## Abstract

Project Summary Abstract
This innovative study will address patient safety and medical error reduction in health disparity populations by
examining nurse evaluation of patients’ safety concerns. Patients in hospitals are at risk of injury from illness,
hospital environment, and healthcare workers. Identification of patient safety risks and events frequently lacks
the critical patient perspective. Patients often rely on nurses to report their expressed safety concerns to the
organization’s incident reporting system (OIRS) for further action and learning. This can be problematic
because patients must rely on their nurse’s judgment about whether their safety concerns are valid and
should be reported to the OIRS for further action. If nurses do not judge the patient’s concern as credible or
important, or do not report the patient’s safety concern through the OIRS, safety can suffer through lack of
both immediate nurse response and long-term organizational response. There is evidence that nurses may
be biased against certain patient characteristics. Nurses’ judgments about the seriousness of patients’ safety
concerns and their subsequent decisions about whether to report them may be subject to such likely implicit
biases. However, very little is known about the factors that influence nurse judgments or the extent to which
this may lead to disparities in the reporting of patient safety concerns. The long-term goal of this research
program is to improve patient safety, mitigate risks, and prevent errors by developing targeted interventions
that support open and unbiased communication between nurses and patients about safety concerns and
allow organizations to respond rapidly to safety concerns. Aims of this foundational study are to 1) Determine
the role bias plays in nurses’ responses to patient safety concerns and 2) Identify factors influencing the
relationship between nurses’ biases and their responses to patient safety concerns. We will recruit 240 adult
inpatient hospital nurses from four states for this quantitative, cross-sectional factorial survey experiment. We
will collect survey data using an online factorial vignette survey to simulate scenarios in which patients
express safety concerns. Participants will 1) read eight vignettes, each including combinations of patient and
event characteristic levels, 2) answer questions about the vignettes, and 3) complete a demographic survey.
We will investigate hypothesized relationships among variables using multilevel linear models. Successful
completion of this exploratory and developmental research project will result in a model illustrating the
relationships among patient characteristics, event characteristics, nurse judgments of credibility and
importance, and nurse intent to communicate patient concerns through the OIRS. This foundation is
necessary to improve patient safety, mitigate risks, and prevent errors by developing targeted interventions
that support a) open and unbiased communication bet...

## Key facts

- **NIH application ID:** 10450218
- **Project number:** 1R21MD016139-01A1
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** Patricia S. Groves
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $213,224
- **Award type:** 1
- **Project period:** 2022-05-31 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10450218

## Citation

> US National Institutes of Health, RePORTER application 10450218, Heeding patient voices: Patient, nurse, and event characteristics associated with nurse judgments about safety concerns conveyed by inpatients in minority and other health disparity populations (1R21MD016139-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10450218. Licensed CC0.

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