NEAT: Nurse Effort Assessment Tool—Human Factors Considerations in Designing for Appropriate Staffing

This research has developed an algorithm to measure behavioral health acuity that relies predominantly on patient-level information from electronic health records (EHRs) but also has nurse-input-driven parameters contributing to the tool, known as the Nurse Effort Assessment Tool (NEAT). The overall objective is to create the NEAT to address the increasing acuity of behavioral patients by moving away from subjective patient assessment to using an automated algorithm that draws predominantly from the rich data in EHRs. Data from over 9,000 patient records provided statistically significant results that the subjective rating both correlates and serves as significant predictors for the NEAT score. Future research looks to expand the algorithm and validate it across multiple clinical sites.

Authors

Institutions

Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2024-09-01
DOI
https://doi.org/10.1177/10711813241261347
Primary Topic
Nursing education and management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NEAT: Nurse Effort Assessment Tool—Human Factors Considerations in Designing for Appropriate Staffing

Michael W. Boyce, Mark Sevilla, Leigh V. Evans, Scott Good
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Nursing education and management
article

NEAT: Nurse Effort Assessment Tool—Human Factors Considerations in Designing for Appropriate Staffing

Michael W. Boyce, Mark Sevilla, Leigh V. Evans, Scott Good
article en

Abstract

This research has developed an algorithm to measure behavioral health acuity that relies predominantly on patient-level information from electronic health records (EHRs) but also has nurse-input-driven parameters contributing to the tool, known as the Nurse Effort Assessment Tool (NEAT). The overall objective is to create the NEAT to address the increasing acuity of behavioral patients by moving away from subjective patient assessment to using an automated algorithm that draws predominantly from the rich data in EHRs. Data from over 9,000 patient records provided statistically significant results that the subjective rating both correlates and serves as significant predictors for the NEAT score. Future research looks to expand the algorithm and validate it across multiple clinical sites.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 68(1)
Yale University (US), Yale New Haven Health System (US)
Openalex Percentile: Top 92%
Nursing education and management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.