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Updated: Jun 30, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

From Concept to Practice: Lessons from the BNuT Balanced Nursing Teams Decision-Support System-A Viewpoint Paper.

Senne Vleminckx¹, Peter Van Bogaert¹, Wim De Keyser²

¹University of Antwerp, Universiteitsplein 1, Wilrijk, BE.

|April 2, 2026

Summary

Strategic nursing workforce optimization requires data-driven systems. The Balanced Nursing Teams (BNuT) system faced adoption barriers due to technological, organizational, and managerial issues, highlighting the need for nursing leadership integration.

Related Experiment Videos

Last Updated: Jun 30, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

Area of Science:

Health Services Research
Nursing Informatics
Organizational Behavior

Background:

The global nursing workforce crisis necessitates a move from reactive staffing to strategic, data-driven workforce optimization.
Existing decision-support systems often fail to integrate diverse data points effectively, hindering comprehensive analysis.

Purpose of the Study:

To analyze the implementation challenges of the Balanced Nursing Teams (BNuT) system, a data-driven decision-support tool for nursing workforce optimization.
To identify barriers to the adoption of evidence-informed digital innovations in healthcare settings using the Human-Organization-Technology fit (HOT-fit) framework.

Main Methods:

Development and attempted implementation of the BNuT system, integrating ~250 data points from organizational systems and staff surveys.
Utilized the Human-Organization-Technology fit (HOT-fit) framework to analyze implementation barriers across eight diverse healthcare settings.
Comparative analysis of successful versus unsuccessful BNuT implementation sites.

Main Results:

Identified three key barrier categories: technological fragmentation, organizational siloing, and managerial hesitance.
Substantial data-integration burdens disproportionately affected organizations with lower digital maturity.
Sustained implementation was achieved only in a nurse-led home healthcare organization with integrated leadership and resource control.

Conclusions:

Successful data-driven nursing workforce optimization hinges on achieving simultaneous fit across human, organizational, and technological domains, not just software maturity.
The marginalization of nursing leadership in governance structures is a fundamental barrier to digital transformation in nursing workforce management.
Systemic investment in nursing leadership authority, data interoperability, and recognizing workforce data as strategic imperatives are crucial for effective digital transformation.