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Related Experiment Video

Updated: May 13, 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 Balanced Nursing Teams Decision-Support System.

Senne Vleminckx¹, Peter Van Bogaert¹, Wim De Keyser²

¹Workforce management, Health systems, and Outcome Research in Care - Centre for Research and Innovation in Care, Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, Wilrijk, Antwerp, 2610, Belgium, 32 474859762.

|May 11, 2026

Summary

This summary is machine-generated.

Strategic nursing workforce optimization requires data-driven systems. Successful implementation hinges on addressing technological, organizational, and managerial barriers, not just software capabilities.

Keywords:

clinical decision-support systems implementation science nursing leadership nursing workforce management sociotechnical barriers

Related Experiment Videos

Last Updated: May 13, 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:

Healthcare Management
Nursing Informatics
Health Services Research

Background:

The global nursing workforce faces a crisis, necessitating a move from reactive staffing to strategic, data-driven workforce optimization.
Decision-support systems are crucial for evaluating nursing team balance across capacity, performance, and outcomes.
The Balanced Nursing Teams (BNT) system was developed to integrate diverse data points for comprehensive team assessment.

Purpose of the Study:

To reflect on the development and implementation of the BNT system.
To analyze barriers to the adoption of evidence-informed digital innovations in nursing workforce management.
To identify critical factors for successful data-driven workforce optimization in healthcare.

Main Methods:

A viewpoint paper analyzing the implementation of the BNT system across 8 diverse healthcare settings.
Utilized the Human-Organization-Technology fit framework to examine adoption challenges.
Collected data through system integration (HR, scheduling, EHR, quality registries) and a 360-degree staff survey.

Main Results:

Identified three interdependent barrier categories: technological fragmentation, organizational siloing, and managerial hesitance.
Implementation challenges were exacerbated by data integration burdens, particularly in organizations with low digital maturity.
Sustained implementation was achieved only in a nurse-led home health care organization with strong leadership control.

Conclusions:

Successful data-driven nursing workforce optimization depends on achieving fit across human, organizational, and technological domains, not solely on software sophistication.
The marginalization of nursing leadership in governance structures is a fundamental barrier to digital transformation in nursing workforce management.
Systemic investment in nursing leadership, data interoperability, and recognizing workforce optimization as a strategic imperative are essential.