Nursing and the Algorithm

Ethics, Safety, and the Future of Clinical Judgment
1. Auflage 2026
ISBN: 978-1-041-22564-5
Verlag: Taylor & Francis Ltd

Buch, Englisch, 256 Seiten, Format (B × H): 178 mm x 254 mm

Nursing and the Algorithm examines one of the most urgent issues facing healthcare today: how nurses and health systems integrate artificial intelligence into bedside practice without undermining safety, ethics, or professional judgment.

While AI-driven clinical tools promise to improve efficiency and accuracy, they also risk eroding the human-centered reasoning that protects patients in complex care environments. Nursing and the Algorithm brings together nursing science, clinical informatics, and IT governance to provide a framework for safe, ethical, and effective AI integration in nursing practice. Existing works focus on AI in medicine broadly or on technical development, but few address how nursing judgment functions in real time at the bedside. This book fills that gap by showing how to preserve professional integrity while adopting algorithmic tools.

This book provides both a theoretical foundation and actionable strategies. It equips nurses, educators, administrators, and policy leaders with guidance to evaluate AI systems, safeguard ethical standards, and preserve the central role of human judgment in clinical decision-making.

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Collier, Alexis M.

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Pflege Krankenpflege
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Gesundheitswirtschaft

Inhaltsverzeichnis

Dedication

Conflict of Interest Disclosure

Acknowledgments

Preface

About the Author and Research Frameworks

How to Use This Textbook

A Note for Readers Pressed for Time

Abbreviations Used in This Textbook

List of Figures

List of Tables

List of Boxes

Chapter 1: Understanding the Algorithm in Healthcare

Chapter 2: Ethics in Algorithm-Assisted Nursing Care

Chapter 3: Safety Frameworks for Algorithm Integration

Chapter 4: Preserving Clinical Judgment in the Age of Algorithms

Chapter 5: Governance Structures for Algorithm Use

Chapter 6: Implementation Strategies for Algorithm-Assisted Practice

Chapter 7: The Future of Nursing in an Algorithmic World

Chapter 8: Education and Training for Algorithm-Competent Nurses

Chapter 9: Building Resilient Systems: Cybersecurity and Algorithm Safety

Conclusion: Nurses as Architects of Algorithmic Healthcare

Glossary

References

Appendix A, Algorithm Readiness Assessment Checklist and FDA Database Reader's Guide

Appendix B, Implementation Toolkit and Vendor RFP Question Template

Appendix C, Human vs. Algorithmic Clinical Judgment

Appendix D, Permissions and Third-Party Content Tracker

Appendix E, Originality Statement for Figures and Frameworks

Appendix F, Instructor's Guide and Teaching Resources

Appendix G, URL Validation and Technical Compliance Log

Appendix H, 2026 to 2030 Regulatory Timeline and Global Perspectives

Appendix I, Patient and Family Voices

Appendix J, Algorithmic Safety When Coverage Is Thin

Supplemental Practice and Implementation Materials

Über Autor(innen)

Alexis M. Collier, DHA, MHA, is a doctoral-prepared clinical AI researcher, founder, educator, and military officer whose work sits at the intersection of nursing informatics, health equity, health information systems, and translational healthcare innovation. Her central research question, why do many clinical AI systems improve predictive performance while worsening equity, and what would it take to build systems that do not, shapes both her scholarship and her commercial work.