Front Matter
Nursing Informatics: Systems, Data, AI, and Digital Practice
A Master's-Level Textbook for Digital Nursing Practice
2026 Edition
Educational Notice
This textbook is an educational resource. It does not replace organizational policy, legal counsel, security guidance, vendor documentation, current regulation, clinical judgment, or the official ANCC certification materials. Laws, standards, technology products, and certification requirements change; readers should verify current requirements before acting on time-sensitive information.
Preface
Nursing informatics is often introduced as the point where nursing, information science, and computer science meet. That definition is useful, but it can sound more abstract than the work actually is. In practice, nursing informatics is about how care becomes information, how information moves through systems, how technology changes work, and how people make decisions within those systems.
The informatics nurse may be analyzing why an assessment is documented twice, deciding whether a vendor integration is safe to scale, testing an EHR upgrade, explaining a FHIR interface to clinical leaders, reviewing a dashboard whose denominator is wrong, helping a team survive downtime, or deciding whether an AI system is ready for clinical use. The common thread is not the tool. The common thread is disciplined reasoning about people, processes, data, technology, risk, and outcomes.
This book is designed around that reality.
It does not assume that the reader wants to become a software developer. It does assume that a master’s-level informatics professional should understand enough technology to collaborate credibly with analysts, engineers, security professionals, vendors, clinical leaders, data teams, and executives. Technical material is therefore taught to the level needed for informed participation, questioning, translation, and judgment.
The text also treats artificial intelligence as part of contemporary informatics work rather than as a novelty added to the final chapter. AI appears throughout the book because it is already changing how requirements are written, how data are analyzed, how documentation is created, how systems are tested, and how decisions are supported. At the same time, the book repeatedly returns to one boundary: AI can accelerate informatics work, but it does not inherit the informaticist’s accountability.
How to Use This Book
Read the anchor bullet first
Each major concept begins with a bold bullet that states the central idea. The paragraph beneath it explains the mechanism, nuance, or consequence. If attention is limited, use the bullet to orient yourself before reading the explanation.
Stop at the Retrieval Checkpoint
Every lesson ends with a Retrieval Checkpoint. Answer the questions in a paper notebook without looking back first. These questions are deliberately more useful than highlighting because they reveal whether you can reconstruct the idea, distinguish it from nearby concepts, and apply it to a new situation.
Use the examples as part of the lesson
Examples are not side decorations. A difficult concept should become more concrete through the example, and the example should make you notice something about the concept that a definition alone would miss.
Treat figures as explanations
Figures and diagrams are designed to reveal relationships: what flows where, what depends on what, where a handoff occurs, or how several layers of a system interact. Do not skip them when the final graphical edition is available.
Do the chapter case before the quiz
The case study requires more synthesis than the quiz. Work through it before testing yourself on individual questions.
Use the lab manual separately
The companion lab manual is where concepts become work products. It includes spreadsheet analysis, SQL, workflow diagrams, FHIR inspection, AI-assisted requirements, test cases, model evaluation, governance exercises, and strategic deliverables.
ANCC NI-BC Learning Track
The book is aligned to the current ANCC Informatics Nursing Board Certification content outline, which organizes scored content into three domains: Foundations of Practice, System Design Lifecycle, and Data Management and Healthcare Technology.
Certification alignment appears in two places:
- NI-BC Connection at the end of relevant lessons; and
- a mapping table at the end of each chapter.
The book intentionally goes beyond the examination where graduate-level practice requires additional depth, particularly in cybersecurity, AI governance, portfolio management, vendor strategy, finance, and enterprise leadership.
Eligibility note: Learning the content in this book does not itself establish eligibility for NI-BC certification. Readers should verify the current ANCC eligibility requirements before applying.
How AI Is Used in This Book
AI appears in three ways:
- As a subject: Chapters 17 and 18 explain AI technology, evaluation, implementation, and governance.
- As a work tool: AI in Practice callouts show how an informaticist can use LLMs for analysis, drafting, critique, coding assistance, and structured reasoning.
- As an applied skill: The lab manual and Appendix A contain prompts and workflows that require the reader to use an LLM, verify its output, and identify where human judgment remains necessary.
The recurring rule is simple: never treat fluent output as validated output.
Parts of the Book
- Foundations of Nursing Informatics
- The System Design Lifecycle
- Data, Standards, and Interoperability
- Clinical and Consumer Technologies
- Artificial Intelligence and Emerging Informatics
- Strategy, Leadership, and the Future
About Sources and Currency
The book is U.S.-centric. U.S. professional standards, regulation, certification, and health-information policy therefore receive the most attention. Global material is used selectively to broaden context, especially where international digital-health strategy, terminology, standards, or comparative implementation improves understanding.
Because health IT changes quickly, the book distinguishes durable concepts from volatile details. A database key, sociotechnical principle, or usability concept will change slowly. An AI model name, certification policy, or federal interoperability requirement may change quickly. Volatile material is concentrated where it can be updated without destabilizing the whole manuscript.