Patient safety is foundational to achieving high-quality healthcare outcomes, yet preventable harm remains a major public health problem. In the United States, unintended patient harm has been estimated to rank among the leading causes of death, underscoring the urgency of system-level approaches to safety. Health informaticists occupy a uniquely powerful position: nearly every informatics intervention—data capture, decision support, workflow automation, analytics, or artificial intelligence—can either mitigate risk or introduce new hazards. To reduce harm effectively, informatics practice must be grounded in the principles of patient safety, systems engineering, human factors, and quality improvement science. This chapter introduces core patient safety concepts beginning with systems thinking and the ability to reason holistically about the interdependencies, trade-offs, and emergent behaviors within complex healthcare systems. It then presents established tools and frameworks for identifying risk, failure modes, and latent system vulnerabilities, illustrating how informatics solutions can unintentionally exacerbate harm when human factors, usability, and sociotechnical context are neglected. The chapter concludes by examining how the growing use of artificial intelligence and machine learning amplifies both opportunity and risk, requiring more rigorous governance, transparency, and safety-oriented design to ensure that advanced analytics contribute to safer, more reliable care rather than new forms of failure.