Overview

In healthcare, AI is not a feature - it is a clinical intervention. Every AI system introduced into care delivery changes decisions, workflows, and risk. When implemented well, it can reduce burnout, improve outcomes, and save lives. When implemented poorly, it creates silent failures, bias, regulatory exposure, and loss of trust. Many organizations are stuck in the ""pilot graveyard,"" experimenting without validation, governance, or scale. AI for Healthcare is written for clinicians, operators, informaticists, and health system leaders who need a rigorous, practical path to deploying AI safely in real clinical environments. This is not a vendor catalog or a research survey. It is an execution-focused playbook for designing, validating, governing, and operating healthcare AI - from first idea to enterprise deployment - with patient safety and clinical utility as non-negotiables. INCLUDED IN THIS BOOK The Clinical AI Playbook: A step-by-step guide to moving from a sticky-note idea to a validated clinical pilot and enterprise scale without falling into the pilot graveyard. 7+ Decision Frameworks: Practical models including the CARE strategy model, the SCOPE problem-definition framework, and the TRIP feasibility check. 30+ HIPAA-Safe GenAI Prompts: Ready-to-use scripts for drafting appeal letters, translating medical jargon, auditing operations, and stress-testing vendor claims. The Governance & Safety Toolkit: Deployable templates for Clinical Model Cards, Datasheets, incident response forms, and acceptable use policies. Vendor Vetting Checklists: Evidence-based scorecards grounded in TRIPOD and CONSORT to separate medical-grade systems from marketing vaporware. The Health Economics Calculator: A framework for measuring hard ROI, soft ROI, and the Quadruple Aim to justify investment to finance leaders. 8 Deep-Dive Case Studies: Unvarnished analyses of real deployments, including sepsis monitoring, stroke triage, and inbox automation. WHAT'S INSIDE THIS BOOK Part I: AI Literacy for the Hybrid Professional A clinically grounded explanation of how AI works, how healthcare data differs from other domains, and how to evaluate evidence beyond accuracy. Part II: Building, Validating & Safety Problem framing with SCOPE, rigorous validation pathways, and safety engineering practices such as FMEA and continuous drift monitoring. Part III: Clinical Applications Real-world use cases across administrative relief, diagnostics, surgery, mental health, genomics, and remote patient monitoring. Part IV: Operational & Financial Strategy AI for throughput, capacity planning, revenue cycle management, and population health - designed for measurable impact, not dashboards. Part V: Governance, Law & Ethics FDA SaMD considerations, liability realities, bias auditing, and the infrastructure required to deploy AI responsibly at scale. Part VI: Case Studies & The Playbook Detailed wins and failures, practical recovery lessons, and a forward-looking roadmap for agentic AI and digital twins in care delivery. If AI is already touching patient care, this book shows you how to deploy it with rigor, safety, and clinical trust. Written for physicians, nurses, healthcare executives, informaticists, data scientists, and compliance leaders who need AI that works in the real world - not just in demos.

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