Responsible Analytics for Superior Health Quality Outcomes

Responsible analytics in healthcare achieves superior health quality outcomes by ensuring data-driven decisions are ethical, transparent, and patient-centered. As healthcare systems rely on big data, AI, and predictive modeling, the importance of responsible data governance, bias mitigation, and privacy protection has become critical. Responsible analytics involves accountability in data collection, interpretation, and application in clinical and operational decisions. By promoting ethical standards and consumer trust, responsible analytics supports more equitable care delivery, enhances population health strategies, and drives improvements in patient outcomes. Responsible Analytics for Superior Health Quality Outcomes explores the potential for AI analytics to enable quality clinical decision making that leads to high quality and high value healthcare outcomes. It examines current techniques using AI tools, describing how best to harness their potential to ensure the delivery of quality healthcare. This book covers topics such as mental health, machine learning, and medical detection, and is a useful resource for business owners, medical professionals, healthcare workers, academicians, researchers, and data scientists.