Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement.

Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a "how-to" manual, showing how to use existing theory and tools to achieve desired positive outcomes.

You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes:

• A thorough introduction to data, caring theory, Relationship-Based Care^(R), the Caring Behaviors Assurance System^(c), and healthcare operations, including how to build a measurement model and improve organizational outcomes.
• An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more--all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes.
• Discussions of how to refine quality improvement initiatives, including the use of "comfort" as a construct to illustrate the importance of solid theory and good measurement in adequate pain management.
• An examination of international organizations using analytics to improve operations within cultural context.

Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.