Data Driven Approaches For Healthcare

Data-Driven Healthcare

Healthcare is changing, and data is the catalyst Data is taking over in a powerful way, and it's revolutionizing the healthcare industry. You have more data available than ever before, and applying the right analytics can spur growth. Benefits extend to patients, providers, and board members, and the technology can make centralized patient management a reality. Despite the potential for growth, many in the industry and government are questioning the value of data in health care, wondering if it's worth the investment. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry tackles the issue and proves why BI is not only worth it, but necessary for industry advancement. Healthcare BI guru Laura Madsen challenges the notion that data have little value in healthcare, and shows how BI can ease regulatory reporting pressures and streamline the entire system as it evolves. Madsen illustrates how a data-driven organization is created, and how it can transform the industry. Learn why BI is a boon to providers Create powerful infographics to communicate data more effectively Find out how Big Data has transformed other industries, and how it applies to healthcare Data-Driven Healthcare: How Analytics and BI are Transforming the Industry provides tables, checklists, and forms that allow you to take immediate action in implementing BI in your organization. You can't afford to be behind the curve. The industry is moving on, with or without you. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry is your guide to utilizing data to advance your operation in an industry where data-fueled growth will be the new norm.

Data Driven Approaches for Healthcare

Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics

Perspectives on Designing Data-driven Approaches in Healthcare Based on Real-world Evidence