Overview

Based on the best-selling book, Health Analytics, by Jason Burke. This course equips professionals with the ability to harness data analytics to drive innovation and improvement in patient care, operational performance, and financial outcomes. In today’s healthcare landscape, understanding data is vital for informed decisions that shape effective, sustainable systems. Through this course, learners will gain hands-on experience in applying analytical methods to healthcare problems. They’ll learn to interpret health data, integrate analytics into decision-making processes, and measure outcomes that influence patient satisfaction, quality of care, and organizational efficiency. Unlike traditional health management programs, this course combines foundational theory with real-world case studies. Learners will explore best practices for implementing analytics in healthcare organizations and building scalable data infrastructures to support evidence-based operations. This course is ideal for healthcare leaders, analysts, and managers seeking to improve care delivery through data-driven insights. No prior analytics experience is required, though a basic understanding of healthcare systems or business operations will be beneficial. © 2013 SAS Institute, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Syllabus

A Changing Business for a Changing Science

In this section, we examine challenges in health data aggregation and the need for standardization to enable knowledge discovery and informed decision-making.

Convergence and the Capability Map

In this section, we examine the importance of collaboration and diverse perspectives in problem-solving. Key concepts include team dynamics, convergence, and the capability map for effective decision-making.

The Four Enterprise Disciplines of Health Analytics

In this section, we explore enterprise health analytics, emphasizing data-driven strategies, frameworks, and capabilities for business leaders to build analytically powered organizations.

Dealing with Data

In this section, we examine Callimachus's early data organization methods, focusing on information retrieval and cataloging. The section highlights historical data management and its influence on modern information systems.

BEST Care, First Time, Every Time

In this section, we examine the role of evidence-based decision making in clinical practice, emphasizing data-driven approaches to improve health outcomes and the importance of scientific rigor in medical interventions.

Financial Performance and Reimbursement

In this section, we examine the interdependencies between financial performance, risk management, and care quality in health care. Key concepts include reimbursement models, patient-centered care, and analytics for optimizing value and outcomes.

Health Outcomes Analysis

In this section, we explore the evolution of medical practices and the role of data in shaping future healthcare.

Health Value and Cost

In this section, we examine healthcare value through cost-effectiveness analysis, outcome weighting, and activity-based costing to improve resource allocation and decision-making in health systems.

The New Behavioral Health

In this section, we examine how EHR portals influence patient behavior and service use, focusing on psychological factors and strategies for effective patient engagement.

Customer Insights

In this section, we examine the shift toward consumer-directed health care, focusing on patient insights, analytics frameworks, and strategies to improve service delivery and outcomes.

Risk Management

In this section, we explore healthcare risk categories, their interdependencies, and structured assessment methods to support informed decision-making and resource allocation.

Quality and Safety

In this section, we explore quality control measures, safety audits, and setting measurable targets to ensure compliance, reduce risks, and enhance organizational performance.

The New Research and Development

In this section, we examine the historical development of the scientific method and its impact on modern research. Key concepts include data analysis, structured frameworks, and continuous improvement in medical and analytical practices.

Conclusions

In this section, we explore value-based analytics models, comprehensive health performance analysis, and collaborative frameworks to drive innovation in health and life sciences.