Analytics and Decision Support in Health Care Operations Management

Yasar A. Ozcan

• Publisher: Jossey-Bass
• Year: 2017
• Print ISBN: 9781119219811
• eBook ISBN: 9781119219828
• Edition: 3
• Topic: Medicine
• Subtopic: Public Health, Administration & Care

A compendium of health care quantitative techniques based in Excel

Analytics and Decision Support in Health Care Operations is a comprehensive introductory guide to quantitative techniques, with practical Excel-based solutions for strategic health care management. This new third edition has been extensively updated to reflect the continuously evolving field, with new coverage of predictive analytics, geographical information systems, flow process improvement, lean management, six sigma, health provider productivity and benchmarking, project management, simulation, and more. Each chapter includes additional new exercises to illustrate everyday applications, and provides clear direction on data acquisition under a variety of hospital information systems. Instructor support includes updated Excel templates, PowerPoint slides, web based chapter end supplements, and data banks to facilitate classroom instruction, and working administrators will appreciate the depth and breadth of information with clear applicability to everyday situations.

The ability to use analytics effectively is a critical skill for anyone involved in the study or practice of health services administration. This book provides a comprehensive set of methods spanning tactical, operational, and strategic decision making and analysis for both current and future health care administrators.

• Learn critical analytics and decision support techniques specific to health care administration
• Increase efficiency and effectiveness in problem-solving and decision support
• Locate appropriate data in different commonly-used hospital information systems
• Conduct analyses, simulations, productivity measurements, scheduling, and more

From statistical techniques like multiple regression, decision-tree analysis, queuing and simulation, to field-specific applications including surgical suite scheduling, roster management, quality monitoring, and more, analytics play a central role in health care administration. Analytics and Decision Support in Health Care Operations provides essential guidance on these critical skills that every professional needs.

Chapter 1
Introduction to Analytics and Decision Support in Health Care Operations Management

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Learning Objectives

• Recognize the analytical techniques for decisions about delivering health care of high quality.
• Describe the historical background and the development of decision techniques.
• Describe the health care manager's role and responsibilities in decision making.
• Review the scope of health services and follow recent trends in health care.
• Describe health services management and distinct characteristics of health services.
• Describe the data flow in health care organizations and how to organize data for analytics.

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In today's highly complicated, technological, and competitive health care arena, the public's outcry is for administrators, physicians, and other health care professionals to provide high-quality care at a lower cost. While an aging population, increase in chronic conditions, and more insurance coverage create higher demand, mass access to social media and other mobile technologies bring higher expectations for care outcomes from patients and their families. Health care managers must therefore find ways to get excellent results from more limited resources. To cater to these new demands and adapt the technologies, health care managers must use a new strategic asset called big data. Big data may come from electronic medical records, social media, public health records, and so on. Hence, only those managers who can seek, organize, and analyze big data will survive as successful managers.

The goal of this book is to introduce aspiring health care managers to analytic and decision support models that allow decision makers to sort out complex issues and to make the best possible use of available resources. Such models are used, for example, to forecast patient demand, and to guide capital acquisition and capacity decisions, facility planning, personnel and patient scheduling, supply chain management, and quality control. They use mathematical and statistical techniques: multivariate statistical analysis, decision analysis, linear programming, project evaluation and review technique (PERT), queuing analysis, and simulation, to name a few. This book presents all these techniques from the perspective of health care organizations' delivery of care, rather than their traditional manufacturing applications. This chapter covers a brief historical background and the development of decision techniques and explains the importance of health care managers using these techniques. Finally, the scope, distinctive characteristics, and current trends of health services are emphasized. After reading this chapter, you should have a fair understanding of how important quantitative techniques are for decisions about delivering health care of high quality.

Historical Background and the Development of Decision Techniques

Beginning in the 1880s, the scientific management era brought about widespread changes in the management of the factories that had been created at an explosive rate during the Industrial Revolution. The movement was spearheaded by an efficiency engineer and inventor, Frederick Winslow Taylor, who is regarded as the father of modern scientific management. Taylor proposed a “science of management” based on observation, measurement, analysis, and improvement of work methods, along with economic incentives. He also believed that management's tasks are to plan, carefully select and train workers, find the best way to perform each job, achieve cooperation between management and workers, and separate management activities from work activities. Taylor's work was based on his idea that conflicts between labor and management occur because management has no idea how long jobs actually take. He therefore focused on time studies that evaluated work methods in great detail to identify the best way to do each job. Taylor's classic 1911 book, The Principles of Scientific Management, explained these guiding principles: (1) development of science for each element of work, (2) scientific selection and training of w...

Table of contents

• Cover
• Title Page
• Copyright
• Dedication
• Tables & Figures
• Acknowledgments
• The Author
• Introduction
• Chapter-by-Chapter Revisions for the Third Edition
• Chapter 1: Introduction to Analytics and Decision Support in Health Care Operations Management
• Chapter 2: Predictive Analytics
• Chapter 3: Decision Making in Health Care
• Chapter 4: Facility Location
• Chapter 5: Facility Layout
• Chapter 6: Flow Processes Improvement: Reengineering and Lean Management
• Chapter 7: Staffing
• Chapter 8: Scheduling
• Chapter 9: Productivity and Performance Benchmarking
• Chapter 10: Resource Allocation
• Chapter 11: Supply Chain and Inventory Management
• Chapter 12: Quality Control and Improvement
• Chapter 13: Project Management
• Chapter 14: Queuing Models and Capacity Planning
• Chapter 15: Simulation
• Appendixes
• References
• Index
• End User License Agreement