Analytics and Decision Support in Health Care Operations Management E-Book

Yasar A. Ozcan

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Jossey-Bass Public Health/Health Services Text
• Sprache: Englisch

Seitenzahl: 882

Veröffentlichungsjahr: 2017

Beschreibung

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.

Leseprobe

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

Historical Background and the Development of Decision Techniques

The Health Care Manager and Decision Making

Importance of Health Analytics: Information Technology (IT) and Decision Support Techniques

The Scope of Health Care Services, and Recent Trends

Health Care Services Management

Distinctive Characteristics of Health Care Services

Big Data and Data Flow in Health Care Organizations

Summary

Exercises

Chapter 2: Predictive Analytics

Steps in the Predictive Analytics Process

Predictive Analytics Techniques

Summary

Exercises

Chapter 3: Decision Making in Health Care

The Decision Process

The Decision Tree Approach

Decision Analysis with Nonmonetary Values and Multiple Attributes

Clinical Decision Making and Implications for Management

Summary

Exercises

Chapter 4: Facility Location

Location Methods

Summary

Exercises

Chapter 5: Facility Layout

Product Layout

Process Layout

Computer-Based Layout Programs

Fixed-Position Layout

Summary

Exercises

Chapter 6: Flow Processes Improvement: Reengineering and Lean Management

Reengineering

Lean Management

Work Design in Health Care Organizations

Summary

Exercises

Chapter 7: Staffing

Workload Management Overview

Summary

Exercises

Chapter 8: Scheduling

Staff Scheduling

Surgical Suite Resource Scheduling

Summary

Exercises

Chapter 9: Productivity and Performance Benchmarking

Trends in Health Care Productivity: Consequences of Reforms and Policy Decisions

Productivity Definitions and Measurements

Commonly Used Productivity Ratios

The Relationships between Productivity and Quality in Hospital Settings

Summary of Productivity-Related Dilemmas in the Hospital Setting

Dealing with the Multiple Dimensions of Productivity: New Methods of Measurement and Benchmarking

Data Envelopment Analysis

Overview on Improving Health Care Productivity

Summary

Exercises

Chapter 10: Resource Allocation

Linear Programming

Integer Linear Programming

Summary

Exercises

Chapter 11: Supply Chain and Inventory Management

Health Care Supply Chain

Summary

Exercises

Chapter 12: Quality Control and Improvement

Quality in Health Care

Quality Measurement and Control Techniques

Process Improvement

Summary

Exercises

Chapter 13: Project Management

The Characteristics of Projects

Project Management Applications in Clinical Settings: Clinical Pathways

Summary

Key Terms

Exercises

Chapter 14: Queuing Models and Capacity Planning

Queuing System Characteristics

Capacity Analysis and Costs

Summary

Exercises

Chapter 15: Simulation

Simulation Process

Performance Measures and Managerial Decisions

Excel-Based Simulation Templates with Performance Measures and Managerial Decisions

Multiphase Simulation Model

Summary

Exercises

Appendixes

Appendix A: Standard Normal Distribution

Appendix B: Standard Normal Distribution

Appendix C: Cumulative Poisson Probabilities

Appendix D: t-Distribution

References

Index

End User License Agreement

List of Tables

Table 1.1

Table 1.2

Table 2.1

Table 2.2

Table 2.3

Table 2.4

Table 2.5

Table 2.6

Table 2.7

Table EX 2.1

Table EX 2.2

Table EX 2.3

Table EX 2.5

Table EX 2.6

Table EX 2.8

Table EX 2.9

Table EX 2.10

Table EX 2.11

Table EX 2.12

Table EX 2.13

Table EX 2.14

Table EX 2.16

Table EX 2.17

Table EX 2.18

Table EX 2.19

Table EX 2.24

Table EX 2.25

Table EX 2.26

Table EX 2.27

Table EX 2.28

Table EX 2.29

Table 3.1

Table 3.2

Table 3.3

Table 3.4

Table 3.5

Table 3.6

Table 3.7

Table 3.8

Table 3.9

Table 3.10

Table 3.11

Table 3.12

Table 3.13

Table EX 3.1

Table EX 3.2

Table EX 3.3

Table EX 3.4

Table EX 3.5

Table EX 3.6

Table EX 3.6.1

Table EX 3.7

Table EX 3.8

Table EX 3.9

Table EX 3.10.1

Table EX 3.10.2

Table EX 3.10.3

Table EX 3.11

Table EX 3.12

Table EX 3.13

Table EX 3.16

Table EX 3.18.1

Table EX 3.18.2

Table EX 3.21

Table EX 3.22

Table EX 3.23

Table EX 3.24

Table EX 3.25

Table EX 3.26

Table EX 3.27

Table EX 3.28.1

Table EX 3.28.2

Table EX 3.28.3

Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 4.5

Table 4.6

Table 4.7

Table 4.8

Table 4.9

Table 4.10

Table EX 4.5

Table EX 4.6

Table EX 4.7

Table EX 4.8

Table EX 4.9

Table EX 4.10

Table EX 4.11

Table Ex 4.12

Table EX 4.13.1

Table EX 4.13.2

Table EX 4.15.1

Table EX 4.15.2

Table 5.1

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Table EX 5.2.1

Table EX 5.2.2

Table EX 5.6

Table EX 5.7

Table EX 5.8

Table EX 5.9

Table EX 5.10.2

Table EX 5.10.3

Table EX 5.10.4

Table EX 5.11.2

Table EX 5.12.2

Table EX 5.12.3

Table EX 5.12.4

Table EX 5.14.2

Table EX 5.14.3

Table EX 5.14.4

Table 6.1

Table 6.2

Table 6.3

Table 6.4

Table 6.5

Table 6.6

Table 6.7

Table EX 6.2

Table EX 6.3

Table EX 6.4

Table EX 6.5

Table EX 6.6

Table EX 6.10.1

Table EX 6.10.2

Table EX 6.10.3

Table EX 6.17

Table EX 6.18

Table EX 6.19.1

Table EX 6.19.2

Table EX 6.20

Table EX 6.21

Table 7.1

Table 7.2

Table 7.3

Table 7.4

Table 7.5

Table 7.6

Table 7.7

Table EX 7.1

Table EX 7.2

Table EX 7.3

Table EX 7.5

Table EX 7.6

Table EX 7.11

Table EX 7.13

Table EX 7.14

Table EX 9.1

Table EX 9.2

Table EX 9.3

Table EX 9.4

Table EX 9.6

Table EX 9.7

Table EX 9.8

Table EX 9.9

Table EX 9.10

Table EX 9.11

Table EX 9.12

Table EX 9.13

Table 10.1

Table EX 10.3

Table EX 10.4

Table EX 10.5

Table EX 10.6.1

Table EX 10.6.2

Table EX 10.8

Table EX 10.9

Table 11.1

Table EX 11.7

Table EX 11.8

Table EX 11.9

Table EX 11.10

Table EX 11.11

Table EX 11.12

Table 12.1

Table EX 12.1

Table EX 12.2

Table EX 12.3

Table EX 12.4

Table EX 12.5

Table EX 12.6

Table EX 12.9

Table EX 12.10

Table EX 12.11

Table EX 12.12

Table EX 12.13

Table EX 12.22

Table EX 12.23

Table 13.1

Table 13.2

Table 13.3

Table 13.4

Table 13.5

Table 13.6

Table EX 13.4

Table EX 13.5

Table EX 13.6

Table EX 13.7

Table EX 13.8

Table EX 13.9

Table EX 13.10

Table EX 13.11

Table EX 13.14

Table EX 13.15

Table EX 13.16

Table EX 13.18

Table EX 13.19

Table EX 13.20

Table EX 13.21

Table EX 13.22

Table EX 13.23.1

Table EX 13.23.2

Table 14.1

Table EX 14.11.1

Table EX 14.11.2

Table EX 14.11.3

Table EX 14.11.4

Table EX 14.11.5

Table EX 14.13.1

Table EX 14.13.2

Table 15.1

Table 15.2

Table 15.3

Table 15.4

Table 15.5

Table 15.6

Table 15.7

Table 15.8

List of Illustrations

Figure 1.1

Figure SE 1.1

Figure SE 1.2

Figure SE 1.3

Figure SE 1.4

Figure SE 1.5

Figure SE 1.6

Figure SE 1.7

Figure SE 1.8

Figure SE 1.9

Figure SE 1.10

Figure SE 1.11

Figure SE 1.12

Figure SE 1.13

Figure SE 1.14

Figure SE 1.15

Figure SE 1.16

Figure SE 1.17

Figure SE 1.18

Figure SE 1.19

Figure SE 1.20

Figure SE 1.21

Figure SE 1.22

Figure SE 1.23

Figure SE 1.24

Figure SE 1.25

Figure SE 1.26

Figure SE 1.27

Figure SE 1.28

Figure SE 1.29

Figure EX 1.6

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 2.5

Figure 2.6

Figure 2.7

Figure 2.8

Figure 2.9

Figure 2.10

Figure 2.11

Figure 2.12

Figure 2.13

Figure 2.14

Figure 2.15

Figure 2.16

Figure 2.17

Figure 2.18

Figure 2.19

Figure 2.20

Figure 2.21

Figure 3.1

Figure 3.2

Figure 3.3

Figure 3.4

Figure 3.5

Figure 3.6

Figure 3.7

Figure EX 3.19

Figure EX 3.20

Figure 4.1

Figure 4.2

Figure 4.3

Figure 4.4

Figure 4.5

Figure 4.6

Figure 4.7

Figure 4.8

Figure 4.9

Figure 4.10

Figure 5.1

Figure 5.2

Figure 5.3

Figure 5.4

Figure 5.5

Figure 5.6

Figure 5.7

Figure EX 5.1

Figure EX 5.4

Figure EX 5.5

Figure EX 5.10.1

Figure 5.11.1

Figure EX 5.12.1

Figure EX 5.13

Figure EX 5.14.1

Figure EX 5.14.5

Figure EX 5.14.6

Figure 6.1

Figure 6.2

Figure 6.3

Figure 6.4

Figure 6.5

Figure 6.6

Figure 6.7

Figure 6.8

Figure 6.9

Figure 6.10

Figure 6.11

Figure 6.12

Figure 6.13

Figure 6.14

Figure 6.15

Figure 6.16

Figure EX 6.22

Figure 7.1

Figure 7.2

Figure 7.3

Figure 8.1

Figure 8.2

Figure 8.3

Figure 8.4

Figure 9.1

Figure 9.2

Figure 9.3

Figure 10.1

Figure 10.2

Figure 10.3

Figure 10.4

Figure 10.5

Figure 10.6

Figure 10.7

Figure 10.8

Figure 10.9

Figure 10.10

Figure 10.11

Figure 10.12

Figure 10.13

Figure 10.14

Figure 10.15

Figure 10.16

Figure 10.17

Figure 10.18

Figure 10.19

Figure 11.1

Figure 11.2

Figure 11.3

Figure 11.4

Figure 11.5

Figure 12.1

Figure 12.2

Figure 12.3

Figure 12.4

Figure 12.5

Figure 12.6

Figure 12.7

Figure 12.8

Figure 12.9

Figure 12.10

Figure 12.11

Figure 12.12

Figure 12.11

Figure 12.12

Figure 12.13

Figure 12.14

Figure EX 12.19

Figure 13.1

Figure 13.2

Figure 13.3

Figure 13.4

Figure 13.5

Figure 13.6

Figure 13.7

Figure 13.8

Figure 13.9

Figure 13.10

Figure 13.11

Figure 13.12

Figure 13.13

Figure 13.14

Figure 13.15

Figure EX 13.1

Figure EX 13.2

Figure EX 13.12

Figure EX 13.23

Figure 14.1

Figure 14.2

Figure 14.3

Figure 14.4

Figure 14.5

Figure 14.6

Figure 14.7

Figure 14.8

Figure 14.9

Figure 14.10

Figure 14.11

Figure 14.12

Figure 14.13

Figure 14.14

Figure 14.15

Figure 14.16

Figure 15.1

Figure 15.2

Figure 15.3

Figure 15.4

Figure 15.5

Figure 15.6

Guide

Cover

Table of Contents

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Chapter 1

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Analytics and Decision Support in Health Care Operations Management

History, Diagnosis, and Empirical Foundations

Third Edition

Yasar A. Ozcan

With Contributions by Hillary A. Linhart

Copyright © 2017 by John Wiley & Sons, Inc. All rights reserved.

Published by Jossey-BassA Wiley BrandOne Montgomery Street, Suite 1000, San Francisco, CA 94104-4594—www.josseybass.com

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Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Readers should be aware that Internet Web sites offered as citations and/or sources for further information may have changed or disappeared between the time this was written and when it is read.

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Library of Congress Cataloging-in-Publication Data

Names: Ozcan, Yasar A., author. | Linhart, Hillary A., contributor.

Title: Analytics and decision support in health care operations management : history, diagnosis, and empirical foundations / Yasar A. Ozcan, with contributions by Hillary A. Linhart.

Other titles: Quantitative methods in health care management

Description: Third edition. | San Francisco : Jossey-Bass & Pfeiffer Imprints, Wiley, [2017] | Preceded by Quantitative methods in health care management / Yasar A. Ozcan. 2nd ed. c2009. | Includes bibliographical references and index.

Identifiers: LCCN 2016055930 (print) | LCCN 2016057247 (ebook) | ISBN 9781119219811 (pbk.) | ISBN 9781119219835 (pdf) | ISBN 9781119219828 (epub)

Subjects: | MESH: Statistics as Topic | Health Services Administration | Decision Making, Organizational | Decision Support Techniques | Models, Theoretical

Classification: LCC RA394 (print) | LCC RA394 (ebook) | NLM WA 950 | DDC 362.1072/7—dc23

LC record available at https://lccn.loc.gov/2016055930

Cover Design: WileyCover Photo: ©Supphachai Salaeman/Shutterstock

To my family,

Gulperi, Nilufer, Gunes, Kevin, and Skyler

Acknowledgments

Writing this book could not have been achieved without the help and encouragement of many individuals. I take this opportunity to thank them; if I miss anyone, it is through authorial oversight only, as all the help received was deeply appreciated. First of all, thanks go to my graduate students from the MSHA and MHA students of the past two decades who lent their real-life experiences with analytics through experiential class projects and associated materials and data, which are used in the examples and exercises throughout the text. In that vein, I thank more specifically Hillary Anne Linhart who converted many of these materials to useful examples and additional chapter-end exercises as well as supplemental exercises in MS Excel and developed chapter content related to several topics including data flow and lean management. I would like to also acknowledge Hillary's diligent editing of the manuscript from cover to cover.

I am also indebted to Hakan Kacak who diligently created new Excel templates and updated the others which all are available for instructors, students, and practicing managers.

I extend my sincere thanks as well to Jossey-Bass/Wiley executive editor Patricia Rossi for her cooperativeness and help in the production of this manuscript.

No book can be written on time without the support and encouragement of loved ones. I am indebted to my wife, Gulperi Ozcan, who became my sounding board for many examples in this book. Moreover, she extended her support throughout the development of the manuscript even as I deprived her of my time in favor of my desktop. I thank her for the sustained support she has given me throughout my academic career and our personal life.

Yasar A. Ozcan, Ph.D.November 2, 2016Richmond, Virginia

The Author

Yasar A. Ozcan, Ph.D. is a professor in the Department of Health Administration, Virginia Commonwealth University (VCU), where he has served as a faculty member for over thirty-seven years. Dr. Ozcan teaches health analytics and decision support courses in graduate professional programs in health administration, and methodology courses at the doctoral level. He has served twice as president of the Health Applications Section in the Institute of Operations Research and Management Science. Professor Ozcan is the founding Editor in Chief of a highly regarded journal, Health Care Management Science, coeditor of the Journal of Central Asian Health Services Research, and serves on the boards of several international journals.

Dr. Ozcan has been principal and co-principal investigator on various federal and state grants and contracts. He has also provided management consultancy services to health care facilities and managed care organizations.

Dr. Ozcan's scholarly work is in the area of health care provider performance. Specifically, he has applied data envelopment analysis to measure efficiency across the range of health care facilities and practices, including hospitals, nursing homes, health maintenance organizations, mental health care organizations, physician practices, and other facilities. He has published another book in this area entitled Health Care Benchmarking and Performance Evaluation. He has presented numerous papers in professional meetings and published extensively in these areas.

Dr. Ozcan has long been active in distance education, having taught health analytics and decision support, the content of this book, both in the traditional MHA and Executive MSHA graduate programs at VCU since the mid-1980s.

Introduction

This book is written to meet the need for analytics and decision support (quantitative methods) in health administration, health care management, or other programs that have such content in their curriculum. It is designed so that it can be used for one-semester courses in graduate programs as well as for advanced undergraduate programs in health care management and administration. Practical and contemporary examples from the field make it a useful reference book for health care managers as well. The changes from second to third edition are listed further below to help the previous adapters to adjust their teaching planning accordingly.

The analytic techniques offered in this book are those more amenable to the health care management environment and those most frequently used. The third edition employs more intense use of Excel. Although the simpler examples are demonstrated in the text, their Excel solutions are also provided. As techniques increase in sophistication, as for example in queuing models, Excel template solutions are preferred to lengthy formulas and look-up tables. The third edition also incorporates additional learning objectives at the beginning of each chapter and key terms at the end of each chapter to facilitate the appropriate pedagogy for learning. Because the intent of the book is to make students into able users of analytic techniques for decision making, the interpretation of the results from hand-calculated or Excel solutions to guide for informed decision making is the foremost goal. Thus, students who have had basic algebra and introductory statistics courses should be able to follow the contents of this book.

The book has fifteen chapters including the introductory chapter. The presentation of analytic techniques starts with predictive analytics, which provides the data for many of the other techniques discussed, as well as for planning in health care facilities. The chapter on decision making provides the decision techniques not only for single attribute decision theory, but also for the multi-attribute methods often used in health care management decisions, especially in evaluating new contracts or in requests for proposals.

Chapters 4 and 5 provide techniques for facility location and layout. The techniques discussed for layout also can be used to improve flows in facilities. Hence, in Chapter 6, flow process improvement via reengineering and lean management is introduced as the means to identify bottlenecks in operational processes and to correct them. Chapters 7 and 8 cover staffing and resource scheduling management in health care facilities; surgical suite resource management is highlighted. These two chapters can be assigned and covered together in one session. Chapter 9, on productivity, not only presents the traditional productivity concepts and their measurements in both inpatient and outpatient settings, but also discusses more contemporary methods of productivity measurements as conducted through data envelopment analysis. Chapter 10 explains linear programming and its use in resource allocation. Furthermore, integer programming, an extension of linear programming, is discussed and illustrated for staff scheduling.

Supply chain management in health care has become popular in recent decades, and the first part of Chapter 11 discusses that; the second part of the chapter is devoted to traditional techniques for inventory management. Quality control, essential above all in health care, is discussed in Chapter 12. Types of control charts and their developments are illustrated. Several approaches to quality control, including total quality management, continuous quality improvement, and six-sigma, are discussed. The tools for quality improvement are presented.

Project management is the subject of Chapter 13, where program evaluation and review technique/critical path method (PERT/CPM) techniques are discussed in detail, with examples of project compression. The last two chapters cover queuing and simulation techniques with emphasis on capacity decisions using those tools. Simple queuing methods are shown with detailed examples. More sophisticated ones are illustrated by Excel solutions.

The sequence of chapters has a certain logic. For example, in Chapter 4, the location of a new facility is identified; and in Chapter 5, layout of that facility can be explored. On the other hand, Chapter 5 can be also used in an independent layout analysis for existing facilities to improve flow and productivity. Similarly, Chapters 6, 7, 8, and 9 are built to feed the knowledge onward. Chapters 14 and 15 address capacity issues using different techniques. Regardless of this sequence, however, the chapters can be selected in any order and presented to students based on the professor's preferences.

Developing exercises for the techniques explained in each chapter has been a consuming task. Any errors and oversights in that process are solely mine. I will appreciate reader comments to improve or correct the exercises, as well as suggestions for incorporating additional material in future editions.

There are online resources to accompany this book. Online resources (password protected) are available to professors who adopt the book and to the students. Professors' resources include PowerPoint lectures, solutions to chapter end exercises, prototype course syllabus, Excel templates, and health care data sets with data dictionary that can be used for various exercises. Using these data sets, instructors can create additional exercises as appropriate. Additionally, select experiential projects using the methods covered in this book are provided. These resources can be accessed via www.josseybass.com/go/ozcan3e.

Chapter-by-Chapter Revisions for the Third Edition

In General

All errata from the previous edition have been corrected.

All Excel screen shots have been updated to the Excel 2016 version.

As some new materials have been added in various chapters, “Learning Objectives” and “Key Terms” have been revised to incorporate the new content.

References have been updated according to new content, and citations have been updated to the most recent available references.

New exercises have been added to each chapter; those chapters that did not have or had very few exercise sets now contain exercises. Where appropriate, exercises incorporate the use of external data sources such as Hospital Compare and the National Cancer Institute's Geoviewer application.

To reflect the use of big data as well as to include additional analytic skill sets, new supplements have been created with illustrative explanations. These are available at www.josseybass.com/go/ozcan3e.

Specific Changes

Chapter 1: The title of this chapter has been changed from “Introduction to Quantitative Decision-Making Methods in Health Care Management” to “Introduction to Analytics and Decision Support in Health Care Operations Management.” Writing in this chapter has been updated to emphasize big data and a population health focus. Health analytics and data flow in health care organizations with Electronic Health Records (EHR), RFID technology, and the impact of health reforms including the ACA (Affordable Care Act) and Accountable Care ­Organizations (ACOs) on quality outcome-driven focus are also emphasized. Various time-dependent tables were removed and have been replaced with simpler statistics.

Finally, a supplement in Data Analytics entitled “Creating and Manipulating Pivot ­Tables” for large enterprise system data was added. This is an illustrative example showing how to create pivot tables that can be used not only in predictive analytics in Chapter 2, but also in other chapters. The data for this supplement will be available in Excel format to users.

This chapter now has seven new exercises involving big data manipulations and analytics as well as accessing external data sets.

Chapter 2: The title of this chapter has been changed from “Forecasting” to “Predictive Analytics.” Some materials have been moved around for better readability, a new multiple regression section has been added, and the section explaining the indices technique has been updated with a new example.

Previously, this chapter had only 12 exercises. The third edition contains 30 exercises and as the number has increased, the sophistication of the exercises also has increased to a case study level.

Chapter 3: The title of this chapter slightly changed, with the word “facilities” dropped from the title to reflect the additional decision-making content. A new example to illustrate decision making based on cost information has been added. A new section on sensitivity analysis with an example also has been added. Finally, a new section that applies the decisions in clinical settings with cost effectiveness analysis has been added, including an example.

Previously, this chapter had only 12 exercises. The third edition contains 28 exercises and as the number has increased, the sophistication of the exercises also has increased to a case study level.

Chapter 4: Updates in this chapter include the use of Google Maps for “Center of Gravity Method.” This section has been completely revised based on this commonly available technology. Also new in this chapter is the expanded section of “Geographic Information Systems (GIS) in Health Care,” with an added example.

Previously, this chapter had only six exercises. The third edition contains 15 exercises and as the number has increased, the sophistication of the exercises also has increased to a case study level.

Chapter 5: An updated Excel template and clarification on use has been added.

Previously, this chapter had only six exercises. The third edition contains 14 exercises and as the number has increased, the sophistication of the exercises also has increased to a case study level.

Chapter 6: The title of this chapter changed from “Reengineering” to “Flow Processes Improvement: Reengineering and Lean Management” to reflect why these methods are being used. A section on Lean in health care has been added in the text in the early stages of the discussion. In work sampling content, manual lookup using a random number table and its example has been removed and emphasis has been instead placed on Excel-generated random work sampling scheduling. The work simplification section has been enhanced through the addition of tools such as the “Value Stream Map” and the “Spaghetti Diagram,” with examples.

Previously, this chapter had only 12 exercises. The third edition contains 22 exercises and as the number has increased, the sophistication of the exercises also has increased to a case study level.

Chapter 7: More clarity has been added by using subheadings such as “Procedural-Based Unit Staffing” and “Acuity-Based Unit Staffing.” The text has been streamlined for better flow. Unit measures in formulas have been converted to hours (rather than minutes) to eliminate an extra step; examples and exercises also have been aligned accordingly.

Previously, this chapter had only seven exercises. The third edition contains 14 exercises and as the number has increased, the sophistication of the exercises also has increased to a case study level.

Chapter 8: The computerized scheduling section has been updated with new industry information.

Previously, this chapter had only three exercises. The third edition contains 8 exercises.

Chapter 9: The title of this chapter has been changed from “Productivity” to “Productivity and Performance Benchmarking.” The introductory section was expanded to include discussion of the Affordable Care Act (ACA) and its demands from the providers. A new section has been added to discuss the current trends in health care productivity, and the consequences of reforms and policy decisions. A discussion of bar coding and RFID technology, used to monitor and improve productivity, also has been added.

Finally, a chapter-end supplement in accessing external data and a benchmarking example have been added.

Previously, this chapter had only seven exercises. The third edition contains 14 exercises.

Chapter 10: Previously, this chapter had only six exercises. The third edition contains 11 exercises.

Chapter 11: The supply chain section is updated to include the hospital materials management systems and their electronic connections to suppliers or distributors. Based on mergers and divestitures information, supplier and distributor company information is updated. In the traditional inventory management section, the UPC and bar coding discussion is updated. Additionally, the inventory classification system discussion has been moved down further in the chapter for better clarity and flow.

Previously, this chapter had only six exercises. The third edition contains 12 exercises.

Chapter 12: The title of this chapter changed from “Quality Control” to “Quality Control and Improvement.” In describing the process measurement charts, a new figure was added to show taxonomy of the control charts. To add further clarity on measurement metrics, they have been classified based on whether they are driven from count or continuous measurement metrics.

A supplement “Creating a Pareto Diagram” using Excel with an example has been created and is available at www.josseybass.com/go/ozcan3e.

Previously, this chapter had 15 exercises. The third edition contains 26 exercises.

Chapter 13: The project compression part of this chapter has been reorganized to reflect two approaches to project length reduction. The existing approach, with an example, is named “Project Compression with Total Cost Approach.” A new section has been added with the title “Project Compression using Incentive Approach” and is illustrated with an example.

A totally new section entitled “Project Management Applications in Clinical Settings: Clinical Pathways” has been added and discusses how this tool could be used in patient management, along with an example.

Previously, this chapter had 15 exercises. The third edition contains 23 exercises.

Chapter 14: Various enhancements to the text have been made and clarifications have been added.

Previously, this chapter had seven exercises. The third edition contains 13 exercises.

Chapter 15: A description of several simulation methodologies (Discrete, Monte Carlo, Agent Based, and Simulation Optimization) has been added into the discussion. A single and multiphase health care operations, Excel-based simulation template with performance measures and managerial decisions also has been added to this chapter with examples. The template also provides an animated icon-based simulation to enhance learning.

Previously, this chapter had seven exercises. The third edition contains ten restructured exercises.

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

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.

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 workers, (3) cooperation between management and employees, and (4) responsibility shared equally between workers and management (Taylor, 1911). Other early contributors to scientific methods of management were Frank and Lillian Gilbreth, who worked on standardization, and Henry Gantt, who emphasized the psychological effects that work conditions have on employees—he developed a time-based display chart to schedule work. Quantitative inventory management was developed by F. W. Harris in 1915. In the 1930s, W. Shewhart and associates developed statistical sampling techniques for quality control (Stevenson, 2015, pp. 23–24). World War II prompted the growth of operations research methods, and development of project management techniques; linear programming and queuing methods followed in the 1950s. After the 1970s, the development and wider use of computers and management information systems (MIS) reshaped all these techniques because large amounts of data could be analyzed for decision making in organizations. Tools for quality improvement such as total quality management (TQM) and continuous quality improvement (CQI) became very popular in the 1980s and 1990s; then came supply chain management and productivity improvement techniques, in particular reengineering and lean management.

The Health Care Manager and Decision Making

A health care manager can be a chief executive officer (CEO) or chief operating officer (COO), or a middle-level manager to whom the duties are delegated. At the top management level, a health care manager's responsibilities include planning for capacity, location, services to be offered, and facility layout; those responsibilities are strategic. The health care manager also is ultimately responsible for overseeing service production through supply chain management, quality monitoring and improvement, and organizing health services to be either produced or outsourced. Finally, the health care manager is responsible for patient and personnel scheduling, and for optimally staffing the facility and directing job assignments and work orders. Regardless of whether health care managers are directly involved or delegate these responsibilities, their ultimate responsibility remains. Generally, operational decisions are delegated to midlevel and lower-level decision makers, while strategic decisions are evaluated at the organization's top levels. With the integrated delivery systems (IDS) movement, health care organizations are becoming larger and more complex, so health care managers are in dire need of the most recent, reliable information derived from quantitative data analysis in order to make informed decisions. Information technology (IT) has become integral to management decision processes.

Importance of Health Analytics: Information Technology (IT) and Decision Support Techniques

If they are to analyze their current situations and make appropriate changes to improve efficiency as well as the quality of care, health care managers need appropriate data. The data, from various sources, are collected by information technology (IT) embedded in systems either internal or external to the health care organization. For example, decisions about the location of a new health facility will require analysis of data on the communities under consideration (such as census, epidemiological data, and so on). Decisions about nurse staffing will require internal data on patient admissions and acuity that are collected routinely by the hospital. Later in this chapter under the heading of “Big Data and Data Flow in Health Care Organizations,” this book identifies the sources of the data for various decision-making techniques and emphasizes the use of IT for informed decision support by health care managers. Furthermore, a supplemental data example using Excel pivot tables is presented at the end of the chapter.

The Scope of Health Care Services, and Recent Trends

According to the Organization for Economic Cooperation and Development (OECD) countries, their members' total expenditures on health services constituted 5.1 to 16.4 percent of gross domestic product (GDP) in 2013, making health services a very significant sector from a public policy perspective. Moreover, the statistics in Table 1.1 show an increasing trend in health care expenditures. The countries that spent an average of about 8 percent of their budgets on health care in the mid-2000s are now spending 12.5 percent more. The United States is the country spending the highest percentage of GDP on health care. However, its percentage share of GDP was stabilized from 2009 to 2013.

Health care, especially in the United States, is a labor-intensive industry with more than 19 million jobs and growing in 2016 (U.S. Department of Labor, 2016). The aging population—as well as the proliferation of medical technology and new treatments—contributes to this growth.

The health care industry seeks to match varying medical needs in the population. Its over half a million establishments vary in size, complexity, and organizational structure, ranging from small-town, private practice physicians with one medical assistant to urban hospitals that employ thousands of diverse health care professionals. Less than about 2 percent of health care establishments are hospitals, but they employ over one-third of all health care workers.

Advances in medical technologies, new procedures and methods of diagnosis and treatment, less invasive surgical techniques, gene therapy—all of these increase longevity and improve the quality of life. Similarly, advances in information technology can improve patient care. For example, handheld order-entry systems such as personal digital assistants (PDAs), radio frequency identification (RFID), and bar code scanners at bedside make health workers more efficient, and also minimize errors and thus improve the quality of care.

These advances usually add to costs, so cost containment is a major goal in the health care industry. To accomplish it, the health care industry has shifted the care of patients from hospital care to outpatient, ambulatory, and home health care. At the same time, managed care programs have stressed preventive care to reduce the eventual costs of undiagnosed, untreated medical conditions. Enrollment has grown in prepaid managed care programs: health maintenance organizations (HMOs), preferred provider organizations (PPOs), and point-of-service (POS) programs.

Table 1.1 Total Expenditures on Health as Percentage of GDP for 37 OECD Countries.

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Average

8.0

8.1

8.0

8.0

8.3

9.0

8.8

8.8

8.9

9.0

Minimum

4.7

5.0

4.9

5.0

5.5

5.8

5.3

5.0

5.0

5.1

Maximum

14.6

14.6

14.7

14.9

15.3

16.4

16.4

16.4

16.4

16.4

Source:

OECD Health Data 2016

The health care industry has turned to restructuring to improve financial and cost performance. Restructuring is accomplished by achieving an integrated delivery system (IDS). An IDS merges the segments of health care delivery, both vertically and horizontally, to increase efficiency by streamlining financial, managerial, and delivery functions.

The Patient Protection and Affordable Care Act, commonly referred to as the Affordable Care Act (ACA), brought significant reform to the health care industry, introducing various programs aimed at improving quality and controlling the rising costs of health care. Many of these programs offer financial incentives for providers to meet certain quality and efficiency benchmarks. The ACA also introduced accountable care organizations (ACOs) as a tool to control costs through improved coordination of care and increased emphasis on preventive care, continuing the shift toward rewarding providers for better outcomes rather than volume.

It is fair to conclude that the changes in the health care industry will continue and will affect the delivery of health services in terms of cost and efficiency as well as the quality of care.

Health Care Services Management

Given such complexity in both the nature and the environment of health care, managers of such establishments face challenging day-to-day decisions as well as long-term and strategic ones. Their discipline, the management and improvement of the systems and processes that provide health care, must rely on decision tools—namely, the specific methods that can help managers analyze, design, and implement organizational changes to achieve efficiency as well as high quality of care (effectiveness) for patients.

Clearly, then, management of health care establishments requires reasoned inquiry and judgment. Therefore, health care managers must use proven scientific methods drawn from such disciplines as industrial engineering, statistics, operations research, and management science. However, it must be remembered that such quantitative tools do not, alone, shape the final decision, which may have to include other, qualitative factors to arrive at the right course of action.