Healthcare Analytics for Quality and Performance Improvement PDF

Healthcare Analytics for Quality and Performance Improvement Healthcare Analytics for Quality and Performance Improvement TREVOR L. STROME Cover image: © iStockphoto.com/pictafolio Cover design: Andrew Liefer Copyright © 2013 by Trevor L. Strome. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com. 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Library of Congress Cataloging-in-Publication Data: Strome, Trevor L., 1972– Healthcare analytics for quality and performance improvement / Trevor L. Strome. pages cm ISBN 978-1-118-51969-1 (cloth) — ISBN 978-1-118-76017-8 (ePDF) — ISBN 978-1-118-76015-4 (ePub) — ISBN 978-1-118-761946-1 (oBook). 1. Health services administration—Data processing. 2. Information storage and retrieval systems—Medical care. 3. Organizational effectiveness. I. Title. RA971.6.S77 2014 362.1068—dc23 2013023363 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 Dedicated to Karen, Isabella, and Hudson—for all your support, understanding, and love Contents Preface ix Acknolwedgments xiii CHAPTER 1 Toward Healthcare Improvement Using Analytics 1 Healthcare Transformation—Challenges and Opportunities 1 The Current State of Healthcare Costs and Quality 3 CHAPTER 2 Fundamentals of Healthcare Analytics 15 How Analytics Can Improve Decision Making 15 Analytics, Quality, and Performance 17 Applications of Healthcare Analytics 19 Components of Healthcare Analytics 21 CHAPTER 3 Developing an Analytics Strategy to Drive Change 29 Purpose of an Analytics Strategy 29 Analytics Strategy Framework, with a Focus on Quality/Performance Improvement 32 Developing an Analytics Strategy 47 CHAPTER 4 Defining Healthcare Quality and Value 51 What Is Quality? 51 Overview of Healthcare QI 59 Common QI Frameworks in Healthcare 61 Working with QI Methodologies 73 CHAPTER 5 Data Quality and Governance 75 The Need for Effective Data Management 76 Data Quality 78 Data Governance and Management 84 Enterprise-wide Visiblilty and Opportunity 88 vii viii Contents CHAPTER 6 Working with Data 91 Data: The Raw Material of Analytics 92 Preparing Data for Analytics 92 Getting Started with Analyzing Data 100 Summary 112 CHAPTER 7 Developing and Using Effective Indicators 115 Measures, Metrics, and Indicators 115 Using Indicators to Guide Healthcare Improvement Activities 123 CHAPTER 8 Leveraging Analytics in Quality Improvement Activities 129 Moving from Analytics Insight to Healthcare Improvement 129 CHAPTER 9 Basic Statistical Methods and Control Chart Principles 145 Statistical Methods for Detecting Changes in Quality or Performance 145 Graphical Methods for Detecting Changes in Quality or Performance 153 Putting It Together 160 CHAPTER 10 Usability and Presentation of Information 165 Presentation and Visualization of Information 165 Dashboards for Quality and Performance Improvement 173 Providing Accessibility to and Ensuring Usability of Analytics Systems 180 CHAPTER 11 Advanced Analytics in Healthcare 183 Overview of Advanced Analytics 183 Applications of Advanced Analytics 186 Developing and Testing Advanced Analytics 190 Overview of Predictive Algorithms 197 CHAPTER 12 Becoming an Analytical Healthcare Organization 205 Requirements to Become an Analytical Organization 207 Building Effective Analytical Teams 213 Summary 215 About the Author 217 About the Companion Web Site 219 Index 221 Preface Why write a book on healthcare analytics that focuses on quality and per- formance improvement? Why not focus instead on how healthcare informa- tion technology (HIT) and “big data” are revolutionizing healthcare, how quality improvement (QI) methodologies such as Lean and Six Sigma are transforming poorly performing healthcare organizations (HCOs) into best- in-class facilities, or how leadership and vision are the necessary driving factors behind innovation and excellence within HCOs? The truth is, this book is about all these things. Or, more accurately, this book is about how healthcare organizations need to capitalize on HIT, data from source systems, proven QI methodologies, and a spirit of innovation to achieve the transformation they require. All of these factors are necessary to achieve quality and performance improvement within modern healthcare organizations. However, the professionals working in healthcare IT, qual- ity improvement, management, and on the front lines all speak different languages and see the world from different perspectives—technology, data, leadership, and QI. This gap (a chasm, really) prevents these professionals from effectively working together and limits their capability to perform effective quality and performance improvement activities. This may in fact be lowering the quality of care and decreasing patient safety at a time when doing the opposite is critical. This book demonstrates how the clinical, business, quality improve- ment, and technology professionals within HCOs can and must collaborate. After all, these diverse professional groups within healthcare are work- ing together to achieve the same goal: safe, effective, and efficient patient care. Successful quality improvement requires collaboration between these different stakeholders and professional groups; this book provides the common ground of shared knowledge and resources necessary for QI, IT, leadership, and clinical staff to become better coordinated, more integrated, and to work together more effectively to leverage analytics for healthcare transformation. ix x Preface In this book, I hope to demonstrate that analytics, above all, can and must be made accessible throughout the entire HCO in order for the insight and information possible through analytics to actually get used where it is needed. I attempt to dispel the myth that only a select few can be qualified to be working with the data of an HCO. Although the process of generat- ing insight through analytics requires some statistics and mathematics, the output or result of analytics must make intuitive sense to all members of the healthcare team. In my experience, if the information and insight produced by business intelligence and analytics is too complex to understand for all but the team that generated it, then that information will contribute very little to healthcare improvement. In keeping with the theme of accessibility, I have attempted to keep this book very accessible to readers with various backgrounds and experi- ence. The book covers a wide range of topics spanning the information value chain, from information creation and management through to analy- sis, sharing, and use. As such, it cannot cover each of the topics completely and in depth. But it does cover the areas that I believe are vital in a quality improvement environment driven by analytics. If you work in the area of health IT, data management, or QI, I have attempted to connect the dots in how your professional discipline fits in with the others. I hope that this book can thereby enable technical, analytical, QI, executive, and clinical members of the healthcare team to communicate clearly, better understand one another’s needs, and jointly collaborate to improve the efficiency, effec- tiveness, and quality of healthcare. I do admit my bias toward the acute-care setting, and emergency departments in particular. The vast majority of my career has been within acute care and emergency, and the writing and examples in this book defi- nitely reflect that bias—although I have tried not to make every example an emergency department example! The basic concepts of quality, value, performance, and analytics will translate well to almost any setting, whether it is medicine, surgery, home care, or primary care. In my opinion, the real value of analytics occurs when the insight gen- erated through analytical tools and techniques can be used directly by qual- ity improvement teams, frontline staff, and other healthcare professionals to improve the quality and efficiency of patient care. To some, this may not be the most glamorous application of analytics, but it is the most important. Book Overview After a discussion of the escalating inefficiencies and costs of healthcare (Chapter 1), a high-level overview of the various components of an effec- tive analytics system within an HCO is covered in Chapter 2. Because of the Preface xi need for strong alignment between the quality and process improvement goals of the organization, the various demands facing healthcare IT depart- ments, and the balancing that analytics must do between these competing interests, Chapter 3 provides an overview of an effective analytics strategy framework that HCOs can use to keep their focus on efforts that achieve the desired improvement results of the organization. Chapter 4 is an overview of the concepts of quality and value, and how these are measured within an HCO. Three quality improvement methodologies (PDSA, Lean, and Six Sigma) are discussed in Chapter 4 as well, and how analytics can provide support to these various types of initiatives. Chapters 5, 6, and 7 focus on data. Chapter 5 is an overview of data quality and data management, and how to ensure that analytics profession- als and stakeholders have access to the high-quality data they need in order to provide information and insight to the organization. Chapter 6 discusses the different types of data, important methods of summarizing and under- standing data, and how data type affects the kind of analysis that is possible. Chapter 7 provides tips on how to convert data into metrics and indicators that provide the HCO with a much clearer lens through which to monitor and evaluate performance and quality. Chapter 8 is about how to meld analytics and quality improvement activities so that QI teams can benefit from the insight and information available throughout all phases of QI projects, regardless of the QI method- ology that is chosen. Chapter 9 highlights several of the key statistical and graphical methods for monitoring performance and detecting when in fact a true change in performance or quality has occurred. Chapter 10 talks about usability of analytics from an access and presentation point of view. The advanced analytics discussed in Chapter 11 includes tools such as regres- sion and machine-learning approaches that can be used to identify patterns in healthcare data and predict likely outcomes. Finally, Chapter 12 discusses achieving analytics excellence within an HCO, including the types of leadership and management required within an HCO to ensure that data and privacy are held secure and that analytics is used appropriately and to its maximum effectiveness. Acknowledgments It is impossible to write a book of this scope without tremendous amounts of support and encouragement. I am lucky to be surrounded by people who have been incredibly encouraging and supportive throughout this journey. First and foremost, I would like to thank my wife and my two wonder- ful children for your unconditional love and support, and for your inspira- tion and undying encouragement during the writing of this book. I love you more than you can ever know! I would like to thank my friends and colleagues at the Winnipeg Region- al Health Authority (WRHA) Emergency Program, within other WRHA departments and programs, and in the Department of Emergency Medicine, University of Manitoba. The support, guidance, and feedback you’ve given me during the writing process were absolutely instrumental in helping me complete this work. I have gained tremendously by working on frontline quality improvement projects with many of the hardest-working and most dedicated clinical personnel in healthcare. To everyone from whom I’ve drawn the examples and case studies in this book, it is from your experi- ence, efforts, and desire to improve healthcare that I gain confidence that healthcare transformation is truly possible. I would like to thank Karen Strome, Lori Mitchell, and Ryan McCormack, who provided invaluable assistance by reviewing and commenting on sev- eral of the key chapters in this book. Your advice and feedback have made this a much better book than would have been possible on my own. I would also like to thank Laura Madsen, preeminent healthcare busi- ness intelligence expert and author of Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics, for inspiring me to write this book and for kindly introducing me to her publisher, John Wiley & Sons. xiii CHAPTER 1 Toward Healthcare Improvement Using Analytics Innovation is anything but business as usual. —Anonymous How sustainable is healthcare in its current state? Most healthcare organiza- tions (HCOs) claim to be undertaking quality improvement (QI) initiatives, but only a few are consistently improving the quality of healthcare in a sustainable fashion. Despite increased spending on healthcare in the United States, there is little evidence that the quality of healthcare can be improved by increasing spending alone. Health information systems is one technology with the potential to transform healthcare because, among its many capabil- ities, it can deliver the best evidence to the point of care, employs intelligent algorithms to reduce and prevent medical mistakes, and collects detailed information about every patient encounter. Even with growing volumes of data to analyze resulting from the continuing proliferation of computer sys- tems, HCOs are struggling to become or remain competitive, highly func- tioning enterprises. This chapter will highlight current challenges and pres- sures facing the healthcare system, identify opportunities for transformation, and discuss the important role that analytics has in driving innovation and achieving healthcare transformation goals. Healthcare Transformation—Challenges and Opportunities Healthcare delivery is undergoing a radical transformation. This is occur- ring as the result of both necessity and opportunity. Change is necessary 1 2 Toward Healthcare Improvement Using Analytics because, in many ways, the provision of healthcare is less efficient, less safe, and less sustainable than in the past. The opportunity, however, arises from the advancement of technology and its impact on healthcare delivery. Technology now allows increasingly intelligent medical devices and information systems to aid in clinical decision making, healthcare management, and administration. The challenge facing HCOs is to lever- age advances in both clinical device technology and information tech- nology (IT) to create and sustain improvements in quality, performance, safety, and efficiency. Data generated via healthcare information technology (HIT) can help organizations gain significantly deeper insight into their performance than previous technologies (or lack of technology) allowed. HCOs, however, face the very real risk of information overload as nearly every aspect of healthcare becomes in some way computerized and subsequently data- generating. For example, radio frequency identification (RFID) devices can report the location of every patient, staff member, and piece of equipment within a facility; sampled every second, the location data captured from these devices accumulates quickly. Portable diagnostic equipment now cap- tures and stores important patient clinical data, such as vital signs, and can forward that data to electronic medical records (EMRs) or other computer- ized data stores. Similarly, devices with embedded “labs on a chip” can now perform point-of-care testing for many blood-detectable diseases, and generate enormous volumes of data while doing so. HCOs must find a way to harness the data at their disposal and take advantage of it to improve clinical and organizational performance. Data analytics is critical to gaining knowledge, insight, and actionable infor- mation from these organizations’ health data repositories. Analytics con- sists of the tools and techniques to explore, analyze, and extract value and insight from healthcare data. Without analytics, the information and insight potentially contained within HCOs’ databases would be exceed- ingly difficult to obtain, share, and apply. But insight without action does not lead to change; data overload can risk impeding, not improving, the decision-making ability of healthcare leaders, managers, and QI teams. In my experience, the true potential of analytics is realized only when analytics tools and techniques are combined with and integrated into a rigorous, structured QI framework. This power- ful combination helps to maintain the focus of QI and management teams on achieving the quality and business goals of an organization. Analytics can also be used to explore the available data and possibly identify new opportunities for improvement or suggest innovative ways to address old challenges. When an HCO uses analytics to focus improvement efforts on existing goals and to identify new improvement opportunities, healthcare can become more effective, efficient, safe, and sustainable. The Current State of Healthcare Costs and Quality 3 The Current State of Healthcare Costs and Quality A discussion on the topic of healthcare analytics must first begin with a discus- sion of healthcare quality. This is because analytics in healthcare exists for the purpose of improving the safety, efficiency, and effectiveness of healthcare deliv- ery. Looking at the current and emerging challenges facing healthcare the way we looked at problems in the past can and will only result in more of the same. And it seems that many people, from healthcare providers who are overworked to patients who must endure unacceptably long waiting lists for relatively com- mon procedures, are extremely dissatisfied with the way things are now. Despite the seemingly miraculous capabilities of the healthcare system to maintain the health of, and in many cases save the lives of, patients, the sys- tem itself is far from infallible. The question of how safe is healthcare delivery must continually be asked. The often-cited Institute of Medicine (IoM) report To Err Is Human: Building a Safer Health System declares that a “substantial body of evidence points to medical errors as a leading cause of death and injury.”1 The report cites two studies that estimate between 44,000 and 98,000 patients die every year in hospitals because of medical errors that could have been prevented. These are people who expected the healthcare system to make them well again or keep them healthy and were horribly let down. According to the IoM report, the types of errors that commonly occur in hospitals include “adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities.” Not surprisingly, emergency departments, operating rooms, and intensive care units experi- ence the highest error rates and those with the most serious consequences. Not only do hospital errors result in a staggering yet largely prevent- able human toll, but they result in a tremendous financial burden as well. It is estimated that the cost to society of these preventable errors ranges between $17 billion and $29 billon in both direct and indirect financial costs. Of course, the majority of these errors are not caused by deliberate malpractice, recklessness, or negligence on the part of healthcare providers. Rather, according to the IoM report, the most common causes of healthcare errors are “due to the convergence of multiple contributing factors” and that “the problem is the system needs to be made safer.”2 In the near decade and a half that has passed since the release of the 1999 Institute of Medicine report, most of its findings are as relevant today as they were in 1999. Despite dramatic innovations in biomedicine and healthcare technology since the IoM report, many HCOs today still find themselves under immense pressures, some of which include: Improving quality and patient safety Ensuring patient satisfaction 4 Toward Healthcare Improvement Using Analytics Adapting to changes in legislation and regulations Adopting new technologies Demonstrating improved patient outcomes Remaining sustainable and competitive The challenge facing HCOs today is to balance the need to innovate by adopting new technologies and improving processes while providing the essentials of safe, efficient, and effective patient care. While these two needs are complementary, with improved patient care as the ultimate goal, they both require financial, human, and technical resources that are drawn from a limited, and in some cases shrinking, resource pool. The Cost of Healthcare HCOs must endeavor to reduce unnecessary deaths, injuries, and other hardships related to medical errors and other issues stemming from sub- standard quality. But given that the cost of healthcare delivery seems to be increasing unabatedly, could healthcare be at risk of becoming unsustain- able in its current form? Direct and indirect costs attributed to healthcare represent a significant and increasing burden on the economies of coun- tries providing modern healthcare, and may not be sustainable at current growth rates. Figure 1.1 illustrates the immense cost of healthcare by showing the percentage of healthcare expenditures as a proportion of the gross domestic product (GDP) of selected countries.3 Of the countries in Fig- ure 1.1, total health expenditure as a share of GDP ranges from 2.4 percent (Indonesia) to 17.4 percent (United States). Of significance is that healthcare expenditures in the United States totaled over 17 percent of its GDP—5 percent more than the next highest country, and almost 8 percent more than the OECD average of 9.6 percent. But not only have expenditures on healthcare increased in the United States from approximately 5 percent of GDP in 1960 to over 15 percent in 2008, they are expected to grow still further, reaching approximately 20 percent of GDP by 2018. Andy Grove, former chief operating office and chief executive officer of Intel Corporation and a pioneer in the semiconductor industry, once stated, “There is at least one point in the history of any company when you have to change dramatically to rise to the next level of performance. Miss that moment—and you start to decline.” Given the numerous pres- sures and escalating costs facing the healthcare systems of many nations, now is the time for HCOs to innovate using available tools and technolo- gies to transform into more sustainable, efficient, effective, and safe pro- viders of care. The Current State of Healthcare Costs and Quality 5 Public Private % of GDP 20 17.4 18 16 14 12.0 11.8 11.6 11.5 11.4 11.4 11.0 12 10.9 10.3 10.0 10.1 9.8 9.5 9.7 9.6 9.6 9.6 9.5 9.5 9.3 10 9.1 9.2 9.0 8.7 8.5 8.5 8.4 8.2 7.9 7.8 7.4 7.4 8 7.0 6.9 6.4 6.1 5.4 6 4.6 4.2 4 2.4 2 0 Slovak Rep. Czech Rep. Russian Fed. Brazil United States Netherlands1 France Germany Denmark Canada Switzerland Austria Belgium2 New Zeland Portugal Sweden United Kingdom Iceland Greece Norway OECD Ireland Italy Spain Slovenia Finland Australia Japan South Africa Chile Luxembourg3 Hungary Poland Estonia Korea Mexico Turkey China India Indonesia Israel FIGURE 1.1 Total Healthcare Expenditures for Selected Countries as a Share of Gross Domestic Product (2009) 1. In the Netherlands, it is not possible to clearly distinguish the public and private share related to investments. 2. Total expenditure excluding investments. 3. Health expenditure is for the insured population rather than the resident population. Source: OECD Health Data 2011; WHO Global Health Expenditure Database. The Analytics Opportunity in Healthcare The good news is that HCOs can take the necessary action to improve qual- ity of care, increase value to patients, and raise the bottom line. Advances in HIT, and particularly the field of healthcare analytics, are now helping HCOs to reveal and act on opportunities for transformative improvement. The term “analytics” has been described in myriad ways. For the pur- poses of this book, I will refer to analytics as the systems, tools, and tech- niques that help HCOs gain insight into current performance, and guide future actions, by discerning patterns and relationships in data and using that understanding to guide decision making. Analytics enables leaders, managers, and QI teams within HCOs to make better decisions and take more appropriate actions by providing the right information to the right people, at the right time, in the right format, with the right technology. 6 Toward Healthcare Improvement Using Analytics Healthcare Analytics Healthcare analytics consists of the systems, tools, and techniques that help HCOs gain insight into current performance, and guide future ac- tions, by discerning patterns and relationships in data and using that un- derstanding to guide decision making. One doesn’t need to look far to observe the impact that analytics has had on other industries. Companies such as Google, Amazon, and others whose very existence depends on users’ ease of access to highly targeted, tailored, and user-friendly information demonstrate the realm of the pos- sible—that the tools, techniques, algorithms, and data now exist to drive our analytics-powered world. The use of analytics in healthcare, however, has lagged behind other industries. Internet search engines make it incredibly easy to enter a search term and almost immediately retrieve a list of web pages that contain infor- mation pertaining to the search term ranked in order of relevance and likely usefulness. Yet anyone who has used an EMR or a reporting tool to look up information on a patient, or a group of patients, knows how difficult finding the necessary information can be. And anybody who has tried to get the information they need for a healthcare quality and/or performance improvement project would not be faulted for thinking that obtaining any information of value is downright impossible. WHY QUALITY IMPROVEMENT PROJECTS FAIL HCOs are always working to improve the quality of their care and the efficiency of their business opera- tions. Many HCOs do not see much improvement in quality and perfor- mance despite engaging in multiple improvement initiatives. Unfortunately, some HCOs will undertake QI projects without an overall quality strategy or long-term evaluation plan and end up with many disconnected, half- evaluated projects that never seem to achieve their objectives. Some HCOs focus on improving quality in bursts, with intense activity and enthusiasm that lasts only for a short period of time. Such torrents of QI activity is usually in reaction to some negative event such as a critical incident, or after a “eureka” moment occurs in which an executive member learns something new at a conference, after seeing a product demonstra- tion, or while speaking with a consultant. Once the initial excitement wears off the initiative, the unit, department, program, facility, or entire enterprise may revert back to its initial or some other suboptimal state if a solid quality framework and sustainability plan are not in place. Even HCOs with QI entrenched in their organizational culture, a proven track record, and well-evolved QI frameworks in place rarely achieve total The Current State of Healthcare Costs and Quality 7 success and must revisit areas of improvement (often multiple times) to help ensure that improvement results are maintained. This is because achieving change within HCOs is difficult and, much like breaking a bad habit, rarely is sustained after the first try. Health care is the most difficult, chaotic, and complex industry to manage today [and the hospital is] altogether the most complex human organization ever devised. —Peter Drucker Making changes to an HCO is difficult because healthcare is a very dynamic environment and in a constant state of flux. Innovations in health- care technology are ushering in changes at a rapid pace, emerging diseases and changing patient demographics are presenting new treatment challeng- es to clinical staff, and organizations themselves face an ongoing barrage of new regulations and changes to funding models. What might have been an effective and/or necessary process, workflow, or policy 20 years ago (or even two years ago) may be no longer relevant, or in need of major updating to be made relevant once again. HCOs must evolve and adapt not merely to maintain and improve qual- ity, performance, and patient safety, but to survive. Of course, the standard principles of providing safe, efficient, and effective patient care will never change—but exactly how that is done must always evolve. LEVERAGING INFORMATION TECHNOLOGY Although HIT is one of the largest drivers of healthcare innovation (or disruption, as some health- care providers would claim), HIT provides the tools required to monitor, evaluate, and improve healthcare quickly and with clarity. In fact, improv- ing quality in a modern HCO to the extent and at the pace necessary without the benefit of the information derived from HIT would be an onerous task. A NOTE ON TERMINOLOGY I will use the term “healthcare information technology” (HIT) when referring to systems that are mainly clinical in nature such as electronic medical record (EMR), radiology information system (RIS), and other similar systems. I will use the term “information technology” (IT) more generically to include both clinical and nonclinical systems (such as financial, supply chain management, and other such tools). 8 Toward Healthcare Improvement Using Analytics Despite what some vendors may promise, it takes more than simply adopting HIT to improve quality and performance within an HCO. In fact, it is ironic that a mere decade ago many healthcare improvement efforts were likely stymied due to lack of data. Now it is entirely possible that improve- ment efforts could be hindered by having too much data available without the necessary experience and tools to analyze it and put it to good use. This is not to say that healthcare improvement cannot occur without the use of IT, but at some point every HCO must use data to monitor and evaluate ongoing changes and fine-tune improvements. I have seen medio- cre HCOs become top performers as a result of the intelligent use of infor- mation in combination with strong leadership, a clear vision, a culture of innovation, and a drive to succeed. Although technology is never the only solution, analytics consists of many tools, technologies, and techniques that HCOs can employ to leverage the data amassed from the increasing number of HIT systems in operation. These innovations in combination with competent, effective leadership enable HCOs to become more effi- cient and adept at achieving, evaluating, and sustaining improvements in healthcare. THE ANALYTICS KNOWLEDGE GAP In pursuit of clinical and operational excellence, HCOs are drawing from diverse, nontraditional professions (from a healthcare perspective) to form QI and innovation teams. In addi- tion to nurses, physicians, and administrators, it is not uncommon to see engineers, computer scientists, and other specialist roles working within healthcare. Although having traditional and nontraditional roles working side by side to solve the many problems facing healthcare brings incredible diversity and flexibility, this arrangement also poses some challenges. Successful healthcare quality and performance improvement initiatives require strong executive sponsorship and support, QI expertise, subject matter expertise, and information management and analysis expertise. Bringing these various disciplines together provides diversity that can lead to the synergistic development of innovations but also exposes significant knowledge gaps between these groups. (See Figure 1.2 for an illustration of this knowledge gap.) Each professional group brings with it its own particular skill sets, knowledge, and comfort levels working with data and analytics. The ana- lytics knowledge gap may make it seem like nobody is speaking the same language, which can prevent teams from working effectively and cohesively together. To reduce friction and misunderstanding on healthcare quality and leadership teams, it is necessary to bridge the knowledge gap. Bridging the gap enables team members to communicate more effectively, to ask the right questions, and to frame the answers and insights in ways that make sense and are relevant to the improvement challenges at hand. The Current State of Healthcare Costs and Quality 9 Healthcare Management & Leadership Information Gap Quality Information Improvement Technology FIGURE 1.2 The Analytics Information Gap between QI, IT, and Healthcare Leadership Leveraging Information for Healthcare Improvement As HCOs turn to technological solutions to manage business operations and treat patients, many are literally becoming awash in data. In fact, some estimates are that healthcare data in the United States alone totaled approximately 150 exabytes (150 × 1018 bytes) in 2011 for clinical, finan- cial, and administration systems; of course, this number will only con- tinue to grow. In fact, a single large American healthcare provider alone is estimated to have accumulated up to 44 petabytes (a petabyte is 1015 bytes) of patient data from electronic health record data (including images and annotations).4 As HCOs continue to amass large quantities of data, that data is only of any value if it gets used. Many HCOs are becoming more “data centered,” in “BIG DATA” IS A RELATIVE TERM Although “big data” is a term commonly used to describe the very large data sets of today, there is no doubt that the anticipated future growth in healthcare data will make today’s “big data” seem minuscule. I still remember when having 16 megabytes of random access memory on a computer was a big deal, and a 1-gigabyte hard drive was considered more storage than you’d ever need. 10 Toward Healthcare Improvement Using Analytics that they are making conscious efforts to make better use of the data avail- able to assist with decision making and QI initiatives. Of course, HCOs vary in the extent and degree of sophistication by which they are leveraging their available data for informed decision making and performance improvement. TRADITIONAL TOOLS ARE OUTDATED AND INEFFECTIVE As analytical tools become more commonly used in healthcare beyond executive-suite ana- lysts and biostatisticians, the questions that are being asked are increas- ingly complex. It is becoming clear that traditional reporting approaches are becoming woefully inadequate and outdated—they are unable to deliver information that is accurate and timely enough to drive decision making, and they can only scratch the surface of today’s growing healthcare data- bases. Healthcare leaders are dealing with a multitude of regulatory, quality, and financial pressures and need accurate, timely, and readily available information to make decisions. In fact, HCOs do not require more reports to achieve desired improvement goals. HCOs require better insight into their own operations, transparency across boundaries, and accountability for their performance. The limiting, conventional views about decision mak- ing, data, and reporting must be challenged to allow for creative use of the available data and emerging analytics tools to foster data-based (not gut- based) decision making—in real time and near the point of care. INFORMING DECISION MAKING It is commonly said that data must be used to “drive decisions” in order to impact quality and performance improve- ment. What does “drive decisions” really mean, however, and how do we measure and judge how well information is being used? Much information is produced by analysts and other users of healthcare business intelligence (BI) systems, and most of this information is consumed by managers and other healthcare leaders. But how does (or how can) all this information actually drive decision making? Unfortunately, the default position for many organizations with respect to using information is the same type of reporting on which they have always relied. I am sure that after installing new HIT and healthcare BI solu- tions, every organization requests the BI and analytics team to develop the exact same reports as before. This discomfort of leaving behind what never really worked anyway means that many HCOs fall into an information rut that inhibits them from truly leveraging the information at their disposal. It is not my intention to give the term “report” a bad name, as if reports are the root of all that is wrong with the use of healthcare data. The truth is that a report can come in many guises. One example is the old-fashioned monthly multipage report that is distributed throughout an organization but rarely makes it out of the e-mail in-box. (Nobody distributes printed reports The Current State of Healthcare Costs and Quality 11 anymore, do they?) Dashboards, of course, are also reports, but good dash- boards present up-to-date indicators, consisting of relevant metrics with targets to maintain accountability, that truly assist with making decisions. In fact, the usefulness of information has absolutely nothing to do with the medium in which it is presented. A graphical, interactive dashboard can be just as disadvantageous as a stale, printed multi-page report in tabular format if the information contained within does not help answer the press- ing business problems facing an HCO. Tip The usefulness of information has absolutely nothing to do with the medium in which it is presented. Rather than getting caught up in which medium information is pre- sented, I believe that analytics professionals need to focus on ensuring that the information that is being used for decision making and QI has most (if not all) of the following attributes, which will be described later in this book. It is: Accurate Timely Relevant (to the questions being asked) Directed (at the right individual or stakeholders) Analyzed (appropriately given the types of data and questions being asked) Visualized (in a way that makes sense to the stakeholder) Beginning the Analytics Journey in Healthcare QI is often considered to be a “journey” in healthcare because of the constant evolution the HCO undergoes, because of the constant learning required to adapt to a changing environment, and because quality is a mov- ing target. An HCO should never strive for good enough, but should always be improving. The use of analytics within an HCO to improve quality and perfor- mance is a journey in much the same way. Analytics must be developed in an agile manner to keep pace with the changing needs of quality and per- formance improvement initiatives. Analytics specialists must keep their pro- fessional knowledge up to date and relevant because the technology that 12 Toward Healthcare Improvement Using Analytics enables analytics is always changing as are the analytic techniques (such as algorithms and statistical models) that are used to gain insight into health- care data. Analytics is very much a moving target—what is sufficient (and even leading-edge) in today’s healthcare environment most likely will not be five years from now. The role of analytics professionals in healthcare will continue to grow both in scope and in importance. I believe that for analytics to become a true game changer, analytics professionals must no longer be relegated to the back rooms of IT shops simply building reports and fulfilling endless data requests. Analytics must be brought to the front lines, where the inno- vative and transformational QI work takes place. Analytics professionals must be willing and prepared to engage with frontline QI teams and clinical staff directly, participate on quality initiatives, and experience what informa- tion is needed and how analytics is, and has the potential to be, used on the front lines. Information served up on a “report development request” basis cannot play a transformational role in healthcare improvement; transformation is possible only with embedded, agile, and motivated analyt- ics teams working side by side with other QI team members to achieve the quality and performance goals and objectives of the organization. It is incumbent on healthcare leaders to enable QI, IT, and analyt- ics teams to work together with frontline staff to support analytics-driven evidence- and data-informed quality and performance improvement initia- tives. In order for that to happen, there must be some common understand- ing around the topics of technology, data, and QI so that professionals in these different disciplines can communicate effectively within a team-based project environment. Unfortunately, many QI professionals and QI team members have lim- ited knowledge of the technology involved in healthcare analytics, what data is available, or even what analyses, visualizations, and other aspects of analytics can even be requested. Technology experts in IT who develop the code to transfer data from source systems to data warehouses (or other data stores) may not know the best format in which to make data available to BI and analytics tools, and so they may choose default data types based on how the data “looks” rather than on contextual knowledge of what the data means and how it will be used. Finally, analytics professionals who are building dashboards and other analytics for QI teams may not know the terminology around Six Sigma or Lean, and may not be familiar with the specific types of visualizations (e.g., statistical process control charts) or other analyses common with such methodologies. Despite where your HCO is on its analytics journey, remember that although the tools and technology of analytics will likely change at a rapid pace, the people are the most important component of healthcare analyt- ics. The future of healthcare analytics will involve professionals from many Notes 13 A NOTE ABOUT TERMINOLOGY It has been an enigma throughout the writing of this book how to name analytics professionals within the HCO. It is challenging to at- tach a label to a group of professionals who come from such diverse backgrounds, bring such an amazing range of skills, and play such an important role in bringing data to life within an HCO. As is typical in this book, I have shied away from using the trendy term of the day, and instead have leaned more toward classical or enduring terminol- ogy. I have opted to use the term “analytics professional,” or some- times “analytics developer,” to be as inclusive as possible. I know that not everyone will agree with this term, and I am ambivalent about it myself, but it is a term I believe is nonetheless both inclusive and descriptive. disciplines, with a common understanding of how analytics and QI must work together, using information made possible via analytics to create an environment able to provide patients with safe and effective healthcare of the absolute highest quality possible. Notes 1. Linda T. Kohn, Janet M. Corrigan, and Molla S. Donaldson, eds., To Err Is Human: Building a Safer Health System (Washington, DC: National Academy Press, 2000), 26. 2. Ibid, 49. 3. Health at a Glance 2011: OECD Indicators (Paris, OECD Publishing, 2011), http:// dx.doi.org/10.1787/health_glance-2011-en. 4. Mike Cottle et al., Transforming Health Care through Big Data: Strategies for Leveraging Big Data in the Health Care Industry (New York: Institute for Health Tech- nology Transformation, 2013), www.ihealthtran.com/big_data_in_healthcare.html. CHAPTER 2 Fundamentals of Healthcare Analytics If you always do what you always did, you will always get what you always got. —Albert Einstein Effective healthcare analytics requires more than simply extracting informa- tion from a database, applying a statistical model, and pushing the results to various end users. The process of transforming data captured in source sys- tems such as electronic medical records (EMRs) into information that is used by the healthcare organization to improve quality and performance requires specific knowledge, appropriate tools, quality improvement (QI) method- ologies, and the commitment of management. This chapter describes the key components of healthcare analytics systems that enables healthcare organizations (HCOs) to be efficient and effective users of information by supporting evidence-informed decisions and, ultimately, making it possible to achieve their quality and performance goals. How Analytics Can Improve Decision Making Healthcare transformation efforts require decision makers to use informa- tion to understand all aspects of an organization’s performance. In addition to knowing what has happened, decision makers now require insight into what is likely going to happen, what the improvement priorities of the orga- nization should be, and what the anticipated impacts of process and other improvements will be. Simply proliferating dashboards, reports, and data visualizations drawn from the HCO’s repository of health data is not enough 15 16 Fundamentals of Healthcare Analytics to provide the insight that decision makers need. Analytics, on the other hand, can help HCOs achieve understanding and insight of their quality and operational performance by transforming the way information is used and decisions are made throughout the organization. Analytics is the system of tools and techniques required to generate insight from data. The effective use of analytics within an HCO requires that the necessary tools, methods, and systems have been applied appropriately and consistently, and that the information and insight generated by analytics is accurate, validated, and trustworthy. In modern healthcare, substantial quality and performance improve- ment may be stymied without changes to the way information is used and acted upon. With this in mind, the fundamental objective of healthcare ana- lytics is to “help people to make and execute rational decisions, defined as being data driven, transparent, verifiable and robust”:1 Data driven. Modern healthcare standards demand that clinical deci- sions be based on the best possible evidence that is generated from extensive research and data. Yet administrative decisions, process and workflow design, healthcare information technology (such as EMRs), and even some clinical decisions are often not held to these standards. Analytics in healthcare can help ensure that all decisions are made based on the best possible evidence derived from accurate and verified sources of information rather than gut instinct or because a process or procedure has always been done in a certain way. Transparent. Information silos are still a reality in healthcare due to the belief by some that withholding information from other depart- ments or programs best maintains autonomy and control. This belief, however, often has the opposite effect and invariably leads to misun- derstandings and a deterioration of trust. A key objective of analytics in healthcare is to promote the sharing of information and to ensure that the resultant insight and information is clearly defined and consistently interpreted throughout the HCO. Verifiable. Consistent and verifiable decision making involves a val- idated decision-making model that links the proposed options from which to choose to the decision criteria and associated methodology for selecting the best available option. With this approach, the selected option “can be verified, based on the data, to be as good as or better than other alternatives brought up in the model.”2 Robust. Because healthcare is a dynamic environment, decisions must often be made quickly and without perfect data on which to base them. Decision-making models must be robust enough to perform in non- optimal conditions. That is, they must accommodate biases that might be introduced as a result of missing data, calculation errors, failure Analytics, Quality, and Performance 17 to consider all available options, and other issues. Robust models can benefit from a feedback loop in which improvements to the model are made based on its observed performance. Analytics and Decisions Healthcare analytics improves decision making by replacing gut instinct with data-driven, transparent, verifiable, and robust decision methods. Analytics, Quality, and Performance The techniques and technologies of analytics provide insight into how well an HCO is performing. Analytics enables healthcare leaders and QI stake- holders to make evidence-informed decisions through techniques, tools, and systems that: Clarify and improve understanding of patterns seen in data. Identify when (and why) change has occurred. Suggest (and help validate) the next logical steps to achieve desired change. First and foremost, analytics must help answer questions and drive deci- sion making related to achieving and maintaining safe, effective, and effi- cient delivery of healthcare. Effective healthcare analytics, however, consists of more than pointing statistical analysis software at large databases and applying algorithms and visualization techniques. What distinguishes analytics from most currently deployed reports and dashboards are the graphical, mathematical, and statistical tools and tech- niques to better understand quality and performance issues, and more impor- tantly, to identify what possible actions to take. Figure 2.1 illustrates the ways in which information can be used to support decision making for quality and performance improvement initiatives. Most HCOs use reports and dash- boards to review past performance (circle 1). Although a solid understanding of past performance is essential in identifying quality issues and monitoring progress toward meeting targets, relying solely on retrospective data provides little insight into what an HCO should be doing now or in the future. Many HCOs are adopting the capability for real-time performance mon- itoring, which may include real-time (or short-cycle) dashboards that pro- vide a reasonable picture of what is currently happening within the HCO (circle 2). To be effective, real-time monitoring must encompass appropriate 18 Fundamentals of Healthcare Analytics What has occurred? (1) What is Healthcare What is likely to occurring occur? Quality and now? (4) Performance (2) Why is it occurring? (3) FIGURE 2.1 Reporting and Analytics Capabilities for Quality and Performance Improvement indicators that are aligned with strategic and/or tactical performance goals and be linked to triggers within business processes that can signal that an action or decision is required. Tip To be effective, real-time monitoring must encompass appropriate indi- cators that are aligned with strategic and/or tactical performance goals and be linked to triggers within business processes that can signal that an action or decision is required. The reports and dashboards typical of circles 1 and 2 may help high- light what has occurred in the past, or what is currently occurring. But on their own, the information typical of circles 1 and 2 provides little insight into why performance is the way it is. Applications of Healthcare Analytics 19 Analytics goes one step further and helps answer questions such as why problems likely are occurring, highlights relationships between events and issues (circle 3), and, given the right models and data, can even begin to anticipate future outcomes and occurrences (circle 4). Analytical approaches (such as regression modeling and data mining techniques, for example) help to highlight relationships between various factors that, to various degrees, may be impacting quality and performance. For example, within existing reports and dashboards, an HCO might see that there has been a steady hospital-wide drop in patient satisfaction over the last quarter, and that an increase in central line infections has occurred over a similar period. Reports and dashboards may also highlight an increase in emergency department lengths of stay, and an increase in staff absenteeism rates. But most standard methods of reporting are inca- pable of providing any insight into why these issues are arising; charting methods such as basic bar or line graphs would be able to illustrate a trend over time and the amount of change in a measure that has occurred. Analyt- ics tools and techniques go one step further to help provide better insight into why these quality issues are present, determine if they are related, and predict future trends and possible outcomes. Applications of Healthcare Analytics One benefit of analytics is to enable healthcare leaders, QI teams, and other decision makers to ensure that the decisions being made are evidence- based, transparent, verifiable, and robust. Most areas of healthcare can ben- efit from decision making that meets these expectations; a few examples are outlined next. Process and workflow improvement. Efficient, effective, affordable, and safe patient care begins with processes and workflows that are free of barriers to quality and from which waste is reduced or eliminated. Determining what to improve, and how to improve it, is the responsibil- ity of dedicated multidisciplinary QI teams. The productivity of these QI teams, however, is greatly enhanced when they can leverage analytics to provide detailed insight into the processes and workflows that com- prise the management and provision of healthcare. QI teams rely on analytics for superior analysis of baseline data to identify bottlenecks and other causes of poor quality and performance. Analysis of baseline performance and quality data helps QI teams to identify and prioritize these causes so that the improvement initiatives selected are the most likely to have an impact and be successful. Analyt- ics is also necessary for monitoring ongoing performance of processes 20 Fundamentals of Healthcare Analytics and workflows, after improvements have been made, to ensure that the improvements are sustained in the long term. Clinical decision support (CDS). Many people incorrectly consider analytics as merely an extension of reporting. But analytics is not just a back-office capability. Analytics in support of clinical decision making can take on many roles, ranging from providing suggestions and evi- dence regarding the management of a single patient to helping manage an entire unit or department during a surge in patients. CDS is perhaps the ultimate use of healthcare analytics, which is disseminating timely, actionable information and insight to clinical providers at the point of care when that information is required and is the most useful. CDS leverages the information available within the entirety of the enterprise data warehouse (EDW) and clinical source systems to give providers insight into many clinical issues, ranging from possible diagnosis sug- gestions to predictions for excessive length of stay or adverse outcomes. An example of analytics in CDS is computerized provider order entry (CPOE) systems. The best of these systems automatically check the order with medical guidelines and compare ordered medications with other medications a patient is taking to check for the possibility of adverse drug interactions. Benefits of CDS systems are already being realized; one study demonstrated a 40 percent reduction in adverse drug reactions and other critical events in just two months.3 Other examples of analytics in CDS include flagging a patient as being at risk for an extended emergency department visit, or assisting with the triage of multiple patients presenting with an unknown respi- ratory ailment during influenza season. In the first case, the patient may be placed on special protocols to prevent unnecessarily long stays in the emergency department. In the second, analytics can help fill gaps in patient information and identify which new cases may be high-risk, allowing care providers to take appropriate isolation and infection con- trol precautions. Population health management. Population health management is “the coordination of care delivery across a population to improve clini- cal and financial outcomes, through disease management, case manage- ment and demand management.”4 Analytics helps HCOs achieve these improvements by identifying patient subpopulations, risk-stratifying the subpopulations (that is, identifying which patients are at highest risk of poor outcomes), and using CDS tools and best evidence to manage patients’ and populations’ care in the best way possible. Analytics also contributes to the ongoing tracking of patients to determine overall compliance and outcomes. Payer risk analysis and fraud prevention. One contributing fac- tor to the high cost of healthcare is fraud and other improper billing Components of Healthcare Analytics 21 to healthcare insurance. Healthcare data analytics is expected “to fun- damentally transform medical claims payment systems, resulting in reduced submissions of improper, erroneous or fraudulent claims.”5 This transformation in fraud prevention is possible because computer algorithms are able to analyze healthcare databases, scanning for pat- terns and other clues in the data that might indicate fraudulent activity and other irregularities. Once a manual, painstaking, and imprecise pro- cess, this is now an automated, immensely more efficient process, sav- ing healthcare systems billions of dollars. For example, the Centers for Medicare and Medicaid Services (CMS) achieved $4 billion in recoveries because of the fraud detection abilities possible with data analytics.6 In addition to improving understanding within each of these and other components of healthcare, analytics offers the potential to break through traditional barriers and allow understanding across so-called silos. Components of Healthcare Analytics Analytics consists of much more than back-office analysts applying com- puter algorithms to ever-growing volumes of data. Analytics exists in health- care to enhance the quality and safety of patient care while reducing costs. Patient care is a human-driven endeavor, therefore healthcare analytics requires the input of stakeholders to define what is useful and necessary. The output that healthcare analytics provides must be utilized by leaders, QI teams, and other decision makers in order to have any effect. Between the initial input and the resultant output, there are many levels and components to an analytics system that make evidence-based decision making possible. Forrester Research, Inc., identifies the “business intelligence [BI] stack” 7 to consist of the following layers: Infrastructure Performance management Supporting applications Analytics Discovery and integration Data Infrastructure The Forrester Research BI stack (and similar models from other organi- zations) provides a highly detailed summary of the components required to construct a BI infrastructure within a business enterprise (of which health- care is but one example). The purpose of this book is to focus on the 22 Fundamentals of Healthcare Analytics essentials of analytics for healthcare quality and performance improvement, so I have employed a modified stack optimized for healthcare analytics that focuses on business problem identification and insight generation. Figure 2.2 illustrates this “analytics stack,” a representation of what is required of an analytics system within an HCO to provide insight and sup- port evaluation of outcomes. Although not strictly necessary for analytics, a well-developed BI infrastructure will definitely support and enable ana- lytics and decision making throughout the HCO. For an excellent health- care BI resource, I recommend Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics.8 The analytics stack described here does not focus on the particulars of any one data warehouse model or technology but instead assumes that a mechanism is in place for data to be made available for analytics in a suitable format. The basic layers of this analytics system for performance and QI are: Business context Data Analytics Quality and performance management Presentation Analytics Stack Presentation Visualization Dashboards Reports Alerts Mobile Geospatial Quality & Performance Management Processes Indicators Targets Improvement strategy Evaluation strategy Analytics l ti Tools Techniques Team Stakeholders Requirements Deployment Management Data Quality Management Integration Infrastructure Storage Business Context Objectives Goals Voice of patient FIGURE 2.2 Components of the Healthcare Analytics “Stack” Components of Healthcare Analytics 23 Business context layer. This layer is the foundation of an analytics system and represents the quality and performance goals and objec- tives of the HCO. Included in the business context is the “voice of the patient” as a reminder that, above all, the goal of HCOs is to provide value to patients by delivering effective, efficient, and safe medical care. Every organization will have its own set of goals and objectives because of varying circumstances, demographics, and other factors. The goals and objectives of the business, and the strategies the HCOs employ to achieve them, drive requirements at every other level. Data layer. This layer of the analytics stack represents the quality, management, integration, and storage of data and the associated infra- structure. With the generation and accumulation of healthcare data comes the need to extract and integrate data from source systems such as electronic medical records (EMRs), store the data securely, and make high-quality data available for analytics and BI uses. Aspects of the data layer include: Data sources. These are the source systems such as EMRs, plus financial, supply chain, and other operational systems, that providers and other staff utilize in their day-to-day work. By and large, data in source systems is optimized for transactions, not analysis. When more than one data source exists, the data sources must be integrated to achieve true enterprise-wide visibility. Operational data store. As part of the integration process of bring- ing multiple data sources together into a single enterprise view, an HCO may opt for an operational data store (ODS) as an intermediary level of data integration. The ODS forms the basis for additional data operations (such as cleaning and integrity checks). Enterprise data warehouse. An EDW is built when available sources of data must be cleaned, transformed, and integrated for analysis and reporting to provide an enterprise-wide view of data. The data warehouse contains key indicators and other performance data pertinent to the quality and performance of multiple domains throughout the HCO. Analytic sandbox. The data in the EDW may be stored in a way that is aggregated to allow for faster, more efficient queries and analysis. Analysts may require access to lower-level data (for example, line- level patient data) to test new business rules or to run data-mining algorithms. The analytic sandbox is an area set aside for data for these purposes that does not negatively impact the performance of other operations on the EDW outside the analytics sandbox. Data marts. It may not be necessary, or advisable, for somebody to see all the possible data from across the entire enterprise that is available in an EDW. In these cases, data marts are instantiated; data 24 Fundamentals of Healthcare Analytics marts are subsets of data from the data warehouse (or the entire data set when only one source system exists), are usually organized by lines of business or healthcare domain, and represent what some- body within a particular line of business would need to see to best understand the performance of his or her program, department, or unit. Integration. Combining multiple source systems into a connected EDW is the process of integration. Without proper integration, an EDW would be nothing more than a collection of data points with- out any clear logic linking them. Integration can occur through a process of Extraction/Transformation/Load (ETL), which, in the most typical scenario, copies data from the source system(s), applies logic to transform it to the analysis needs of the organization, and loads it into an EDW. Other forms of integration, including virtualization, which defines a single interface that links to every point of data in the HCO, are increasingly common as volumes of data expand and new approaches to data management are required. Analytics layer. This layer is comprised of the tools and techniques that analytics teams use to generate information and actionable insight that drives decision making. Components of this layer include the intel- lectual knowledge of analytics teams and the computer software tools to apply that know-how. In this layer, analytics helps to identify quality and performance problems, develop analytical models appropriate to the problem, perform statistical analyses, generate insight into problem- solving approaches, and trigger necessary action. The analytics layer requires strong involvement from stakeholders, who provide the requirements for analytics that link the strategic-level goals and objectives for the organization to more tactical-level analyt- ics for decision making on the front lines by managers and QI teams. Consideration of how analytics projects and teams are to be managed to ensure a successful deployment is also necessary. There are several key features of the analytics layer: Online analytical processing (OLAP). OLAP tools typical- ly accompany data sets that are preaggregated and stored in a multidimensional format (that is, based on dimensions and facts) that allows users to quickly and interactively analyze data from multiple perspectives. OLAP typically consists of three types of operations: drill-down, which allows users to obtain and navigate through additional detail (for example, viewing revenue from each line of business of an HCO), roll-up (the opposite of drill-down, or the consolidation or aggregation of data), and slice-and-dice (with which users can extract a subset of data and view it in multiple dimensions). Components of Healthcare Analytics 25 Ad hoc analytics. When more complex analysis is required than is available through OLAP tools, analysts will use more statistical-based or other specialized tools to conduct deeper analysis. This type of analysis usually relies on nonaggregated data, and is often best per- formed in an analytics sandbox away from other EDW activities. Text mining. Text mining involves extracting value (by deriving pat- terns and trends) from unstructured text data. This is data that is stored in progress notes and wherever else codified data entry is not performed. Data mining/predictive analytics. These two disciplines consist of the process of determining patterns and trends in the data, and using the knowledge and understanding of those patterns and trends to make predictions about future performance or occurrences. Quality and performance layer. This layer aligns analytics to the processes that need to be improved, the indicators by which processes and outcomes will be evaluated, and the performance targets desired by the HCO. The actual improvement strategies and methodologies to be used (such as Lean and Six Sigma) should also be considered in this layer. This is important because improvement projects usually require extensive analysis of baseline performance and typically utilize indica- tors to evaluate project outcomes in order to sustain improvements in the long term. Processes. Data is a by-product of the work that clinical providers and other healthcare workers perform. When these workflows and processes are documented, data can be aligned with them to increase understanding of what the data means. Indicators. These are measures of certain aspects of an HCO’s per- formance. Targets. These are values that represent what the performance levels of a process or workflow should be, and represent the ideal range of an indicator. Improvement strategy. This describes how an HCO intends to address quality and performance issues, and what methodology the organization intends to employ (such as Lean or Six Sigma). Evaluation strategy. This is how organizations plan to monitor and evaluate the performance of key processes and indicators within the HCO. Presentation layer. This layer of the analytics stack can be considered the analytics “user interface.” The presentation layer manages the form in which insights and information are delivered to the decision makers. This layer is comprised of elements ranging from traditional reports to contemporary dashboards and can include more specialized tools such as geospatial visualization (or mapping). Although much of the heavy 26 Fundamentals of Healthcare Analytics lifting of healthcare analytics is situated within the data, analytics, and quality management layers, the presentation layer is critical because how well information is communicated will impact its usefulness to decision makers, QI teams, and other stakeholders. Given the different components that must work in concert to provide meaningful insight to decision makers, the effectiveness of an analytics sys- tem for quality and performance improvement will be greatly diminished without an analytics strategy. (See Chapter 3 for further information about developing an analytics strategy.) The purpose of the analytics strategy is to guide the HCO’s ability to rapidly respond to the information needs of stakeholders while maintaining a consistent direction in supporting the quality and business goals of the HCO. The analytics strategy provides a guide for sorting through the many and perhaps conflicting analytics needs of the HCO, and ensuring that each of these layers is configured, aligned, and/or developed appropriately to achieve the quality goals of the HCO. The strategy must guide decisions regarding what projects to undertake, what tools to invest in, and how to maximize return on investment in ana- lytics tools. The analytics strategy will align with, or be a component of, the overall BI strategy, since many analytics capabilities will depend on the extent to which a BI infrastructure is in place. Beyond the layers of data and technology of an analytics system is how the data is used—that is, the problem-solving that spans all of these layers. For example, many dashboards and reports merely reflect what has happened, and provide data in typical, predictable ways. But analytics encourages and assists people to think differently about the data they have and the problems they are solving. Sometimes a simple change such as applying a new visu- alization or applying a new statistic can help illuminate an existing problem in a whole new light. Other times, more sophisticated analytical techniques will be required to solve a particularly perplexing problem. All components of the analytics stack require careful consideration to ensure that the known questions of today are being addressed, and that an analytics infrastructure is being built that ultimately will address the unknown questions of the future. Notes 1. Rahul Saxena and Anand Srinivasan, Business Analytics: A Practitioner’s Guide, International Series in Operations Research & Management (New York: Springer Science+Business Media, 2013), 9. 2. Ibid, 10. 3. Mike Cottle et al., Transforming Health Care through Big Data: Strategies for Leveraging Big Data in the Health Care Industry (New York: Institute for Notes 27 Health Technology Transformation, 2013), www.ihealthtran.com/big_data_in_ healthcare.html. 4. The Free Dictionary, “population health management,” http://medical-dictionary.thefreedictionary.com/population+health+management. 5. Mike Cottle et al., Transforming Health Care through Big Data. 6. Ibid. 7. Boris Evelson. It’s Time to Reinvent Your BI Strategy (Cambridge, MA: Forrester Research, 2007), 4. 8. Laura B. Madsen, Healthcare Business Intelligence: A Guide to Empowering Suc- cessful Data Reporting and Analytics (Hoboken, NJ: John Wiley & Sons, 2012). CHAPTER 3 Developing an Analytics Strategy to Drive Change You’ve got to think about big things while you’re doing small things, so that all the small things go in the right direction. —Alvin Toffler An analytics strategy is more than simply a data utilization strategy, a data analysis strategy, a technology strategy, or a quality improvement strategy. In fact, elements of all these are required for an effective analytics strategy. An analytics strategy is necessary to ensure that an organization’s analyt- ics capabilities are aligned with its quality and performance improvement needs. This chapter discusses what an analytics strategy is, and will outline the steps necessary to develop an effective analytics strategy. In develop- ing a strategy, the chapter will discuss the components of and inputs to an analytics strategy, stakeholders who must be involved in developing the strategy, communicating the strategy, and how to implement it for maxi- mum success. Purpose of an Analytics Strategy The purpose of an analytics strategy is to guide a healthcare organiza- tion’s (HCO) ability to rapidly respond to the information needs of stake- holders while maintaining a consistent direction in supporting the quality and business goals of the HCO. It provides a guide for sorting through many, perhaps conflicting information and analysis needs, and prevents the HCO from being too swayed by vendor hype and other distractions. 29 30 Developing an Analytics Strategy to Drive Change The strategy provides analytics teams with the focus and direction needed to establish analytics and business intelligence (BI) as a strategic resource for healthcare leaders, quality improvement teams, and other decision makers within the HCO. Ultimately, the analytics strategy must aid the HCO to determine: What data is most required to address key quality, efficiency, and per- formance issues facing the HCO; What major analytics development projects to undertake and on what tasks to focus the analytics team; What skills and knowledge are necessary in the HCO’s analytics team; What data and integration infrastructure is necessary to support analyt- ics initiatives; What analytics software and hardware tools to invest in; and How to maximize return on investment in analytics tools, teams, and training by demonstrating value to the HCO. One definition of strategy is “a bridge that connects a firm’s inter- nal environment with its external environment, leveraging its resources to adapt to, and benefit from, changes occurring in its external envi- ronment,” and as “a decision-making process that transfers a long-term vision into day-to-day tactics to effect the long-term plan.”1 This defini- tion is pertinent to an analytics strategy because the analytics strategy will enable the HCO to leverage its information and analytics resources as it responds to and begins to control the many factors, both internal and external, that impact overall quality and performance. An analytics ANALYTICS AND BUSINESS INTELLIGENCE The analytics strategy is a critical adjunct to an HCO’s BI strategy, because the hardware, data integration, and data management re- quired for BI also enables the use of analytics. If an HCO is just embarking on the development of a BI infrastructure (perhaps in- cluding enterprise data warehouse development), then analytics requirements should be considered during the requirements gath- ering phase. If a BI infrastructure is already in place, an analytics strategy can help to identify any gaps that exist in BI that might need to be addressed to fully enable the desired analytics require- ments of the HCO. Purpose of an Analytics Strategy 31 strategy also helps to guide day-to-day decisions regarding systems, peo- ple, tools, and techniques, with the long-term goal of enabling analytics to provide information and insight regarding the most pressing problems facing the HCO. HCOs should develop a strategy for analytics to ensure that the infor- mation resources of the organization are aligned with the activities nec- essary for achieving the HCO’s quality and performance goals. Having a strategy cannot guarantee success, but without a strategy, analytics and IT development, team formation, and infrastructure procurement will proceed without the benefit of any clear plan or mandate. This likely will result in an investment of money and time (both resources usually in short supply) in analytics infrastructure, technology, and development projects that may not contribute to the fundamental goals of the organization, and may distract the HCO from achieving its goals. One of the most challenging aspects of working in a healthcare envi- ronment is the “emergencies.” Not the medical emergencies—those are the domain of the clinicians—but the frequent and urgent need for data and information. These urgent requests range from information required by gov- ernment agencies, to data for critical incident occurrence reviews, to a quick aggregation of data for a researcher racing to meet a grant deadline. These are a fact of life when working with healthcare data and cannot be avoided, but they should not result in complete and utter chaos within an analytics team. One struggle for healthcare analytics teams is to maintain sight of “true north,” that is, to know where and when to resume work on strategic pri- orities despite many competing demands. The analytics strategy can help prevent analytics teams from becoming overwhelmed and underproductive by keeping the organizational priorities in focus. Without a strategy that out- lines what the analytics priorities are and against which to judge the priority of new and urgent requests, what gets done is usually the request initiated by the person who is the most persuasive, or the problem that seems the most interesting to the analytics team, not necessarily the issue or problem that is the most important to the organization as a whole. An analytics strategy that aligns with the quality and performance goals of an organization will help the analytics team balance competing requests with strategic priorities and help the team maintain their productivity by reducing the feeling of being overwhelmed. A solid analytics strategy will help enable the analytics team to become a strategic information resource for business improvement and not simply purveyors of reports and data. When analytics teams are primarily occupied fulfilling the data requests of others, the result is that not much time is available for the strategic develop- ment of the group. 32 Developing an Analytics Strategy to Drive Change STRATEGIC DEVELOPMENT VERSUS DEVELOPMENT BY AGGREGATION I often joke (somewhat ruefully) that analytics tools and capabilities within an HCO are developed through aggregation instead of through design and strategy. For example, whenever an analytics team gets a request for information, they might add the report, dashboard, or other analytics tool to the general analytics or BI repository because “somebody else might need it.” The result is a sizable collection of reports and other tools that even the team doesn’t remember what they all do. To make matters worse, this causes work to be replicated because one analyst may not be aware of what somebody else has done, or because the

Healthcare Analytics for Quality and Performance Improvement PDF

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