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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: SAS Institute Inc
- Sprache: Englisch
Real-life examples of how to apply intelligence in the healthcare industry through innovative analytics Healthcare analytics offers intelligence for making better healthcare decisions. Identifying patterns and correlations contained in complex health data, analytics has applications in hospital management, patient records, diagnosis, operating and treatment costs, and more. Helping healthcare managers operate more efficiently and effectively. Transforming Healthcare Analytics: The Quest for Healthy Intelligence shares real-world use cases of a healthcare company that leverages people, process, and advanced analytics technology to deliver exemplary results. This book illustrates how healthcare professionals can transform the healthcare industry through analytics. Practical examples of modern techniques and technology show how unified analytics with data management can deliver insight-driven decisions. The authors--a data management and analytics specialist and a healthcare finance executive--share their unique perspectives on modernizing data and analytics platforms to alleviate the complexity of the healthcare, distributing capabilities and analytics to key stakeholders, equipping healthcare organizations with intelligence to prepare for the future, and more. This book: * Explores innovative technologies to overcome data complexity in healthcare * Highlights how analytics can help with healthcare market analysis to gain competitive advantage * Provides strategies for building a strong foundation for healthcare intelligence * Examines managing data and analytics from end-to-end, from diagnosis, to treatment, to provider payment * Discusses the future of technology and focus areas in the healthcare industry Transforming Healthcare Analytics: The Quest for Healthy Intelligence is an important source of information for CFO's, CIO, CTO, healthcare managers, data scientists, statisticians, and financial analysts at healthcare institutions.
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Seitenzahl: 444
Veröffentlichungsjahr: 2020
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Table of Contents
Cover
About the Authors
Acknowledgments
DISCLAIMER
Foreword
NOTE
CHAPTER 1: Introduction
PURPOSE OF THIS BOOK
HEALTH DATA DEFINED
HEALTHCARE CHALLENGES AND FOCUS AREAS
AUDIENCE FOR THIS BOOK
HOW TO READ THIS BOOK
STATE OF HEALTHCARE
HEALTH TECHNOLOGY
HEALTH RESEARCH
MEDICAL PROCEDURES
GROWTH IN HEALTHCARE
HEALTHCARE DATA
MULTIFACETED AND SILOED DATA
UNSTRUCTURED DATA
STRICT REGULATIONS
VALUE OF ANALYTICS
NOTES
CHAPTER 2: People
THE ABILITY TO PRODUCE RESULTS
WHAT TYPES OF ROLES?
WHAT JOB ROLES DO YOU NEED?
ORGANIZATIONAL STRUCTURES FOR YOUR COMPANY
ROLES TO EXECUTE YOUR DATA AND ANALYTICS STRATEGY
CHALLENGES TO GETTING THE RIGHT PEOPLE
THE ABILITY TO CONSUME RESULTS
THE “REAL ANALYTICAL EMPLOYEE COST” TO AN ORGANIZATION
BUILDING A RESOURCE LIBRARY
CONCLUSION
NOTES
CHAPTER 3: Process
WHAT IS CULTURE?
WHAT IS DESIGN THINKING?
WHAT IS LEAN?
WHAT IS AGILE?
DESIGN THINKING, LEAN, AND AGILE DEFINITIONS
CREATING A DATA MANAGEMENT AND ANALYTICS PROCESS FRAMEWORK
CHANGING THE ANALYTICS JOURNEY
WHAT IS THE CURRENT PROCESS?
NOTES
CHAPTER 4: Technology
STATUS QUO
IN-DATABASE PROCESSING
IN-MEMORY PROCESSING
OPEN SOURCE TECHNOLOGY
HADOOP
SPARK
PYTHON
R
OPEN SOURCE BEST PRACTICES
NOTES
CHAPTER 5: Unifying People, Process, and Technology
PEOPLE USE CASE – DELIVERING PRIMARY CARE PREDICTIVE RISK MODEL
PROCESS USE CASE – RATE REALIZATION MODEL
TECHNOLOGY USE CASE – POPULATION HEALTH MANAGEMENT APPLICATION
INTEGRATING PEOPLE, PROCESS, AND TECHNOLOGY – ENHANCING CLEVELAND CLINIC WEEKLY OPERATIONAL MEETINGS/CORPORATE STATISTICS DASHBOARDS
CASE STUDY TAKEAWAYS
CONCLUSION
CHAPTER 6: The Future in Healthcare
CLOUD
SECURITY
INTERNET OF THINGS
AI – ARTIFICIAL (AND AUGMENTED) INTELLIGENCE
VIRTUAL REALITY – TEACHING, EDUCATION, LAB
NOTES
CHAPTER 7: Final Thoughts
FINAL THOUGHTS
INDUSTRY TRANSFORMATION
JOURNEY TO VALUE-BASED CARE
INVESTMENT VS. RETURNS ON TECHNOLOGY AND INNOVATION
APPLYING AUGMENTED INTELLIGENCE TO SUPPORT CLINICAL, OPERATIONAL, AND FINANCIAL OUTCOMES
ENGAGEMENT ON THE HEALTHCARE DELIVERY MODEL TO UNDERSTAND EXPERIENCES AND VIEWPOINTS TO CREATE TRUST BETWEEN PROVIDERS AND PATIENTS
CONCLUSION
KEY TAKEAWAYS
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Assets Within an Organization
Table 2.2 Traditional Quantitative Business Analytics Techniques
Table 2.3 Machine Learning Algorithms
Table 2.4 Typical Storytelling Skills
Table 2.5 General Business Skills
Table 2.6 Sample of Healthcare Business Acumen Questions
Chapter 4
Table 4.1 Data Sources
Chapter 5
Table 5.1 Surgical and E and M Forecast Model Performance
Chapter 6
Table 6.1 IoT in Industries
List of Illustrations
Chapter 1
Figure 1.1 Storage of Your Medical Records
Figure 1.2 Sources of Health Data: From Paper to Digital
Figure 1.3 Major Advancements in Healthcare
Figure 1.4 Uniqueness of Healthcare Data
Chapter 2
Figure 2.1 What Is an Analyst?
Figure 2.2 The Analytics Maturity Curve
Figure 2.3 Four Capability Pillars to Support Your Analytics Strategy
Figure 2.4 A3 Title: What Are You Talking About?
Figure 2.5 Skills Map
Figure 2.6 Basic Organizational Models
Chapter 3
Figure 3.1 Process Framework
Figure 3.2 Getting-Ready-for-Work Process
Figure 3.3 Traditional Process Workflow
Figure 3.4 The Analytics Maturity Curve
Figure 3.5 Evolving Process Workflow
Chapter 4
Figure 4.1 Traditional Approach to Data and Analytics
Figure 4.2 In-Database Processing Approach
Figure 4.3 In-Memory Processing Approach
Figure 4.4 Hadoop Architecture
Figure 4.5 Data Analysis with Python and R
Chapter 5
Figure 5.1 Traditional Work Silos
Figure 5.2 The Work Is Converging
Guide
Cover
Table of Contents
Begin Reading
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For more information on any of the above titles, please visit www.wiley.com.
Transforming Healthcare Analytics
The Quest for Healthy Intelligence
Michael N. Lewis
Tho H. Nguyen
Copyright © 2020 by John Wiley & Sons, Inc. 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. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748–6011, fax (201) 748–6008, or online at www.wiley.com/go/permissions.
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Library of Congress Cataloging-in-Publication Data:
Names: Lewis, Michael N., author. | Nguyen, Tho H., 1972- author.
Title: Transforming Healthcare Analytics: the quest for healthy intelligence / Michael N. Lewis, Tho H. Nguyen.
Description: Hoboken, New Jersey : Wiley, [2020] | Series: Wiley & SAS business series | Includes index.
Identifiers: LCCN 2019053586 (print) | LCCN 2019053587 (ebook) | ISBN 9781119613541 (hardback) | ISBN 9781119613572 (adobe pdf) | ISBN 9781119613589 (epub)
Subjects: LCSH: Medical informatics—Technological innovation. | Medicine—Data processing.
Classification: LCC R858 .L493 2020 (print) | LCC R858 (ebook) | DDC 610.285—dc23
LC record available at https://lccn.loc.gov/2019053586LC
ebook record available at https://lccn.loc.gov/2019053587
COVER DESIGN: WILEY
COVER IMAGE: © MIRAGEC / GETTY IMAGES
This book is dedicated to my wife, Shelia, whose love, support and inspiration gave me the courage and strength to just write the damn book already! To my kids, Nicholas, Nevin, Natalie, Emily, Charlie, and Annie, who taught me that, through perseverance and determination, anything can be accomplished, you only have to try. To my dad, who taught me the important traits of being a successful leader and provided me invaluable career advice no matter how insignificant it seemed at the time. And finally, to my mom, who recently passed away from cancer. She was always there to lend a supportive ear and encouraged me to be the best person I could.
– Michael Lewis
This book is dedicated to my wife and kids who provided their unconditional love and unrelenting support with all the late nights, frantic weekends, and even working vacations to write this book. My family has been my greatest inspiration, giving me the flexibility and focus needed to complete this book in a timely manner. To all refugees around the world, hard work and persistence will open many opportunities.
– Tho H. Nguyen
About the Authors
Michael Lewis is Senior Director of Enterprise Analytic at Cleveland Clinic. Michael graduated from Cleveland State University in 1987 with a Bachelor of Business Administration with a concentration in Quantitative Business Analysis. He went on to receive his Master of Business Administration from Baldwin-Wallace College in 1991. He also earned the Health Insurance Associate Designation from America's Health Insurance Plans.
Michael was also a professor with Tiffin University where he taught graduate and undergraduate-level classes in Analytical Mathematics, Management Analysis and Research, Management Information Systems, and Information and Decision Support Systems.
He has spent his entire career of 30+ years in healthcare developing world-class analytics programs that promote a culture of fact-based decision-making and measurable continuous improvement. He has held the role of Senior Director of Enterprise Analytics since December 2015. He leads an industry-leading, cross-functional team to promote the design, implementation, and monitoring of innovative advanced analytical disciplines and solutions through the coordinated and systematic use of clinical and encounter-based data, related business insights, and multidisciplinary skill set for planning, management, measurement, and learning. Previously, he joined Cleveland Clinic in June 2012 as the Director of Contract Economics. He developed and implemented strategic reimbursement models, self-help analytics, and discovery dashboards to meet Enterprise metrics on US$8.0 billion+ of revenue.
Before joining Cleveland Clinic, Mike started his career in healthcare in 1988 working for Medical Mutual of Ohio (MMO) (formerly known as Blue Cross Blue Shield of Ohio). During his 24+ years at MMO, he held a variety of positions. As an Actuarial Analyst and Senior Financial Analyst in Provider Reimbursement and Data Analysis, he developed and implemented analytical models that enhanced the company margins by 3 percent. As a Regional Network Manager, he architected the building of proprietary hospital networks in Indiana and Northeast Georgia. He built analytical models that help identify reimbursement unit costs opportunities that were contractually implemented network-wide.
In his spare time, Mike is an avid sports fan and options investor. He enjoys spending time with his wife, traveling, reading, and listening to podcasts. He is a foodie and craft beer enthusiast. Mike enjoys exploring exotic foods with Tho any time they are together.
Tho H. Nguyen came to the United States in 1980 as a refugee from Vietnam with his parents, five sisters, and one brother. As the youngest in the family, Tho has tremendous admiration for his parents, who sacrificed everything to come to America. Sponsored by the St. Francis Episcopal Church in Greensboro, North Carolina, Tho had enormous guidance and support from his American family who taught him English and acclimated him and his family to an opportunistic and promising life in America.
Tho holds a Bachelor of Science in Computer Engineering from North Carolina State University and an MBA degree in International Business from the University of Bristol, England. During his MBA studies, He attended L'École Nationale des Ponts et Chaussées (ENPC) in Paris, France – University of Hong Kong, Hong Kong – and Berkeley University, California. Tho proudly represented the Rotary Club as an Ambassadorial Scholar, which provided him a global perspective and a deep appreciation for the world of kindness.
With more than 20 years of experience, Tho has various leadership roles in data management and analytics. Integrating his technical and business background, Tho has extensive experience in alliance management, global marketing, and business/strategy management. Tho is an author, an active presenter/keynote speaker at various conferences, and a technology enthusiast.
In his spare time, He does volunteer work for various non-profit organizations and has held leadership positions for the Vietnamese-American Association of Raleigh, NC and Asian Focus NC. He has donated all of his proceeds from his first book to charities locally and globally, and gave two scholarships to pay it forward. Tho enjoys spending time with his family, traveling, running, and playing tennis. He is a foodie who is very adventurous, tasting different and exotic foods around the world.
You can connect with him via LinkedIn https://www.linkedin.com/in/thohnguyen/.
Acknowledgments
First, I would like to recognize my co-author, Tho H. Nguyen, for his understanding, guidance, and support during this new chapter in my life. Tho is a leading expert in how technology can play a role in your analytics strategy. Tho's first book, Leaders and Innovators, is a must read and inspired me to share my experiences. Second, I would like to recognize you, the reader of this book. By showing interest in learning how to bring to life an analytics strategy, your quest for health intelligence will be a positive disruptor for the healthcare industry.
There are many people at Cleveland Clinic, who started this journey before I arrived, who believed in me and allowed me to help shape the analytics strategy of the future. First is Chris Donovan, whose leadership, mentorship, and relentless pursuit of perfection gives me the drive to put forth world-class analytics for a world-class organization. Second is Andrew Proctor and Eric Hixson. As business partners in Enterprise Analytics, it is their clinic and operational knowledge and expertise that allows the converging work to be more meaningful to the organization. An extra thanks to Eric for always taking the time to debate the merits of any and all methods and models considered. To my analytics team, especially, Don McLellan, Cathy Merriman, Joe Dorocak, Michael Bromley, John Urwin, Colleen Houlahan, Dan Rymer, and James Allen, and those not named, for your tireless attention to details and putting up with my crazy ideas. I know we are making a difference and it starts with your dedication to our patients and organization.
I cannot thank everyone enough who tirelessly spent long nights reviewing and providing input, chapter by chapter, especially Lauree Shepard, Tho Nguyen, and Michael Bromley. Trying to bring to life real-world learnings, following my logic, opinions, and trying to understand how you put an analytics strategy into words can be maddening, but you did it with kindness, compassion, and thoughtfulness. I owe you gratitude beyond expression for your tremendous dedication to making sure the message is easily consumable and usable to the readers.
A special salute to all healthcare professionals whom I have interacted personally with and those I have not. Your dedication to caring for the sick and trying to cure life-changing medical events continues to ignite my passion to solve healthcare challenges as they arise. Finally, to my wife and children, thank you for brightening my life every day and allowing me to share yours.
– Michael N. Lewis
First, I would like to recognize my co-author, Michael N. Lewis, for his passion and patience writing this book with me. Mike brings the deep knowledge and insightful experience to make this book relatable. Second, I would like to recognize you, the reader of this book. Thank you for your interest to learn and be the agent of change in the healthcare industry. I am contributing the book proceeds to worthy charities that focus on technology and science to improve the world, from fighting hunger to advocating education to innovating social change.
There are many people who deserve heartfelt credit for assisting me in writing this book. This book would have not happened without the ultimate support and guidance from my esteemed colleagues and devoted customers. A sincere appreciation to my colleagues who encouraged me to share my personal experience and helped me to stay focused on what's relevant.
I owe a huge amount of gratitude to the people who reviewed and provided input word by word, chapter by chapter, specifically Lauree Shepard, Clark Bradley, Paul Segal, and Michael Lewis. Reading pages of healthcare jargon, trying to follow my thoughts, and translating my words in draft form can be an overwhelming challenge but you did it with swiftness and smiles. Thank you for the fantastic input that helped me to fine-tune the content for the readers.
A sincere appreciation goes to James Taylor, all healthcare professionals, IT specialists, and business professionals whom I have interacted with over the years. You have welcomed me, helped me to learn, allowed me to contribute, and provided real intelligence for this book. Finally, to all my family (the Nguyen and Dang crew), the St. Francis Episcopalian sponsors, the Rotary Club (the Jones Family, the Veale Family) – all of whom have contributed to my success – I would not be where I am today without them. To my wife and children, thank you for being the love of my life and bringing light and purpose to my day.
– Tho H. Nguyen
DISCLAIMER
The views expressed in this book are those of the individual authors representing their own personal views and not necessarily the position of either of their employers.
Foreword
by James Taylor 1
I have been working with advanced analytics for nearly 20 years. The market has matured dramatically to the point where analytics, machine learning, and AI are now common topics of conversation in every industry. Once, analytic models were handcrafted for a few high-value scenarios. Now, companies are automating the creation of advanced analytics and using them to solve a wide range of problems. The time to develop and deploy advanced analytics has gone from months to seconds, even as the amount of data being analyzed has exploded. Every industry is focused on being more data-driven and healthcare is no exception.
Tho and I met many years ago through our work as faculty members of the International Institute for Analytics. We have a shared interest in the technologies and approaches of analytics and in how organizations can truly take advantage of their data.
Healthcare is an industry that impacts everyone throughout their life. New drugs, new treatments, and new understanding drives continual and rapid innovation. Yet even as healthcare technology and treatments get more effective, populations in many countries are struggling with older populations and an epidemic of obesity. Drug resistance is an increasing problem and costs continue to rise. The healthcare industry needs to find ways to use data to tackle these and many other challenges.
Healthcare organizations have a particular challenge when it comes to analytics. Healthcare data is uniquely complex and uniquely sensitive. It must capture the state of a complex, living person. It is only imperfectly digitized and much of it is image related, time series related, or both – hard classes of data to manage and analyze. It is also intensely personal, so its use is regulated and controlled to protect people's privacy and prevent health-related discrimination. Taking advantage of this data to reduce costs and improve outcomes is both essential and complex.
Over the years I have worked with hundreds of organizations that are using analytics to improve their decision-making. Like Tho and Mike, I have come to see that people and process are as essential as technology – perhaps even more so. Building cross-functional teams, engaging a broad set of skills, and having a process that focuses on decision-making are all necessary if analytic technology is to be applied effectively.
Take one healthcare provider I was working with recently. A technical team had developed some potentially useful analytic models. But working alone they could get no traction. We engaged clinical and operations staff in a discussion of the current decision-making. We applied design thinking and decision modeling to see how that decision might be improved with the analytic. With this shared understanding the technical team could see what a minimum viable product would require and could execute a series of Agile sprints to deliver it. People, process, and (analytic) technology.
With this book, Tho and Mike hope to show healthcare professionals how to transform their industry with data and analytics. Right from the start, they emphasize the importance of people, process, and technology – not just the coolest, newest technology. Real-world stories of healthcare problems addressed by insight-driven decisions show healthcare professionals what's possible and what technology exists. The stories help bring to life how analytics might create a more effective future state in healthcare.
The core chapters on People, Process, and Technology are full of great advice. There is a discussion of the skills needed, especially in analysis and business understanding. The need to invest in a wide range of roles (not just hire unicorns) and the importance of changes in sponsorship culture are emphasized. Three critical elements of process are discussed next. Design Thinking – something we find very effective in defining how analytics can improve decision-making – Lean and Agile. Our experience is that the hardest problem is defining the business problem so analytics can be applied effectively. As the authors point out, success therefore requires process change and the creation of a repeatable, sustainable playbook. The technology chapter gives a succinct but complete overview of available technology. All of this is pulled together into a framework for integrating people, process, and technology to drive culture change and move up the analytic maturity curve. The authors talk about the importance of focusing on data as an asset, bringing together cross-functional teams, providing clear leadership, and investing in growing analytic talent. All of this is illustrated with real-world case examples. A final chapter lays out what's coming and how will it change healthcare, especially the growth in sensors and devices connected through the Internet of Things, the growth of the cloud, and the adoption of artificial intelligence.
If you are a healthcare professional concerned about applying data and analytics to improve your organization, this book will give you valuable insights. The advice and framework will help you organize, recruit, train, and develop the data analytics capability you need.
Healthcare needs to become more data-driven, more analytic. This book will show you how.
NOTE
1
James is CEO and Principal Consultant, Decision Management Solutions and a faculty member of the International Institute for Analytics. He is the author of
Digital Decisioning: Using Decision Management to Deliver Business Impact from Artificial Intelligence
(MK Press, 2019) and
Real-World Decision Modeling with DMN
with Jan Purchase (MK Press, 2017). He also wrote
Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics
(IBM Press, 2012) and
Smart (Enough) Systems
(Prentice Hall, 2007) with Neil Raden. James is an active consultant, educator, speaker, and writer working with companies all over the world. He is based in Palo Alto, CA and can be reached at
.
CHAPTER 1Introduction
“Without data, you're just another person with an opinion.”
— W. Edwards Deming
PURPOSE OF THIS BOOK
While there have been many improvements and changes in healthcare, Mike and I strongly believe there is still a lot to do and we want to share with you our journey to make healthcare better one patient at a time. Our motivation for this book is to share with our valued readers real-world, personal experiences and to show how technology coupled with people and process is paving the way toward the adoption of the digital transformation in healthcare. Digital transformation also makes a strong case for how healthcare organizations can do so much better because of the innovative analytical practices that we have readily available today but they are not implemented or being considered in many instances.
Whether you realize it or not, healthcare affects everyone – young and old. When you, Mike, or I were kids, healthcare was not a topic of concern. Most of us had parents or guardians to oversee our healthcare. Personally, as I have gotten older and more mature mentally and physically, healthcare has become a necessity with more regular visits to the doctor or hospital. When we become guardians and parents ourselves, we have others to think about, whether it is looking after our own kids, taking care of an elderly family member or our own parents, or even fostering children. As a new parent, healthcare is definitely a priority for my wife and kids, not only having access to healthcare but also the quality of care that we seek when needed. Healthcare affects all of us one way or another throughout our life cycle, from birth, toddler, adolescent, adult to end of life.
Healthcare affected me very personally about a year ago when my wife was misdiagnosed or missed diagnosed due to lack of data and empathy in the plan of care. It was a brisk winter morning in February when it all started when my wife complained about some back pains and stomach discomfort. My wife and I had our daily routines where I was working in my home office and she was getting our daughter ready for school. That afternoon, my wife's agonizing back pains and stomach discomfort escalated to a level beyond tolerable. Having had these symptoms in the past, she had been taking over-the-counter medications to see if they would go away. Unfortunately, they didn't and this time the pain became so much worse. Since our family doctor's office was closed due to it being after business hours, urgent care was our best option. It was late afternoon on Valentine's day and it was a day that we will never forget. Once we arrived at the urgent care, we filled out forms about my wife and symptoms that she was experiencing. The nurse asked her repetitive questions and took notes at the same time. We provided our insurance coverage details and were asked to wait. Because it was Valentine's day, the urgent care waiting room was nearly empty and the doctor was able to see us pretty quickly. The urgent care doctor asked my wife the same questions that the nurse had asked, then examined my wife but could not pinpoint the cause and a cure for the pain. The urgent care office suggested that we go directly to a nearby hospital emergency room (ER) to get a better diagnosis of my wife's condition. The urgent care nurse said that all of my wife's visit and information would be transferred to the ER and they would know what was done at the urgent care since it is affiliated with the ER hospital. Upon arrival at the ER and at check-in, there were no records and no one was aware of our arrival, situation, and condition. Thus, the traumatic drama escalated and continued on Valentine's evening.
Because all the data that was collected at the urgent care office was not in the system at the ER hospital, my wife and I had to relate all the same information again. In the midst of severe pain, I responded to most of the questions on behalf of my wife. The most obvious data such as name, address, birth date, Social Security number, insurance numbers, and gender were needed and entered on a form again before we could be checked into the ER. At this point, I could see my wife's pain had worsened and asked why there were no records and information from the urgent care office which is affiliated with the ER hospital. I questioned the repetitive process and why we had to enter the same data on the forms when my wife is a patient at the hospital and had history at the facility for over five years. The response was “We needed the data and forms to admit your wife” and we had no choice but to abide at that moment in time. After a few hours of waiting, we finally saw a nurse who asked the same, repetitive questions from the forms that we had filled out and then documented my wife's symptoms. A few more hours of waiting and we finally saw an ER doctor. The ER doctor asked the same questions as the nurse did and we felt like a broken record repeating the same information for the fourth time. Finally, the ER doctor ordered blood tests, x-rays, and a magnetic resonance image (MRI). Each procedure was executed by a different personnel and department, so we had to wait even more in the hospital room in between each test. As you can imagine, hours passed waiting for results from each test and the pain continued.
Being helpless had to be the worst feeling – unable to do anything except sit and wait with my very young daughter, whom we had brought along, thinking that we would be home within a few hours. Having a two-year-old toddler in an ER at the peak of winter when colds and flus were highly contagious was nerve-racking and worrisome. The doctor finally visited our room to give us the diagnosis. Based on the results, the diagnosis was an infection and the doctor gave my wife some prescription medication to help with the pain and antibiotics for what was diagnosed to be a urinary tract infection (UTI). I vividly remember it was 4 a.m. the next day that my wife was released from the ER and we got to go home. We would never forget how we spent that year's Valentine's Day and were happy to head home. It was a blessing that there was a path to alleviating the pain for my wife.
A few weeks later, once the antibiotics were completely consumed, my wife was feeling better and we thought she was cured with no pain in the stomach and back. Regrettably, that was not the case as the back pain and stomach discomfort returned with a vengeance. Over a six-month time span, from February when the pain started to August when it was correctly diagnosed and my wife had an operation, we had multiple visits to our family doctor, specialists, and the ER. Each time we visited a clinician and the ER, my wife had a different diagnosis which the doctors were unsure how to treat and what to do about it. Each visit required more bloodwork, x-rays, and MRIs – all of which were captured in fragmented, siloed systems from each office and there was no clear path to a cure in sight. Each ER stay was one week long and I had to communicate the history and recent visits to each nurse, doctor, and specialist at each hospital. During this time, my wife had multiple procedures and operations at various hospitals within one healthcare system but much of the data and details were not related or communicated among nurses and doctors. In the middle of summer, we had our final procedure and it was to remove an infected gallbladder, an insertion and removal of two stents to isolate the gallstone and an extremely stubborn, oversized gallstone. What should have been a simple diagnosis to detect the gallstone and removal of the gallbladder dragged on for six months with extreme pain and agony. In addition, we had multiple hospital stays that were costly and stressful.
What I learned from this experience is that:
Healthcare has become shallow with longer wait times and shorter face-to-face time with the doctors and clinicians with redundant processes for each touchpoint.
Clinicians have become data clerks and their notes are not well captured and not well communicated among themselves and within the healthcare ecosystem.
Healthcare data is so overwhelming due to its volume and lack of data in the same ecosystem that clinicians are unable to review and correctly diagnose the ailment and provide a cure in a timely manner.
The cost of each visit was astronomical and accumulated with every point of contact – the hospital check-in staff, nurse assistant, nurse, physician assistant, physician, and specialist. When we received the bill for each line item, we were very thankful to have health insurance; otherwise, we would have been in great debt. I can't imagine not having insurance.
Illnesses cause stressful times for families, especially the kids. Being sick and not knowing or having a care plan in sight for a cure was very traumatic for me and my daughter (who missed her mother terribly during overnight stays). My wife is a strong and patient woman who endured so much pain and agony.
The above scenario spawned the idea for this book. Mike and I have been in the data and analytics profession for over 45 years collectively and we want to educate you on concepts that can lead your organization to sustained changes and to improve clinical, operational, and financial outcomes. Over the years, data and analytics have changed considerably and have become more convoluted – particularly in the healthcare sector. Health data volumes have skyrocketed, legacy data archives are on the rise, and unstructured data will be more prevalent in the healthcare sector than in any other sector. Healthcare is the only industry that keeps all types of records from birth to end of life and that volume puts a tremendous amount of burden on healthcare organizations to maintain and manage. But it is definitely an exhilarating time that generates many challenges and great opportunities for healthcare organizations to investigate and implement new and innovative technologies to accommodate data management and analytical needs. Thus, Mike and I invite you to join us on our journey to improve healthcare outcomes with insights and to integrate data and analytics in a harmonious environment.
When Mike and I met over five years ago, we both had attended a number of conferences and presented to both business and technical audiences about solutions that help healthcare organizations to be more effective managing the exponential growth in healthcare data and more efficient by streamlining the analytical processes that provide insight-driven decisions. As we shared our experiences, we received in return an overwhelming insight into healthcare organizations' challenges and issues. The biggest and most common questions were around people, process, and technology:
What skill sets do I look for when hiring people, business analysts, or data scientists?
What can I do to challenge my staff to do things differently and more efficiently?
What are some ways to improve processing time since there is more data than ever to analyze?
How can I deliver results to my leadership team with information that is real-time and improves decision-making?
What technologies should I consider to support a digital transformation?
Is cloud the right strategy for my healthcare organization?
Is open source being implemented in other healthcare organizations?
Where does artificial intelligence and augmented intelligence fit in?
Are healthcare organizations keeping pace with other industries?
What services should I consider training my organization in to be self-reliant?
As Mike and I attended a conference in sunny San Diego, California, we had a eureka moment over a meeting. What if we wrote a book that combines real-world problems focused on data and analytics in healthcare and share with our readers the challenges and successes? Mike would bring the business perception while I would bring my technology background to provide a complete perspective. We realize there are many books about healthcare, but this book is unique in ways that connect people, process, and technology to prepare for the digital transformation in healthcare from our direct experiences and backgrounds. We approached an editor who is also a mutual colleague with this idea and concept, and she was very enthusiastic about our book proposal. After several months of negotiations and developing the outline and timeframe, the publisher accepted our pitch. Our goals for this book are to:
Share real-world healthcare problems and use cases focusing on connecting and integrating people, process, and technology to deliver insight-driven decisions.
Educate healthcare professionals in what innovative technologies are available to manage data and apply analytics with some best practices to transform your organization.
Provide a unique perspective of the future of healthcare and what to expect with the rise of digital transformation, machine learning, and artificial intelligence.
Whether you have a business or technical background, we truly believe you will appreciate the real-world use cases presented here. Before we dive into the details, we believe it is very appropriate to set the tone with what is health data and some challenges in the healthcare sector that demand the connection and integration of people, process, and technology. It is needed to maintain and sustain leadership in a very complex and growing healthcare industry.
HEALTH DATA DEFINED
Many years ago when I was a child, I could recall sitting in my doctor's office as my parents filled out forms about me and my health that contained fields such as name, date of birth, address, Social Security number, medications that I was taking, what is the purpose of this visit, and history of family members for each visit to each type of clinician. All of these forms have been kept in a folder that can stack up as high as the ceiling and stored in file cabinets (see Figure 1.1). When you visit the doctor and hospital, they retrieve those records, sift through all of that historical data, and review them to assess your condition. As the nurse called my name to go back to see the doctor, they would measure my weight, my blood pressure, and body temperature – all of these data points were also captured and entered in my file. Once the doctor was ready to see me, he would ask me some simple questions based on the information provided on the forms. At the same time, the doctor also evaluated and observed my physical and emotional attributes to see if any of these intangible factors provided any insights to my wellbeing, both physically and emotionally. From my pediatrician to my current doctor, all of my records have been maintained in some manner.
This photo by an unknown author is licensed under CC-BY-2.0.
Figure 1.1 Storage of Your Medical Records
Source: S.J. Howard, 2010. https://sjhoward.co.uk/in-support-of-a-national-nhs-computer-system/. Licensed under CC-BY-2.0.
With technology advancements, these forms can now be scanned, notes from paper can be entered into computers, and observations get captured in a dialogue – all of which can now be archived in electronic health systems. As I have gotten older, I had to fill out the same forms myself with the identical data points and the repetitive information continued to get captured. If you are a healthy person, you would visit your doctor once a year for a wellness checkup; otherwise, you may have additional visits when you get a cold, cough, or an injury. Unfortunately, if you encounter a serious illness such as injury, cancer, stroke, or a heart attack, x-rays and magnetic resonance imaging (MRI) are needed and these images are combined with your other data points to diagnose a problem and plan a treatment. All of these health data are kept from year to year, from birth to end of life, and become very voluminous and complex.
Thus, health data is defined as any data that relates to your health and comes from many sources such as behavioral observations, environmental factors, and socioeconomic data. Health data can be structured or unstructured. For example, your name, date of birth, blood type, or gender is considered as structured data and can be standardized in columns and rows. Most structured data can be stored in a data warehouse. Unlike structured data, unstructured data such as your doctor's notes, x-rays, MRIs, audio recordings, or emails are not standardized and have become more prevalent. Unstructured data are typically not stored in a data warehouse and require a different data storage mechanism. All structured and unstructured health data are collected over time to help understand the past, assess the present, and foresee the future of your health.
Besides our own personal health data, other health data sources can come from clinicians, pharmacies, labs, hospitals, health agencies, and devices (mobile) as described below and shown in Figure 1.2:
Clinicians
– an encounter with your family physician, specialist doctor, physician assistant, or nurse that examines your condition and recommends a cure for a diagnosis.
Pharmacies
– medications that are prescribed by your doctor to maintain your health or cure an illness and distributed at your local or online pharmacy. It can be generic as antibiotics to destroy bacteria or specific as lowering your blood pressure. These prescriptions are captured and regulated to avoid possible abuse.
Labs
– this can be as simple as blood work to determine your blood type, cholesterol level, vitamin deficiency, or as complex as tests to determine if you are a sickle cell anemia carrier. Each lab work is carefully analyzed and the results are reported to your doctor for diagnosis.
Hospitals
– visits to the hospital that normally require a doctor's attention for more serious procedures such as heart surgery, removal of a tumor, or giving birth.
Health agencies
– agencies such as Centers for Disease Control (CDC) or National Institute of Health (NIH) provide medical research using our health data to control the spread of diseases or find cures of deadly diseases so that we can live longer and healthier.
Devices (mobile)
– these can be both apparatus that gets installed in your body or wearables on your wrist to collect vital data such as heartbeat, blood pressure, number of steps, etc.
Figure 1.2 Sources of Health Data: From Paper to Digital
Source: Author.
HEALTHCARE CHALLENGES AND FOCUS AREAS
Now that the definitions have been established, let's examine some high-level challenges and how they translate into focusing on people, process, and technology to move forward into the twenty-first century and beyond. Many organizations, not just healthcare, are sharing with us similar challenges they encounter in the ever-changing world of economics and competition.
The first challenge is shortage of resources. Providing quality healthcare starts with people. Nurses, physician assistants, doctors, lab technicians, specialists, and therapists, etc. are always in high demand. According to Mercer's US Healthcare External Labor Market Analysis, the United States will need 2.3 million new healthcare workers by 2025 in order to take care of its aging and growing population adequately. These professions interact with patients on a daily basis, collect your data for analysis, and document the diagnosis and treatment to help you become healthy and on your way to wellness. Currently, there is a shortage and a challenge to keep talented professionals in healthcare while other sectors such as retail and manufacturing are displacing workers. Some healthcare organizations are welcoming these displaced workers and train them to fill some jobs such as lab technician, data entry, and medical assistants. There are many reasons as to why there is a shortage, but healthcare organizations are exploring strategies and offering incentives to avoid high turnover. Some of the strategies will be explored in a later chapter that revolves around people. In a nutshell, treating people as an asset becomes a competitive advantage to fulfilling the needs in healthcare.
The second challenge is using data effectively. As Figure 1.2 illustrates, the multifaceted sources of health data each person produces, managing the data is the biggest challenge healthcare organizations are facing today. Much of the data are in multiple places and systems. There is no industry standard or governance to allow patients to see their own data and there is no one system to host all of the patient data for easy access that clinicians and hospitals need to provide high-quality care for their patients. In the healthcare sector, we have plenty of data but lack the comprehensive knowledge because organizations are unable to access the many dimensions of data and analyze all the data they have about a patient. To borrow a quote from W. Edwards Deming, “Without data, you're just another person with an opinion.” Opinions in healthcare can become a liability and have high risks of being incorrect. Clinicians need to have data-driven insights to make informed decisions so that mistakes can be avoided. In addition, the ability to analyze the data has become more complex with a variety of data types (structured and unstructured) and healthcare organizations may not have the right infrastructure and/or tools to mitigate the complexity. As data become more voluminous, it is imperative to have a solid foundation of technology and a well-defined process for managing data, analytics, and decisions.
The third challenge is innovating toward a digital transformation. All industries are challenged to innovate and prepare for the digital transformation. Digital transformation requires organizations to re-think their overall business processes. It is about using digital technologies and leveraging all your data to put the customer at the heart of the business. Other industries such as retail, travel and transportation, and telecommunications have embraced and started the path toward a digital transformation, placing emphasis on enhancing the customer experience. We believe the healthcare sector is lagging behind and has not yet developed a comprehensive strategy to overcome a highly siloed, fragmented ecosystem that many healthcare providers are dealing with toward obtaining a holistic view of the patient. In order to succeed in digital transformation, healthcare organizations must start evaluating a strategy and determine ways to become more effective and efficient in connecting and applying the data, deploying technology to improve communication to better engage with the patient, and enhance the patient experience. We believe healthcare companies are in catchup mode and the digital transformation initiative must be in the forefront to elevate the healthcare industry.
The above challenges that we hear from colleagues, vendors, and customers translate into three areas of focus for the rest of this book:
People
– many industry articles, thought leaders, and trends tend to indicate that data is the most important asset. Mike and I disagree and strongly believe that people are the most important asset, regardless of the industry or organization. You and I are the ones who are the creators and consumers of data. You and I are the ones who have to provide the intelligence into systems to transform data into action. Thus, investing in people, especially in healthcare, is a priority to become more patient aware and more friendly in communication as an industry.
Process
