Natarajan / Frenzel / Smaltz Demystifying Big Data and Machine Learning for Healthcare

Erscheinungsjahr 2017
ISBN: 978-1-315-38931-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 210 Seiten

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it.

Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to:

- Develop skills needed to identify and demolish big-data myths

- Become an expert in separating hype from reality

- Understand the V’s that matter in healthcare and why

- Harmonize the 4 C’s across little and big data

- Choose data fi delity over data quality

- Learn how to apply the NRF Framework

- Master applied machine learning for healthcare

- Conduct a guided tour of learning algorithms

- Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs)

The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Autoren/Hrsg.

Natarajan, Prashant

Frenzel, John C.

Smaltz, Detlev H.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Krankenhausmanagement, Praxismanagement
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Dienstleistungssektor & Branchen

Inhaltsverzeichnis

Chapter 1: Introduction

- What is big data and how is it similar/different from business intelligence or analytics – the basics? Analytics 1.0, 2.0, and 3.0

- Why big data needs machine learning - in brief

Chapter 2: Healthcare and the Big Data V's

- The case for big data - market analysis - vendors and applications

- Introduction to the V's

- When do we need to care about data quality?

- What can you do with this data? Introduction to Types of analytics

Chapter 3: Big Data - How to Get Started

- Getting started within your Organization

- Assessing your environment and organizational readiness

- Understanding the data needed to support the use cases

- Organizational structuring considerations for big data

Chapter 4: Big Data – Challenges

- Skills gap

- The need for data governance

- Understanding data quality and big data

- The role of Master Data Management

- The big brother challenge

- Going beyond silos – how to integrate insights between big and small data

Chapter 5: Best Practices

- Debunking some common myths

- Executive sponsorship need; what must an executive sponsor do to ensure a data driven culture? CAO or CDO - is there a need? What are the similarities & differences?

- Is the DW dead with the advent of big data? What happens to my existing analytics?

- Big data and the cloud, an introduction

- Best Practices to ensure success

Chapter 6: Machine Learning and Healthcare - the Big Data Connection

- What is AI? What is ML? How are they related to data mining & data science? Can we demystify the terminology?

- Real life examples from outside healthcare - Netflix, Amazon, Siri, etc

- What does it mean for healthcare? Why should you care? State of the industry.

- Inductive v Deductive v Other reasoning - an introduction and why should we care?

- Types of Machine Learning - what are learning algorithms?

- Supervised, unsupervised, semi-supervised, reinforcement with some discussion. What is deep learning?

- Popular algorithms and how they are used

- Computational biomarkers, data charting, visualization - a discussion in context

- Representative use cases in healthcare

- Medical imaging ML & imaging biomarkers for Traumatic brain injury - UCSF

- Population Health: ML for diabetes prediction

- Cardiology predictive analytics - Stanford

Chapter 7: Advanced Topics

- Unstructured data & text analysis: NLP

- The learning organization and knowledge management

Chapter 8: Case Studies from healthcare organizations

- MD-Anderson Cancer Center

- Penn OMICS

- CIAPM -

- Ascension case study

- Deloitte case study

Appendix A. Big data technical glossary

Über Autor(innen)

Prashant Natarajan Iyer is Product Director of Healthcare Solutions at Oracle in the Health Sciences Global Business Unit. He has portfolio responsibility for precision medicine, population health, translational research, and convergence products. He is passionate about helping healthcare organizations maximize their technology investments to improve patient care, provider satisfaction, personal wellness, and health policy. Prior to joining Oracle in 2008, Prashant contributed in progressive career roles as product manager, emerging technologies specialist, and consultant at Healthways, McKesson, Siemens, and eCredit.com.

Prashant received his master’s degree in technical communications and linguistics from Auburn University (2005) and his undergraduate degree in chemical engineering from Mangalore University (1999). He is also a Stanford Certified Project Manager. Prashant is author or contributing author of three books on healthcare informatics, including this one. Others are Multi-Disciplinary Approach to Head and Neck Cancer (2017) and Implementing BI in your Healthcare Organization (2012).

Prashant is Industry Advisor for Data Science and AI at UCSF/Center for Imaging of Neurodegenerative Disease in the San Francisco VA Center. He volunteers on the Board of Advisors for the Council for Affordable Health Coverage, Washington DC, and is currently serving as Co-Chair of HIMSS NorCal’s Innovation Committee. Prashant lives in Livermore, CA, with his wife, Vishnu; daughter, Shivani; and Australian Cattle Dog, Simba.

John Frenzel, MD, is the Chief Medical Informatics Officer at MD Anderson Cancer Center and a Professor in the Department of Anesthesiology and Perioperative Medicine. He received his medical degree from Baylor College of Medicine and completed his fellowship training in Cardiovascular and Thoracic Anesthesia at the Mayo Clinic in Rochester, Minnesota.

In 2001, he received a Master’s Degree in Informatics from the University of Texas Health Science Center Houston, School of Information Science. Dr. Frenzel has been active in applied Informatics throughout his career at MD Anderson.

In addition to several clinical leadership roles, in 2010 he was asked to lead the development and installation of MD Anderson’s third-generation Clinical Data Warehouse, which sought to bring together all institutional clinical and genomic data. In 2012, he was asked to help lead the Institution’s effort to install the Epic EHR and integrate clinical data back into the institutional warehouse.

John has published on various topics pertaining to clinical informatics. He is currently focused on the use of Time-Driven Activity-Based Costing (TDABC) to drive hospital revenue process optimization and labor costing efforts in preparation for bundled payments in oncology care. He is Board certified in both Anesthesiology and Informatics. John lives in Houston, Texas.

Herb Smaltz is the Founder, President, and CEO of CIO Consult, LLC, a strategic IT consulting firm. Prior to founding CIO Consult, Herb founded Health Care DataWorks, a healthcare business intelligence software company that earned the distinction of being a Gartner "Cool Vendor" prior to being acquired in 2015. Prior to his consulting and entrepreneurial career, Herb served as the CIO of the Ohio State University Wexner Medical Center, a $1.7B, six-hospital academic medical center comprising more than 1100 beds and over 13,000 FTEs. Herb has over 25 years of experience in healthcare management, with all but four of those years as CIO/CKO at various sized organizations including a 20-bed community hospital, a 300-bed tertiary referral medical center, an 1100-bed tertiary referral medical center, a five-state region, a seven-country international region; and at the corporate headquarters of a $6.2B globally distributed integrated delivery system.

Herb is a Fellow of the Healthcare Information & Management Systems Society (FHIMSS) and has served on the HIMSS Board of Directors from 2002–2005 and as the HIMSS 2004–2005 BOD Vice Chair. In addition, he is a Fellow in the American College of Healthcare Executives (FACHE).

His recent publications include Information Systems for Healthcare Management, 8th Edition, with Gerald Glandon and Donna Slovensky; The Healthcare Information Technology Planning Fieldbook, with George "Buddy" Hickman; The Executive’s Guide to Electronic Health Records, with Eta S. Berner; and The CEO-CIO Partnership: Harnessing the Value of Information Technology in Healthcare, Smaltz, D., Glaser, J., Skinner, R., and Cunningham, T., III, eds.