by Tomer Amit rtificial intelligence (AI) and machine learning are set to revolutionize the healthcare market, across mu
Volume 4, Issue 14
July Second Edition 2017
The Next Wave of Innovation: Using Machine Learning to Build a More Efficient Healthcare System by Tomer Amit rtificial intelligence (AI) and machine learning are set to revolutionize the healthcare market, across multiple vectors and in ways that are still challenging to comprehend. While there is already talk of machine learning algorithms replacing doctors—and some AI systems are even being developed to assume the roles of clinicians1—the reality of computer systems completely replacing human physicians remains, at best, decades in the future and might never come to fruition. On the contrary, rather than replace physicians, machine learning will enhance their role as diagnosticians and caregivers, changing the nature of their profession—but by no means replacing it. Of perhaps most immediate significance is the use of powerful machine learning algorithms in identifying the relationships and insights that might be residing in very large amounts of data. Based on—but not restricted by—traditional statistics, these systems are capable of digesting and “learning” from vast amounts of data, identifying patterns to provide actionable insights which would be difficult if not impossible for humans to recognize.2 Such systems can detect complex patterns, trends and interrelationships within medical data to help predict the likelihood of an individual developing specific diseases and the effectiveness of potential treatment plans. Big Data: An Unprecedented Opportunity
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Paramount to the effectiveness of machine learning in healthcare is access to large amounts of reliable data. In the preceding decades, vast amounts of information have been collected but have remained largely dispersed and unusable, lacking the necessary analytical tools and computing capabilities. Digitization and computing have now sufficiently advanced to enable users to understand different subtle trends and patterns that occur within data; however, this is only possible with incredibly large amounts of information. Without it, such insights would arise only infrequently, and it would be extremely difficult to generalize and understand stronger patterns. Applying AI to big data can provide the ability to select what works, reject what doesn't and recommend different protocols that can only be seen in very large amounts of data and from cases that the average physician might never have encountered. Thus, physicians can benefit from what can be seen within data, interpreting them to be more decisive or to provide additional information from thousands of previous cases where only a computer can find semblance. This is the opportunity that lies ahead. Applying New Techniques to Extract the Greatest Value The interpretation of known factors with computers makes it possible to look at subtle relationships and trends in a way that humans would otherwise struggle to infer. This is perhaps most evident in the move from hypothetical- to empiricalbased medical research. Traditionally, research in healthcare has been hypothesis-based, whereby a theory is raised on the supposition that certain conditions will result in a specific outcome. A study is then designed to either confirm or refute the proposed hypothesis. Machine learning enables an empirical approach to research, in which everything is inferred from data. Rather than rely on a hypothesis, one looks instead to data, performing multiple manipulations to enrich them and create more information. One such example concerns imaging, in which a computer can infer different concepts from an image to identify abstract patterns. These can then be applied to see if they correlate in a statistically valid way to the outcomes. Such methods facilitate the creation of increasingly complicated structures, which can associate with ever more outcomes. Utilizing large enough amounts of data, computer systems can find in minutes and hours what would take humans a lifetime to identify. The ultimate question is whether the numerous and disparate pieces of information contained in medical data could explain things that are happening within one’s own body. This provides the perfect opportunity for machine learning to discover relationships and behaviors that are manifested in data and could be strongly correlated to what is happening. For example, when examining people with a specific outcome, it is possible to identify if certain proteins have a higher representation in said individuals and if these increased over time. This and more can be inferred from data. However, as promising as it may be to process such new information about a human body, this can only be achieved if there is enough data available and the machine learning techniques to analyze them. When it comes to predictive healthcare, one need not wait for new and emerging data, which are not yet widely available and “Machine learning enables an empirical approach to research, in which everything is inferred from data. Rather than rely on a hypothesis, one looks instead to data, performing multiple manipulations to enrich them and create more information.”
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Healthcare Innovation News Healthcare Innovation News July Second Edition 2017, Volume 4 Issue 14 ISSN Print 2372-1553 ISSN Electronic 2372-1561
Editorial Advisory Board Sam Basta, M.D., MMM, FACP, CPE Senior Medical Director, Clinical Integration, Sentara Healthcare/Optima Health Plans Founder and President, Healthcare Innovation by Design, Virtual Chief Innovation and Medical Officer, Venture Partner, Virginia Beach, VA
Lyle Berkowitz, M.D. Associate Chief Medical Officer of Innovation and Primary Care Physician, Northwestern Memorial Healthcare; Director, Szollosi Healthcare Innovation Program; Founder/Chairman, healthfinch; Associate Professor, Clinical Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL
Glenn D. Braunstein, M.D. Vice President, Clinical Innovation, Cedars-Sinai Health System; Director, Cedars-Sinai Thyroid Cancer Center; James R. Klinenberg, M.D. Chair in Medicine, Cedars-Sinai Medical Center Los Angeles, CA
Joanne Conroy, M.D. Chief Executive Officer Lahey Hospital & Medical Center Burlington, MA
Molly Joel Coye, M.D., MPH Social Entrepreneur in Residence, NEHI, San Francisco, CA
Wendy Everett, Sc.D. CEO of NEHI; Former Director, Institute for the Future, Cambridge, MA
Roy M. Rosin, MBA Chief Innovation Officer, Penn Medicine’s Center for Innovation, University of Pennsylvania Health System, Philadelphia, PA
Larry Stofko, M.S. Executive Vice President, The Innovation Institute, St. Joseph Health System, Orange County, CA; Bon Secours Health System, Marriottsville, MD
Publisher Clive Riddle, President, MCOL Editor Mari Edlin Healthcare Innovation News 1101 Standiford Avenue, Suite C-3 Modesto CA 95350 Phone: 209-577-4888
[email protected] www.healthcareinnovationnews.com Healthcare Innovation News is published monthly by Health Policy Publishing LLC. Newsletter publication administration is provided by MCOL. Copyright 2017 by Health Policy Publishing LLC. All rights reserved. No part of this publication may be reproduced or transmitted by any means, electronic or mechanical, including photocopy, fax or electronic delivery, without the prior written permission of the publisher.
July Second Edition 2017
may not be for some time to come. Existing electronic medical records (EMRs), implemented for approximately the last 15 years, can provide necessary information, indicating with better accuracy and yield those at highest risk of certain outcomes; picking up subtle signals before they reach those thresholds set by physicians or healthcare systems; and examining the trends and combinations of parameters, which may still be close enough to the norm as not to raise alarm. This “connecting of the dots” could aid physicians in finding individuals who may be asymptomatic. A Glimpse Into the Future: the Potential Impact Potential benefits of applying machine learning to healthcare are extensive. From an economic perspective, significant savings could be achieved through reduction of errors—the bane of every health provider. Organizations would be able to significantly reduce considerable resources currently invested in dealing with errors, such as premature discharge of patients at high risk of returning to the system and additional costs incurred by having to treat them again. Physicians also could save time and resources using machine learning, which could provide algorithms to predict the effects of certain medications and how long workers might have to take off work until they balance their medications. The biggest potential impact of machine learning will be in preventive healthcare. Rising-risk patients of today will become the high-risk patients of tomorrow, accounting for approximately half of the nation’s entire healthcare spending.3 Machine learning can effectively identify those at highest risk of developing or harboring specific diseases. Early identification could lead to early diagnosis, paving the way for interventions that would be of most benefit to patients and preventing the complicated outcomes that would incur multiple costs downstream. In addition, the application of machine learning could assist physicians in considering other options, thus avoiding clinical and economic consequences of misdiagnosis. While humans have the tendency to narrow down a judgment to the exclusion of other possibilities, mathematical algorithms could assist in suggesting alternative options to consider before settling on a prognosis.
“The biggest potential impact of machine learning will be in preventive healthcare. Rising-risk patients of today will become the high-risk patients of tomorrow, accounting for approximately half of the nation’s entire healthcare spending.”
Another significant aspect is the humanitarian potential of machine learning in reducing inequality in healthcare, particularly in rural areas. Such locations may lack experienced physicians and access to hospitals with optimal facilities. A flagging system could help to ensure that far fewer cases are missed, while a newly qualified radiologist, lacking proximity to peers with whom to consult, could run images through a computer to identify issues which a human would struggle to see. That same system can then trawl through masses of research literature to suggest a treatment plan tailored to a patient’s specific requirements.4 Climbing the AI Mountain The most critical hurdle for integration of AI into day-to-day medical practice is for physicians to start accepting the addition of machine learning technology into their decision process. While other industries—notably retail, entertainment and finance—dove wholeheartedly into the deep end of big data analytics, healthcare remains paddling in the shallows. Machine learning could provide clinicians with enhanced analytic and decision-making tools resulting in more efficient and personalized care. The first implementations will naturally be in those areas of mitigated risk, where gaps are noticeable and achievements will be most evident. Ultimately, machine learning will come down to leveraging vast amounts of healthcare data which, lacking standardization, range across multiple platforms including physician notes, electronic images and information from an array of monitoring devices. In addition, it will require collaboration. Disparate datasets, dispersed across multiple platforms and organizations, must be securely collated and connected if they are to ultimately improve diagnostics, decision making and overall care.5
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Healthcare Innovation News
July Second Edition 2017
With the advent of machine learning, an exciting transformation is indeed underway, one which will result in a more effective, cohesive and equal healthcare system. As with most technological advances, it is the early adopters who will see the biggest returns on their investment in the form of more personalized, proactive and preventive care. 1
Darcy AM, Louie AK, Roberts LW. “Machine Learning and the Profession of Medicine.” Journal of the American Medical Association. Feb. 9, 2016;315(6):551. Pyle D, San Jose C. “An Executive’s Guide to Machine Learning.” McKinsey Quarterly. June 2015:3-4. 3 Zubin JE, Sachin HJ. “Redesigning Care for High-Cost, High-Risk Patients.” Harvard Business Review. Feb. 7, 2017. 4 Marr B. “How Machine Learning, Big Data and AI are Changing Healthcare Forever.” Forbes. Sept. 23, 2016. 5 Shah ND, Pathak J. “Why Health Care May Finally be Ready for Big Data.” Harvard Business Review. Dec. 3, 2014. 2
Tomer Amit is vice president of corporate marketing for Medial EarlySign, a developer of machine learning tools for data-driven medicine. He may be reached at
[email protected].
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