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Health Informatics

Homero Rivas Katarzyna Wac Editors

Digital Health Scaling Healthcare to the World

Health Informatics

This series is directed to healthcare professionals leading the transformation of healthcare by using information and knowledge. For over 20 years, Health Informatics has offered a broad range of titles: some address specific professions such as nursing, medicine, and health administration; others cover special areas of practice such as trauma and radiology; still other books in the series focus on interdisciplinary issues, such as the computer based patient record, electronic health records, and networked healthcare systems. Editors and authors, eminent experts in their fields, offer their accounts of innovations in health informatics. Increasingly, these accounts go beyond hardware and software to address the role of information in influencing the transformation of healthcare delivery systems around the world. The series also increasingly focuses on the users of the information and systems: the organizational, behavioral, and societal changes that accompany the diffusion of information technology in health services environments.

Developments in healthcare delivery are constant; in recent years, bioinformatics has emerged as a new field in health informatics to support emerging and ongoing developments in molecular biology. At the same time, further evolution of the field of health informatics is reflected in the introduction of concepts at the macro or health systems delivery level with major national initiatives related to electronic health records (EHR), data standards, and public health informatics.

These changes will continue to shape health services in the twenty-first century. By making full and creative use of the technology to tame data and to transform information, Health Informatics will foster the development and use of new knowledge in healthcare.

More information about this series at http://www.springer.com/series/1114

Homero Rivas • Katarzyna Wac Editors

Digital Health

Scaling Healthcare to the World

ISSN 1431-1917 ISSN 2197-3741 (electronic) Health Informatics ISBN 978-3-319-61445-8 ISBN 978-3-319-61446-5 (eBook) https://doi.org/10.1007/978-3-319-61446-5

Library of Congress Control Number: 2017963147

© Springer International Publishing AG 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Editors Homero Rivas Stanford University School of Medicine Stanford, CA USA

Katarzyna Wac University of Copenhagen Copenhagen Denmark

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Foreword

Prepare for a new digital era of medicine. In 2017, millions of people are collecting their vital signs, such as blood pressure and respiratory rate, on everyday devices like smartwatches and iPhones. Surgeons are leveraging advanced robotics in the operating room and live-streaming their most challenging cases via virtual reality headsets. Primary care practitioners are capturing patient data in real time without glancing once at a screen, thanks to their augmented reality “smart” glasses. And medical records, once stacked in filing cabinets at hospitals and clinics across the country, are now being stored electronically (http://www.modernhealthcare.com/ article/20160531/NEWS/16053999).

Many of these technological advancements were subsidized into existence through the major health reforms of the past decade, which should not be overlooked—notably the Affordable Care Act and the Health Information Technology for Economic and Clinical Health (“HITECH Act”). These legislative changes inspired venture investors in Silicon Valley and other tech hubs to open their checkbooks to health technology entrepreneurs, and for the world’s most valu- able companies like Apple, Amazon, and Google to begin eyeing opportunities in the $3 trillion medical sector for the first time. Healthcare is an “enormous” oppor- tunity, Apple chief executive Tim Cook recently told the television news network CNBC in a revealing interview. “You can have patients that really feeling like cus- tomers… and can have systems and applications that bring out the best in medical professionals.” Imagine a health system that could deliver an experience on par with one that consumers expect in every other industry from retail to financial services.

But before all this technology can deliver on its potential to transform the health experience for the better, a deeper change is required. Incentives need to shift from older financial models that reward hospitals and clinics for expensive procedures and tests, rather than on keeping their patients healthier for longer. The United States spends twice as much as any other developed country on healthcare, but this investment has not resulted in improved health outcomes (http://www.pbs.org/new- shour/bb/u-s-pays-health-care-rest-world/). This nation surpasses the rest of the

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world on cutting-edge research and basic science, but it has failed to provide a path for ordinary Americans to access these innovative therapies at an affordable price tag. Former US Vice President Joe Biden considered selling his home to pay for his son’s cancer treatment. If the country’s leaders can barely afford life-saving treat- ment, just imagine the plight faced by ordinary Americans.

Shifting these incentives will be the task of policymakers, but it also presents an opportunity for the exploding crop of health technology start-ups in Silicon Valley and beyond. The emerging category known as digital health, which broadly refers to the convergence of digital tools with health and healthy living, raised a mammoth $4.2 billion in 2016 alone (https://rockhealth.com/reports/2016-year-end-funding- report-a-reality-check-for-digital-health/). Other upcoming areas include digital therapeutics, which involve computer-based interventions to replace or augment drugs, and computational biology, such as machine learning tools to parse through miles of medical images and scans.

Many of these companies make their money by propping up the status quo. But a select few are attempting to forge a new path, that is, to down health costs by pro- viding people with digital services to manage their own care preventatively and to avoid expensive medicines and emergency room visits. Such companies are produc- ing simple apps and messaging tools that are designed to provide pertinent health information to low-income communities that lack reliable access to care. Or the companies that are connecting people in rural areas, located many miles from a hospital, with a new way to consult with a physician via video chat. A category called “liquid biopsies” are developing tests to screen for diseases like cancer that can be treated in the early stages. Vijay Pande, appointed to run the new bio fund for the well-known technology investment fund Andreessen Horowitz, has gone as far as to describe this whole transformation as the “industrial revolution for biology” (https://a16z.com/2015/11/18/bio-fund/).

Amid all this excitement, these technologies will need to be evaluated in three key ways: Can they improve overall health outcomes for patients, enhance the qual- ity of care, and reduce health costs? This framework for optimizing health system performance is known as the “triple aim.”

In healthcare, many new technologies will initially add cost to the system. But the hope is that such advancements are laying the groundwork for potential cost savings. The promise of electronic medical record systems, for instance, is improved care coordination and disease management between physicians and their patients, as well as reduced errors. But before that dream can become a reality, it will need to be far easier for these electronic medical record systems to aggregate and share data.

Indeed, the next phase of medicine will require integration or interoperability of health information in support of a new style of medicine based on data and evi- dence. Some of the world’s most valuable companies, including Apple, Amazon, and Google, have all taken on this challenge in different ways. These companies are betting on health hardware, such as wearable technologies and medical devices, machine learning and artificial intelligence as applied to medical specialties like radiology, telemedicine or virtual care, and software tools for users to view their personal medical information. However, before any of these services are truly valu-

Foreword

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able, it will be necessary to aggregate medical information from charts, labs, devices, health apps, and so on.

From Silicon Valley to Washington D.C. and beyond, a movement is underway backed by government officials, nonprofits, and patient advocates for patients to access their medical information in a user-friendly format. One of the most successful efforts is a nonprofit organization called OpenNotes, which advocates for patients to access their physicians’ notes. Despite ongoing resistance from the medical community that patients would misinterpret this information, some 14 million people have accessed these notes electronically—with little confusion and few mishaps (https://patienten- gagementhit.com/news/using-opennotes-for-positive-impact-on-patient-data-access).

The winners that emerge in healthcare in the coming years have a choice: Do they build tools for healthcare as it is today? Or are they building for a future that is both patient-centered and evidence-based? The latter option represents a windier, longer, and more challenging path, but it’s the right one.

CNBC, San Francisco Christina Farr CA, USA

References

http://www.modernhealthcare.com/article/20160531/NEWS/16053999. http://www.pbs.org/newshour/bb/u-s-pays-health-care-rest-world/. https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-

digital-health/. https://a16z.com/2015/11/18/bio-fund/. https://patientengagementhit.com/news/using-opennotes-for-positive-impact-on-patient- data-access.

Foreword

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Contents

1 Creating a Case for Digital Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Homero Rivas

2 Mobile Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Lavanya Vasudevan, Kelsey Zeller, and Alain Labrique

3 Redesigning Healthcare Systems to Provide Better and Faster Care at a Lower Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 J.P. van der Heijden and L. Witkamp

4 Patient-Centric Strategies in Digital Health . . . . . . . . . . . . . . . . . . . . . 43 Larry F. Chu, Ashish G. Shah, Dara Rouholiman, Sara Riggare, and Jamison G. Gamble

5 Informatics and Mass Data Analysis in Digital Health . . . . . . . . . . . . 55 Nick van Terheyden

6 “Healthcare on a Wrist”: Increasing Compliance Through Checklists on Wearables in Obesity (Self-)Management Programs . . . . . . . . . . . 65 Thomas Boillat, Homero Rivas, and Katarzyna Wac

7 From Quantified Self to Quality of Life . . . . . . . . . . . . . . . . . . . . . . . . 83 Katarzyna Wac

8 3D Printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Michael Gelinsky

9 Augmenting Behavioral Healthcare: Mobilizing Services with Virtual Reality and Augmented Reality . . . . . . . . . . . . . . . . . . . . 123 Brenda K. Wiederhold, Ian Miller, and Mark D. Wiederhold

10 How Serious Games Will Improve Healthcare . . . . . . . . . . . . . . . . . . . 139 Maurits Graafland and Marlies Schijven

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11 Drones in Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Sharon Wulfovich, Homero Rivas, and Pedro Matabuena

12 Digital Health and Obesity: How Technology Could Be the Culprit and Solution for Obesity . . . . . . . . . . . . . . . . . . . . . . . . 169 Matthew Cooper and John Morton

13 Engaging a Digital Health Behavior Audience: A Case Study . . . . . . 179 David Bychkov and Sean D. Young

14 How Digital Health Will Deliver Precision Medicine . . . . . . . . . . . . . 189 Pishoy Gouda and Steve Steinhubl

15 The Digital and In Silico Therapeutics Revolution . . . . . . . . . . . . . . . 197 Carolina Garcia Rizo

16 Biodesign for Digital Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Bronwyn Harris, Lyn Denend, and Dan E. Azagury

17 Enhancing Clinical Performance and Improving Patient Safety Using Digital Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Mitchell G. Goldenberg and Teodor P. Grantcharov

18 The Evolving Law and Ethics of Digital Health . . . . . . . . . . . . . . . . . . 249 Nathan Cortez

19 Digital Health Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Hubert Zajicek and Arlen Meyers

20 Who Will Pay for Digital Health? The Investor Point of View . . . . . . 289 Mussaad Al-Razouki

21 An Education in Digital Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Carlo V. Caballero-Uribe

22 Future Directions of Digital Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Bertalan Mesko

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365

Contents

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Contributors

Mussaad Al-Razouki Kuwait Life Sciences Company, Sharq, Kuwait

Dan E. Azagury Stanford University School of Medicine, Stanford, CA, USA

Stanford Byers Center for Biodesign, Stanford, CA, USA

Thomas Boillat Stanford University, Stanford, CA, USA

University of Lausanne, Lausanne, Switzerland

David Bychkov InHealth, Johns Hopkins University, Baltimore, MD, USA

Carlo V. Caballero-Uribe Associated Professor of Medicine, Universidad del Norte, Barranquilla, Colombia

Larry F. Chu Stanford School of Medicine, Stanford, CA, USA

Matthew Cooper Stanford University School of Medicine, Stanford, CA, USA

Nathan Cortez Dedman School of Law, Southern Methodist University, Dallas, TX, USA

Lyn Denend Stanford Byers Center for Biodesign, Stanford, CA, USA

Jamison G. Gamble Stanford School of Medicine, Stanford, CA, USA

Michael Gelinsky Centre for Translational Bone, Joint and Soft Tissue Research, University Hospital and Medical Faculty, Technische Universität Dresden, Dresden, Germany

Mitchell G. Goldenberg Keenan Centre for Biomedical Science, International Centre for Surgical Safety, St. Michael’s Hospital, University of Toronto, Toronto, ON, Canada

Pishoy Gouda Division of Internal Medicine, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada

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Maurits Graafland Department of Surgery, Academic Medical Centre, Amsterdam, The Netherlands

Teodor P. Grantcharov Keenan Centre for Biomedical Science, International Centre for Surgical Safety, St. Michael’s Hospital, University of Toronto, Toronto, ON, Canada

Bronwyn Harris Stanford University School of Medicine, Stanford, CA, USA

J.P. van der Heijden Research and Development, KSYOS TeleMedical Center, Amsterdam, The Netherlands

Alain Labrique Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Global mHealth Initiative, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Pedro Matabuena Unmanned Aerial Vehicle Systems, Instituto Tecnológico Autónomo de México and Aidronix, CDMX, Mexico

Bertalan Mesko Semmelweis University, Budapest, Hungary

Arlen Meyers Society of Physician Entrepreneurs, South Norwalk, CT, USA

Ian Miller Virtual Reality Medical Center, Interactive Media Institute, San Diego, CA, USA

John Morton Stanford University School of Medicine, Stanford, CA, USA

Sara Riggare Karolinska Institutet, Stockholm, Sweden

Homero Rivas Stanford University School of Medicine, Stanford, CA, USA

Stanford University, Stanford, CA, USA

Carolina Garcia Rizo Roche Molecular Systems, San Francisco, CA, USA

Dara Rouholiman Stanford School of Medicine, Stanford, CA, USA

Marlies Schijven Department of Surgery, Academic Medical Centre, Amsterdam, The Netherlands

Ashish G. Shah Stanford School of Medicine, Stanford, CA, USA

Steve Steinhubl Scripps Translational Science Institute, La Jolla, CA, USA

Nick van Terheyden Gaithersburg, MD, USA

Contributors

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Lavanya Vasudevan Center for Health Policy and Inequalities Research, Duke Global Health Institute, Durham, NC, USA

Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Global mHealth Initiative, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Katarzyna Wac Stanford University, Stanford, CA, USA

Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

Quality of Life Technologies Lab, University of Geneva, Geneva, Switzerland

Brenda K. Wiederhold Interactive Media Institute, Virtual Reality Medical Center, San Diego, CA, USA

Mark D. Wiederhold Interactive Media Institute, Virtual Reality Medical Center, San Diego, CA, USA

L. Witkamp Department of Medical Informatics, KSYOS TeleMedical Centre, Academic Medical Centre Amsterdam, Amsterdam, The Netherlands

Sharon Wulfovich Stanford University, Stanford, CA, USA

Sean D. Young Department of Family Medicine, Center for Digital Behavior, University of California Institute for Prediction Technology, University of California, Los Angeles, CA, USA

Hubert Zajicek Health Wildcatters, Dallas, TX, USA

Kelsey Zeller Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Global mHealth Initiative, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Contributors

1© Springer International Publishing AG 2018 H. Rivas, K. Wac (eds.), Digital Health, Health Informatics, https://doi.org/10.1007/978-3-319-61446-5_1

Chapter 1 Creating a Case for Digital Health

Homero Rivas

Abstract The central paradigm in medicine is based on the patient–provider rela- tionship. In these times, previously unheard diseases are being described every day while novel therapies for previously uncured diseases are introduced along with novel state-of-the-art diagnostic and therapeutic technologies. These develop- ments alter the patient–physician relationship, which has remained largely unchanged for thousands of years. Digital Health represents an evolutionary adap- tation of the art and science of medicine to pervasive information and communica- tion technologies (ICTs). Without a doubt, this represents a phenomenal opportunity for us to scale access to care to any area in the world where connectiv- ity may be available. This chapter reviews the ways that healthcare has evolved and its conceivable opportunities, challenges, and socioeconomic consequences.

Keywords Digital Health • Medicine • ICTs • Patient–Provider Relationship • Social Media • Wearables • 3D Printing • Augmented and Virtual Reality • Economics

1.1 Evolution of Medicine and Delivery of Healthcare

The practice of medicine goes back thousands of years. There is enough evidence to show that stone-age humans practiced some type of medicine and even developed primitive instrumentation to perform surgery such as cranial trepanation (Fig. 1.1). While modern medicine and surgery have evolved dramatically during the last hun- dred years, with many breakthroughs such as antisepsis, anesthesia, analgesia, anti- biotics, endoscopic, robotic and even scar-less surgery among many others, the

H. Rivas, M.D., M.B.A. Stanford University School of Medicine, 300 Pasteur Ct, Suite H3680H, Stanford, CA 94305, USA e-mail: hrivas@stanford.edu

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essence of the business model of medicine has changed very little if any (Neuburger 1910; Kelly et al. 2003; Schlich 2007). The practice of medicine remains very arti- sanal, requiring at least a one-to-one ratio of medical provider to patient for a single medical encounter, thus preventing the scalability of healthcare delivery. With higher standards of care almost universally available and resulting longer life spans and prevalence of more chronic diseases, there is a shortage of medical providers for the continuously larger surplus of patients (Petterson et al. 2012; Sheldon et al. 2008). Conventional medicine, unlike technology industries such as software, hard- ware, semiconductors, microcontrollers, etc., cannot scale production from day to day. Conversely, if a company such as Google or Facebook decides to change basic or complex software algorithms, a logo design, color, fonts, etc., they can do it immediately and have an impact on masses of users (Rogers 2003; Moore 2014). On the other hand, practicing medicine usually relies on one-to-one patient−pro- vider encounters/relationships; hardly scalable if any potential implementation would be needed to include large groups of people. Medicine itself can only scale to a degree by medical education, rendering new doctors who will see more people, or by implementation of public health through preventive medicine strategies. Both efforts will still have severe constraints and neither can achieve the technological scalability of the industries described before.

Single Port Laparoscopy

Laparoscopy

Open Surgery

Technology and Innovation

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en ts

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Evolution of Surgery

Endoluminal Surgery

Fig. 1.1 While medicine and other specialties such as surgery have evolved dramatically, the prac- tice of medicine cannot be scaled as it still depends on a one-to-one patient-provider relationship

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1.2 Digital Health as an Opportunity and Challenge in the Twenty-First Century

Digital health bases itself on the implementation and leverage of information and communication technologies (ICTs) to deliver and scale healthcare to the masses. Throughout this book, we will discover many of the technologies that are being implemented with great success in healthcare to make this a reality.

Presently in the USA, nearly 20% of the gross domestic product is used for health- care. This represents more than three trillion US dollars per year spent for healthcare (Centers for Disease Control and Prevention n.d.; Centers for Medicare and Medicaid Services 2015). Certainly this will not be sustainable in the near future unless cost containment strategies are widely implemented. During the recent past, former US Secretary of Health and Human Services, Kathleen Sebelius, referred to mHealth as “the biggest technology breakthrough of our time” and maintained that its use would also “address our greatest national challenge” (Sebelius 2011; Steinhubl et al. 2013). Without a doubt, this is not only applicable to the USA, but also to the rest of the world. Nathan Cortez et al. recently published a review of the FDA regulation on mobile healthcare technologies where they identified at least 97,000 available health apps (Cortez et al. 2014). This number has grown exponentially over the last couple of years to be approximately 250,000 health apps available online and/or in the healthcare market (McCarthy n.d.). Unfortunately, this truly represents an enormous challenge as the FDA has approved much less than 1% of those apps for clinical use. Furthermore, there is a forecast of 1.7–2 billion users of digital health by 2018 (Cortez et al. 2014). In addition, as with many other disruptive technologies, it is unclear if many have been responsibly created or if they are inclusive of all critical stakeholders in this market (care providers, patients, administrators, computer scien- tists, behavioral scientists, entrepreneurs, investors, etc.) as they likely are underrep- resented by patients and care providers or led by technologists and entrepreneurs. Historically there is a great disconnect between those two polarized groups of peo- ple, and while physicians claim to embrace innovation, their ecosystem has great limitations to innovate in comparison to technologists and others. In general, no for- mal medical school curriculum includes digital health, and physicians and healthcare systems would rarely embrace innovative ways to take care of patients due to a lack of scientific evidence, potential liability, and red tape among many others (Beck 2015; Asch and Weinstein 2014; Armstrong and Barsion 2013; Woods and Rosenberg 2016). The profile of a successful physician usually includes being extremely risk- averse and having a low tolerance for failure. Although not extensively talked about, physicians are known to engage in secrecy in research, cost insensitivity, and other behaviors. Conversely, very successful innovators and entrepreneurs (i.e., founders of major media conglomerates such as Google, Facebook, YouTube, etc.) have a very opposite profile of success to that of physicians. They usually have a high tolerance for failure, a great enthusiasm for risk, and embrace crowd-source collaboration, etc. (Rogers 2003; Moore 2014; Chamorro-Premuzic 2013.). Finding a middle ground to merge successful physician and innovator profiles into one represents a big and very

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ambitious challenge. However, once achieved, this could lead to the successful implementation of digital technologies in healthcare. For this to be sustainable, a culture of innovation must be nurtured to become pervasive throughout basic and advanced medical education curricula. Interestingly, while there are several hurdles for innovation adoption; including the nature of technologies themselves, regulation, cost, universal availability, etc., historically the biggest barrier is professional inertia. This is likely a result of a fixed mindset that most physicians have not to change the way they have learned and practiced medicine for a very long time. During the last few years, the widespread use of mobile phones, patient social communities, tele- medicine, consumer driven health, low-cost commercially available wearable tech- nologies, the Quantified Self movement, low-cost 3D printers, virtual and augmented reality, artificial intelligence engines, among several others are rapidly sculpting the way new generations will practice medicine and, certainly, how patient expectations will likely be in the near future (Sweeney 2011; Turner-McGrievy et al. 2013; Spring et al. 2017; McConnell et al. 2017; Spring et al. 2013; Case et al. 2015; Mackillop et al. 2014; Smith 2013; Turakhia and Harrington 2016; Sinnenberg et al. 2016; Eichstaedt et al. 2015; Patel et al. 2015a; Logghe et al. 2016; Flynn et al. 2017; Pew Research Center 2013; Chung et al. 2017; Farmer and Tarassenko 2015; Patel et al. 2015b; Bassett et al. 2010; Rosenberger et al. 2016; Walsh et al. 2014; Jakicic et al. 2016; Troiano et al. 2014; Shull et al. 2014; Schreinemacher et al. 2014; Pagoto et al. 2014; The Independent 2015; Zheng et al. 2016; Lim et al. 2016; Biglino et al. 2015; Randazzo et al. 2016; Giannopoulos et al. 2016; Wengerter et al. 2016; Burn et al. 2016; AlAli et al. 2015; Hong et al. 2017; Ng et al. 2016; Preis and Öblom 2017; Morrison et al. 2015; Wiederhold 2016; Lafond et al. 2016; Mosso-Vázquez et al. 2014; Wiederhold et al. 2014; Zhu et al. 2017; Bernhardt et al. 2017; Lyon 2017; Rochlen et al. 2017; LeBlanc and Chaput 2016; Lister et al. 2014; Esteva et al. 2017; Rumsfeld et al. 2016; He et al. 2017; Ashley 2015) (Figs. 1.2–1.5).

Throughout the world, digital health is being implemented in daily clinical prac- tice. From simple software algorithms utilized in feature phones to improve adher- ence to tuberculosis medication, to very interactive software applications used in smart phones to evaluate heart rhythm (Sweeney 2011; Turner-McGrievy et al. 2013; Spring et al. 2017; McConnell et al. 2017; Spring et al. 2013; Case et al. 2015; Mackillop et al. 2014; Smith 2013) (Fig. 1.6). The low cost of many of these digital health innovations makes them very attractive to emerging markets. In fact, most emerging markets commonly have prevalent needs and constraints that usually result in unique creativity (Lewis et al. 2012; The Economist 2010). The social and economic impacts that some of these digital health implementations could be dra- matic even in the developed world, like in the USA, where the medication adher- ence market represents at least 300 billion US dollars (P&S Market Research 2016). Even very modest mHealth strategies could have dramatic returns on investment. This has attracted many entrepreneurs to this market segment.

In addition, an overall lower cost of digital health technologies and less regulation in such emerging markets may result in a very fertile ecosystem for them to thrive and, thereby, expand and accelerate their adoption. The same has been experienced in other

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Fig. 1.2 This is an example of a very low cost cardboard device used with a mobile phone that allows a virtual reality experience. This can be used for teaching purposes on patients, students, providers, etc. Additionally it can be used to improve patient experience by distracting patients from an otherwise unpleasant experience

arenas, such as banking, where a few years ago, in places like Kenya, near to 80% of transactions were done by mobile phone versus in places like in the USA where they would have a market share less than 10% (The Economist 2010). Already in places like Gaza, innovators are using 3D printing to print very simple, low cost medical instruments and devices such as stethoscopes, needle drives, oxymeters, among others

Fig. 1.3 Augmented reality obtained through head mounted displays, merging reality and sus- pended holograms which can be interactive

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Fig. 1.5 Low cost 3D printers can print on low cost materials, highly functional prosthesis, which otherwise would have a prohibitive cost to many around the world. Social media and crowd-source learning platforms can be used to obtain free blue prints of such prosthesis

Fig. 1.4 3D Printing can produce low cost replicas of exact anatomical models used for teaching, simulation, surgical planning, among others. More costly materials can be used to print exact implants (i.e. joint implants, etc.)

(The Independent 2015). Presently, the USA and other developed countries are imple- menting very strict regulations to any 3D printing done for medical purposes, even when this might not even be bio-printing yet (Morrison et al. 2015). With no doubt, such regu- lations will maintain safety; however, they also may hinder innovation and rapid adoption.

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1.3 The Economics of Digital Health

In general, the economics of digital health seem very conducive for the universal adop- tion of many of its value propositions. Usually, for a given innovation to be massively adopted it must be simple in nature, simple to use, easily reproducible or scalable, cost- effective, make sense, have relative advantage(s), low cost, and be safe among other features (Rogers 2003; Moore 2014). In general, in conventional medicine, many of these features cannot be easily matched and often times, innovative diagnostic or thera- peutic modalities are complex in nature, not user-friendly or highly operator-depen- dent, of questionable value/benefit, and very expensive to say the least.

Economic opportunities have been already identified by major venture capitalists in healthcare as investment in digital health has dramatically peaked over the last few years. In the USA alone, about 55% of all digital health investments since 2011 have been in companies whose technologies interface with the consumer in some manner [76, 77]. This reflects the convergence of technologies to drive and measure improved health outcomes and cost savings, and funding has followed. In general, most stake- holders acutely identify great strengths and opportunities in less- regulated areas, such as fitness and wellness, through the implementation of numerous wearable devices that can monitor most body functions, vital signs, biometric parameters, physical activity, posture, etc. (Farmer and Tarassenko 2015; Patel et al. 2015b; Bassett et al. 2010; Rosenberger et al. 2016; Walsh et al. 2014; Jakicic et al. 2016; Troiano et al. 2014; Shull et al. 2014; Schreinemacher et al. 2014). By encouraging consumers and patients to change health-related behaviors through personal accountability, many propose their use is not only for prevention, but also for clinical diagnosis and man- agement of disease. This has been, in fact, the strategy that many have utilized to enter the medical market as it follows the shifts toward clinically driven consumer health

Fig. 1.6 This is an example of a low cost, FDA device and software, that allows to obtain and to share, medical grade EKG monitoring at anytime, anywhere. Additionally and through an artificial intelligence engine, it can assess for common hearth rhythm pathologies

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not only for prevention and wellness, but also (and very attractively) for the manage- ment of numerous chronic diseases (high blood pressure, diabetes, obesity, asthma, etc.). While entering a more regulated market represents the need for formal clinical studies, only a few highly compelling technologies have undertaken formal random- ized clinical trials. This would only lead to support from the medical community and more universal adoption if such studies show beneficial results. Clinically proven soft- ware and hardware would be integrated to drive better health outcomes and cost sav- ings not only in clinical care, but also in research and education.

Additionally, very innovative research is being done thanks to nearly universal access to information and communication technologies, through the use of crowd- sourced recruitment of patients, and/or crowd-sourced funding in research. Investigational technologies, such as the SCANADU™, have leveraged their micro- investor crowd base to also become investigational subjects once they have received their device for personal use (Fig. 1.7). Only then, and after signing an informed

Fig. 1.7 Innovative business models using crowd funding and micro investing are being success- fully used in digital health. Additionally some groups are using models of crowd source research, where all micro investors also become research subjects

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consent form to be part of the study, could they use such devices. This will crystalize into a complete clinical study lead by Scripps Clinic in San Diego, CA, USA, which may be finished soon. Even if such devices do not prove any individual clinical ben- efit to prevention, prompt diagnosis, and/or offer more efficient management of dis- ease, many of these digital health technologies can greatly improve the efficiencies and logistics of clinical research with great economic saving during conventional clinical trials. Often times, patients have to travel great distances just for simple evaluations done through interviews, basic assessments of physical signs and/or bio- metrics, and other methods. Many of these clinical parameters can be easily attained through telemedicine, medical grade wearable devices, or other means. Undoubtedly, digital health allows access not only to care but also to research of people even in remote locations.

1.4 Crowdsourcing Healthcare, Artificial Intelligence and Final Thoughts

Lastly, but perhaps of greater importance, digital health can be greatly utilized in educating patients, medical students, physicians, allied personnel, and also in communicating among themselves and with others. Crowd-sourced knowledge that patients share through online patient communities is truly priceless and was impossible to attain only a few years ago. Now through some of these communi- ties, patients suffering rare diseases can leverage on the experience of many other similar people throughout the world regarding symptoms, diagnosis, and treatment. The same can be experienced with widely prevalent diseases such as obesity, diabetes, etc. Most disease management and remote monitoring compa- nies are shifting their focus to specific diseases to help patients and providers better manage the condition as opposed to providing general solutions aimed at patients facing different diseases. In addition, many have proposed that through engines of artificial intelligence, algorithms could soon evaluate mass data and propose more educated diagnosis and treatment than what many experienced physicians could offer themselves.

Envisioning an ideal patient-centered framework, we could conclude that knowledge, engagement, and consumer friendliness can be improved through digital health. Providing ready access to education and relevant and personal- ized health information, could improve health literacy. Engaging and affecting behavioral change in healthcare consumers to better manage their own health could provide them with better tools to manage their health and wellness. Lastly, by improving consumer-friendliness, we could greatly improve consumers’ access to healthcare and their user experience. Patients’ choices can be improved and better price transparency is expected. User experience, in general, is critical when implementing any innovation universally as successful innovations must be easy to use, have great incentives—like improvement of health—and must make economic sense. Digital health shares all of these qualities.

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Chapter 2 Mobile Health

Lavanya Vasudevan, Kelsey Zeller, and Alain Labrique

Abstract Rapid innovations in digital communications technologies have fueled the use of mobile phones for delivering health services and information—a phe- nomenon termed mobile health (mHealth). Current mHealth strategies for health service delivery range from the implementation of simple text message reminders to complex clinical decision support algorithms, and extending in recent years to con- nect mobile phones to sensors and other portable devices for diagnosis at the point- of- care. This chapter summarizes the current state of mHealth, important strides that have been made in strengthening the global mHealth evidence base, and key ‘best practices’ in scaling mHealth for achieving universal healthcare.

L. Vasudevan Center for Health Policy and Inequalities Research, Duke Global Health Institute, Durham, NC, USA

Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Global mHealth Initiative, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

K. Zeller Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Global mHealth Initiative, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

A. Labrique (*) Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Global mHealth Initiative, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA e-mail: alabriqu@jhsph.edu, alabriqu@gmail.com

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Keywords Mobile health • mHealth • Digital health • 12 common mHealth and ICT applications • Universal health care

2.1 Introduction

No other technological innovation has diffused through human society as rapidly as mobile phones. Mobile-cellular network infrastructure has seen an exponential growth in the last decade, reaching almost 95% of the world’s population in 2016 (International Telecommunications Union 2016). Some of the most rapidly growing regions of mobile phone ownership and use are in the developing world, including countries in the Asian and sub-Saharan African continents. In concert with this growth in infrastructure, ownership, and use, the rapid evolution of mobile devices has fostered new opportunities to address information and communication challenges that previously did not exist (Qiang et al. 2012). While phone calls and short messag- ing service (SMS) continue to remain the most common modes of communication, mobile phones present a novel modality for internet access not previously possible in rural, hard to reach areas or for individuals without a means of accessing traditional fixed broadband connections. Currently, close to 3.6 billion people are anticipated to be reached by mobile internet services (International Telecommunications Union 2016). Massive infrastructural investments by mobile network operators in extending the reach of mobile network coverage, along with the accessibility, portability, and connectivity-on-the-go offered by mobile phones make them a widely-appealing communication medium for the delivery of information and services (World Health Organization 2009). Not surprisingly, several areas of innovation leveraging mobile phones have emerged in the last decade, including mHealth, mAgriculture, mGover- nance and mFinance (Kelly et al. 2012). Increasingly, the power of mobile network connectivity is being harnessed within these mDomains to improve service delivery, user experience, and coverage, supplementing the basic phone call and text messag- ing services utilized by individuals in their daily lives (Kelly et al. 2012).

One area where the utilization of mobile phones has garnered much attention is health care. The use of mobile phones to optimize the delivery and receipt of health information and services, also referred to as mobile health or mHealth, is innovative for several reasons. First, the ubiquity of mobile phones makes the concept of remote health care a viable and scalable reality. No longer is health care tethered to facilities as mHealth pushes these bounds further to the communities, and in many cases to the individual themselves. Unlike prior generations of digital innovation such as telemedicine and eHealth, there has been little to no investment by the Public Health community to build this global infrastructure. Second, the fact that most mobile phone owners carry the device with them where they go, we now have the unprecedented ability to deliver health services and information to individuals where they are and when they want or need it. Third, mobile phones have allowed users of healthcare to seek information and connect to providers with ease. In many

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developing countries, people are using mobile phones as the preferred medium to access the internet. Consequently, their ability to seek health information- on- demand is very high, even in the absence of formalized mHealth programs. As phones incorporate increasing computational power, while becoming cheaper and sleeker, the opportunities for health service delivery via these devices are tremen- dous. Current mHealth strategies for health service delivery range from the imple- mentation of simple text message reminders to complex clinical decision support algorithms, and extending in recent years to connect to sensors and other portable devices to aid diagnosis at the point-of-care (Labrique et al. 2013a).

In this chapter, we will describe the 12 key applications of mHealth that have categorized how this technology has been used in mitigating the key constraints to health systems. We will use real-world implementations of mHealth to illustrate how these technologies function across the three layers of healthcare, namely at the patient, provider and health system-level. We will briefly review the current evi- dence base and highlight areas where more rigorous evaluations are warranted to establish the impact of mHealth. Finally, we will close with recommendations for researchers new to mHealth on currently available resources to help plan research and implementation of these technologies.

2.2 mHealth and Its Public Health Appeal

Numerous constraints and barriers exist to providing high quality, accessible, and timely health services, especially in low-resource settings (Labrique et al. 2013a; Mehl and Labrique 2014; Agarwal et al. 2015). These health constraints impede optimal health promotion, diagnosis, and care, and can be described as barriers to (1) information, (2) availability, (3) quality, (4) acceptability, (5) utilization, (6) efficiency, or (7) cost related to health or health services (Mehl and Labrique 2014; Mehl et al. 2015). The “bottom billion”, representing the world’s poorest popula- tions, receives health care predominantly from low trained, non-facility based front- line health workers (Agarwal et al. 2015; Kallander et al. 2013). Equipping these frontline health workers with mHealth solutions helps bring these clients under the umbrella of the traditional health system, allowing them to be counted and enumer- ated, which builds accountability for frontline health workers to their supervisors. mHealth interventions capitalize on key features inherent in mobile technologies to bridge these constraints. In settings where women frequently give birth at home, the decision to seek medical help during delivery can be a difficult one (Kim et al. 2012; Kruk et al. 2016; Sikder et al. 2011). In many cases, women require family approval and input before such a decision is made. Even without the need for co-decision making, the choice to move to a health facility is complicated, weighing the poten- tial financial costs and/or difficulty of reaching the facility in light of the woman’s obstetric risks during childbirth (Sikder et al. 2014). mHealth interventions may act in several ways to reduce these barriers. In a more robust system, where frontline health workers have registered every pregnancy and are aware of impending births,

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they can be held accountable for attending these births, advocating for women, and helping the family make the decision when it is time to go to a health facility. Several mHealth interventions aim to compress this delay, using methods ranging from digi- tal population registries to SMS-based labor and birth notification (Kruk et al. 2016; McNabb et al. 2015). In the event an extensive registry system like this is not avail- able, provision of one simple thing-the emergency contact number of the designated frontline health worker to the woman and her family-enables the family to connect with a supportive decision-maker. Leveraging simple SMS-based delivery of health information leading up to childbirth about reasons for delays/danger signs can also help women and other key members of her family make a decision to seek medical attention in a timely manner (Lund et al. 2012).

2.3 The 12 Common mHealth and ICT Applications

The 12 common mHealth and ICT applications are currently the most widely adopted categorization of the ways in which mobile technologies are used for the delivery of health services and information (Fig. 2.1) (Labrique et al. 2013a).

The 12 applications are cross-cutting—extending across the three layers of the healthcare system—patient, provider and broader system. At the client level, there are extensive examples for the use of mHealth as a medium to deliver behavior change communication in a variety of health domains. Current implementations focus on leveraging simple communication modalities such as phone calls and text messaging to reach a broad audience—especially for those without access to smart- phone technologies and ‘apps’. Examples include the use of text messaging services or interactive voice response systems for the delivery of health information related to family planning, pregnancy and newborn care, immunizations, and management of chronic illnesses. In South Africa, the national Ministry of Health has capitalized