wh ch 6 piniewski final cut 20131028

Remaking of Medicine by Pervasive Technologies

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Chapter 6: Personalized Medicine and Public Health

Brigitte Piniewski, M.D.1

Chief Medical Officer, PeaceHealth Laboratories

Abstract—Science and technology are generating new medical breakthroughs routinely, yet an inescapable truth remains. The foundational health in almost every developed country is markedly eroding. Illnesses, once seen almost exclusively in old age, are now increasingly occurring in young people. Could it be our traditional, centuries-old, man-made medical models and economic systems are keeping us trapped in an underperforming paradigm? Once upon a time, this paradigm may have been inescapable as we lacked the tools necessary to drive better performance. Arguably today, new tools do exist, including wireless health, and driving optimal crowd health is indeed within our grasp.

Learning Objectives—By studying this chapter, the reader will:

x Become familiar with ‘Personalized Medicine’ and ‘Public Health’ as currently known and practiced, as well as appreciate the underlying assumptions that beg reassessment;

x Be able to use a basic modern health landscape to help contextualize strategic advances of personalized medicine and public health;

x Recognize why we need other people’s data;

x Recognize the urgent need to enable cross-generational collaboration models in order to support a community data commons model similar to our National Oceanic Atmospheric Administration (NOAA);

x Be able to describe concrete case examples using ‘community data commons’ as a foundation for monetization alliances and innovative business models; and

x Be able to articulate how regulatory agencies may transform from approving static solutions, to enabling dynamically improving solutions dependent upon continuous community input.

Outline: 1. Introduction 2. Personalized Medicine and Public Health as Currently Known and Practiced 3. The Modern Health Landscape: Disappearance of The Accidentally Well 4. Why There Is a Need for Other People’s Data 5. A Plausible Solution: Cross-Generational Collaboration 6. Household Data Trumps Institutional Data 7. Advancing the FDA and the Regulatory Framework 8. Safety and Security

1 The author is grateful for initial editing of this chapter by John Friesen.

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9. Recommendations for Next Steps 10. Summary and References


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1 Introduction

The U.S. is about to lose a national treasure. Within a few decades, we will lose the ‘last of the accidentally well’—a large population that acquired a level of health, fitness and cognitive performance that they did not expressly set about to earn. What they acquired came to them by virtue of the time in which they were born. At that time, parents had no need to understand what refined carbohydrates and trans-fatty acids were, or to worry about their children getting sufficient levels of activity. High calorie fast food was not yet ubiquitous, and children tended to play outdoors, rather than plunk themselves down in front of tablet screens, computer keyboards and game consoles. During the heyday of accidental wellness, perhaps prior to the 1980s, type II diabetes was relatively rare in the U.S. population. By 2010, it had risen to more than 8%, and may continue to climb [6-1]. Likewise, U.S. students were once consistently ranked in the top third, globally, in math and science. Now, there is grim evidence that they are losing significant ground academically.

http://www.all4ed.org/files/IntlComp_FactSheet.pdf

What does all this mean? It means the fast paced, ever-advancing modernization may inadvertently steer complex biological systems in undesirable directions. The current method of tracking and effectively managing human performance across large diverse complex systems has failed to adjust with the pace of change. Perhaps current business models, some of which are underpinned by the narrow pursuit of profit, may be woefully missing the broader societal impact they generate. If society is no longer accidentally well, the traditional ways of gathering and disseminating health, academic and other information may be primed for a serious upgrade—a necessity to ensure society accurately predicts and prevents impending, yet avoidable, adverse population trends. Society is primed to advance beyond the industrial era, through the information era, to embark on a new and prosperous future. Wireless health is a key foundational element of the fabric supporting this ever-improving future.

This chapter begins with 'personalized medicine' and 'public health' as currently known and practiced. Underlying assumptions are examined and a basic modern health landscape is proposed to help contextualize strategic advances in traditional personalized medicine and public health. With this foundation in place, a cross-generational collaboration model is proposed as a fundamental requirement of sustainable societies. The logical technical framework for this is introduced and illustrated with examples. The merits of this model extend to improving the regulatory responsibilities for groups such as the Food and Drug Administration (FDA), as well as reducing privacy and security challenges.

2 Personalized Medicine and Public Health as Currently Known and Practiced

Today, personalized medicine holds the promise to better tailor medical interventions to individual needs as defined by genomic and other types of data. In this way, medical practice goes beyond the days of treating everyone as a population or treating failing organ systems as if they were under identical influences in each patient. This approach takes advantage of predictive models that have grown out of previous health experiences and over time have an

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improved ability to separate out the prognosis and therapeutic benefit of alternative medical management. The examples cited often include tailored cancer treatment protocols based on genomic-derived predictions of whether or not an individual is more or less likely to respond favorably to a particular therapeutic option. Thus, patients avoid unnecessary exposure to the hazards of harmful agents with little or no therapeutic potential.

In addition, cost savings from avoiding potentially unsuccessful treatment can be important. The total global economic burden of cancer is estimated at approximately $900 billion. However, there is strong evidence suggesting a large portion of this cancer burden (at least one in three cases) appears preventable http://www.aicr.org/learn-more-about-cancer/infographics-economics.html?gclid=CPPQ8rjR77ECFWk0Qgod3WEA0w . More tailored and more effective cancer therapies will clearly offer some cost savings. However, a potential of carving one third or $300 billion out of the cancer bill through effective prevention strategies may provide much larger cost savings. However, the overwhelming emphasis on personalized medicine as a route to more precise and more effective care pathways is unlikely to advance effective prevention strategies.

Similarly, our traditional population health approach has merit, but is failing to keep up with the modern pace of avoidable adverse community health. Population health has been referred to as” the science and art of preventing disease, prolonging life and promoting health through the organized efforts and informed choices of society, organizations, public and private, communities and individuals.” http://en.wikipedia.org/wiki/Public_health

However gallant our traditional approaches to optimizing population health have been, we constantly fail in protecting the health outcomes of groups. We struggle to adequately and timely predict gross adjustments in adverse health trends, and incessantly fall desperately short in redirecting adverse health trends while they remain reversible. One example from the 1980’s, involved health experts recommending that the U.S. population adopt a ‘low fat’ diet. This was a simplistic notion, well received by the general population. For most families, it seemed reasonable to assume that less fat in the diet would result in less fat in the body. However, decades later, it became clear that the U.S. population failed to reap the intended health benefits. Each decade, rates of obesity and type II diabetes climbed across the U.S. http://www.cdc.gov/obesity/data/adult.html Obesity experts such as David Ludwig, of Boston Children’s Hospital, suggested that offering no recommendation at all may have resulted in better health outcomes than this well-intended, ‘low fat’ message. Evidently, people in the US found plenty of high-calorie, low-nutrition substitutes for fat.

Decades later, it became clear that a ‘good fat’ message may have been the preferred approach; but during the immediate aftermath of the “low fat” recommendation, the U.S. lacked a wireless health ecosystem that would have made rapid access to the relevant population warnings feasible. [6-2] Had communities been accustomed to using simple sensors and apps to track high yield health behaviors, we might have recognized that both weight and other metabolic parameters were moving in the wrong direction soon after the “low fat” message. Rapid community feedback via a wireless health ecosystem may have alerted a course correction.


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Clearly, U.S. obesity maps remain an ominous reminder of our decade’s long inability to achieve course correction http://www.ehd.org/health_obesity.php. Communities have enabled progressive development of obesity and in turn the secondary adverse health outcomes such as more type II diabetes, some cancers and more cardiovascular disease. The genetically stable population has adjusted the phenotype from previously lower rates of overweight and obesity to more than 67% overweight and obese within just four decades. This is cause for alarm. Clearly, traditional public health approaches are struggling to protect public health amidst a growing plethora of poor health choices. It may not be overstating the case to suggest that many developed nations today share a similar path and are all in desperate need of more effective tools.

Current concepts of personalized medicine and public health came about before there was an Internet to make possible globally widespread interconnections. Mobile communications make it possible for almost everybody to be in touch—anytime, anywhere. Thus personalized medicine and public health approaches are primed for a fundamental upgrade if rapid detection and timely prevention is to be achieved.

Purposeful strategies using wireless approaches (such as using wearable sensors) can, and likely will, move people beyond the current obsession with improving care pathways, to advancing effective preventive strategies that dramatically lower the collective burden of pre-illness among large populations. Discussed later in this chapter are wireless health cancer prevention strategies. These are good examples of how the data from large groups can deliver the scientific truths needed to eliminate unnecessary suffering and shrink the future burden of modifiable poor health outcomes. Wireless health will enable ‘high definition’ living—the ability to see at the pace of change relevant and hopefully correctable co-occurrences that are culminating towards future health experiences. With the robust ability to map event streams to future health outcomes, society will be able to proactively design optimal health journeys.

Until recently, it was assumed that an individual’s genetic disposition was largely responsible for oncological outcomes. Many citizens believed cancer “fell from the sky” and “afflicted” the unsuspecting victim. Increasingly, studies confirm that unhealthy behaviors may drive the prevalence of many cancers. For example, the cumulative burden of breast cancer in a population appears to be significantly driven by environmental factors.[6-3]-[6-7] Lifestyle choices, such as activity levels and nutrition can either ‘dial up’ or ‘dial down’ the future community burden of breast cancer. Communities going about their business of living without the benefits of monitoring important contributors by using wireless health tools may be blindly ‘dialing in’ unnecessarily excessive future rates of heart disease, diabetes, strokes, and many cancers.

The great promise of innovative approaches like wireless health is the possibility to reduce the disease burden within populations, in order to obtain the lowest achievable rates of disease within a free-living system. However, the move from digitizing care pathways to drastically reducing disease prevalence will require new societal, cultural and economic approaches to both personalized medicine and public health.

In short, the traditional medical model of reactive care is a relic. Transforming the health outcomes of crowds in the modern era of rapid change will require ultra-efficient means of

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avoiding the avoidable. It will require the task be spread beyond the health experts to the general citizens. Everyone will need to roll up their sleeves and participate in the quest to grasp reachable health outcomes. Wireless health can serve as an enabler for teaching and supporting this new skill. Simple distributed sensors, lightly instrumenting the communities, can advance personalized medicine beyond reactive care toward proactive, preventive personalized health. In this way, Public Health strategies will be able to advance rapidly. With citizens capable of co-locating the things that really matter throughout the decades of their lives, some may advance beyond being accidentally well to achieving levels of “super wellness” and “super cognitive” capacity. Before promising the moon, it will be necessary to lay out a reasonable path towards this future. The rest of this chapter seeks to effectively provide this pathway.

3 The Modern Health Landscape: Disappearance of The Accidentally Well

The traditional medical model assumed all people were well until diagnosed ill. In other words, individuals were free to exist within their communities, and as long as the basics (i.e., clean air, portable water and wholesome farm food) were available, individuals could expect to achieve their full potential in the form of academic achievement, suitable employment, social position, longevity and so on. Parents were not required to know how much exercise or what types of nutrition their children needed to reach their full potential—the ‘accidentally well’ society.

Those were blissful days of low definition living: no data to track, no devices to wear, and little progress to chart. Going along with this model, the underlying assumption was that everyone got more or less what he or she needed, and that living blindly (without actively gathering data or wearing simple devices) would make little or no difference to the outcome of a person’s life. It seemed reasonable to assume that every incoming younger generation would access, at a minimum, the preferred outcomes of the outgoing generation. In other words, with modernization, one might have assumed the younger generation would likely be smarter, stronger and faster than the preceding generation.

Sadly the rapid pace of progress and the pure pursuit of profit may actually have made it possible to modernize large nations in such ways that future generations would fair considerably less well across many measures of human performance. Today in fact, modern societies are plagued with increasing levels of poor health that outpace advances in medical technology. The rates of diabetes, obesity, heart disease, stroke and cognitive declines are growing—some even doubling and tripling—within genetically stable populations. The Economist in 2003 reminded us of this stark contrast (see the time line enhance photo Fig 6.1). While technology seems to advance and re-invent itself in weeks and months, our biological systems move at a remarkably slower pace. It may have required six million years to transition from ape to man, but transitioning to reach a majority overweight or obese in the U.S. required 3-4 decades, essentially a blink of the eye in evolutionary terms. New operating systems may be designed over a weekend, but the operating system (genetic material) of our human biology was set many centuries ago. Clearly, through modernization, societies may inadvertently violate the basic expectations of human biology (such as activity and nutritional expectations) and


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suffer profound erosion of community health outcomes. Why is this important? Namely because most think wireless health is of benefit to patients and of a lesser value to people who are not yet patients. The reverse may indeed be true. Wireless health may be of greatest value in ensuring that folks not yet unwell have access to their health journeys during the time while adverse trends remain reversible. Avoiding the avoidable will be the hallmark of successful wireless health approaches.

Å----------------5-6 Million Years---------------------ÆÅ----3-4 DECADESÆ

Fig. 6-1: A Society No Longer Accidentally Well - Modified from the Economist 2003

Children are bearing the unintended burden of teaching society that the adults in the room have been ineffectual at generating and sustaining optimal prosperity for mankind. For centuries, the chronic diseases of aging occurred, by definition, in the aged. Now, many chronic diseases occur prematurely, not by weeks or months, but by decades. Type II diabetes, once called “adult onset diabetes,” increasingly accounts for more than 50% of pediatric diabetes. [6-8]-[6-11]

By mapping out the landscape of modern health journeys, effective strategies become more obvious and easier to visualize. The remainder of this section presents a series of graphics designed to orient the reader to the concepts that underpin new approaches to transforming the health of crowds.

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Fig. 6-2: Ideal Crowd Health Landscape

Fig. 6-2 proposes the green path of an optimal health journey, an ideal population arriving in good health and remaining in good health throughout the decades of their lives—experiencing a rapid drop off to death in the end. With modern times, persons following the green path are becoming vanishingly few.

Fig. 6-3: An Illness Episode

Fig. 6.3 Illustrates the traditional medical model centered on an acute care episode: An individual experiences illness and medical care comes to the rescue, returning the individual to good health. This acute care model has become the minority of care offered today. As medical practice improves many once fatal conditions are now treatable and the vast majority of medical practice today is actually chronic care management. Each decade, communities are facing more chronic disease with earlier and earlier onset. Fig 6.4


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Fig. 6-4: Chronic Care Management

Recall Line A is the preferred or ideal health journey. Lines B, C and D represent health journeys with premature arrival into the chronic care stage of life. The figure fails to map the beginning of this premature arrival in order to highlight the relative lack of visibility into the early decades predating avoidable health futures. Patients today are identified if sufficient pathology can be measured such as abnormal breast tissue cells in the diagnosis of breast cancer. However, the high yield co-occurrences that predated this diagnosis are largely undocumented. In other words, the current reactive medicine approach provides more definition of disease progress (see orange tracks above) but fails to provide significant insight into the co-occurrences which predated disease onset.

Reversibility potential and corrective impact is often greatest during the early pre-clinical health journey. Yet individuals, who remain not-yet-chronically-ill, often know little to nothing in regards to their current health position or direction. (see Fig 6.5) Often the sobering truth is delivered too late into the clinical journey and willful redirection is limited. An individual that finds herself at the bottom third of the diagram is likely to require chronic care management for her entire remaining years.

Fig. 6-5: Early Health Journey --The dotted line highlights largely undocumented pre-clinical journey.

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In a world that is no longer accidentally well, there is a need to harness the power of large data to define the co-occurrence patterns that combine effectively to create the outcomes people wish to pursue. Life is short. There is too much to figure out. The current methods of seeking out scientific truths are desperately and unacceptably slow. Communities are losing ground. The role of wireless health in connecting hundreds of thousands of pre-clinical health journeys is outlined below.

4 Why there Is a Need for Other People’s Data Patients see value in sharing experiences with similar patients. Few would prefer to embark on treacherous treatment protocols or years of chronic disease without the wisdom of the crowd that went before them. To this end many patient-to-patient data sharing communities have enjoyed sustained commitment from their members. (see “Patientslikeme, Chrohnology and others)

Is sharing data prior to illness necessary? In short, yes…namely because communities are no longer accidentally well. The last few decades have exposed our ability to engineer our daily activities such that entire communities are able to violate the basic expectations of their genetic material.

Meet Sharon. She is 24 years old and completes her annual physical. Her values are ‘normal’. Does this ‘normal’ mean... a clean bill of health...no work to be done? Her care provider may suggest Sharon simply “carry on” and check back in a suitable period of time and show up for preventive screening as indicated by age and gender. Is Sharon free to carry on as before? Is her access to full academic potential and an optimal health future intact? Perhaps not in today’s world where 'normal' is simply 'prevalent'—not necessarily optimal.

Fig. 6-6: Location on Health Journey

As highlighted above, the term “normal” is of little value in a world where the majority (66%) is overweight or obese. The traditional medical model looked for prevalence in the community


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to define “normal”. Clearly, that which is prevalent may have little to do with that which is optimal. Defining “optimal” will depend upon linking clinically relevant, high-yield health data across crowds; the workhorse of wireless health. In isolation, personal data alone, especially during periods before overt illness, may be of little value. But when this personal data is super-imposed on data from large groups whose eventual outcomes have been tracked over time, the individual may be able to see where they are in relation to others tracking with alternative health journeys. Increasingly, sustainable societies will require the older generation to pass on high yield health data to the younger generation, to free all future generations from repeating the health-related mistakes of the past.

Preventive health screening practices today may do little to prevent the onset of premature adverse health outcomes. For example, population screening for diabetes and lipid disorders depends upon a sufficient prevalence of the disease being present in the population, before screening can be justified. As this model is dependent upon prevalence, it acts as ‘case finding’. Case finding approaches to prevention merely prevent the delayed onset of care. They do little to prevent the co-occurrences that enabled the health journey to prematurely arrive at the point of needing care.

The dichotomous approach to evaluating health state as ‘normal’ or ‘not normal’ may in part be an artifact of human intelligence. Most outcomes are multi-factorial in nature and the result of several events which in isolation may be benign but when occurring in unison the combination may be significantly detrimental. For humans, mentally managing many moving parts is not easy even for the most gifted. Thus, many experts rely on past medical experience and may consider remarkably few variables when determining if a particular treatment is warranted. Furthermore, evidence-based medicine refers to the evidence as gathered by studying populations. Thus by insisting on the use of evidence-based practices, the medical industry must treat individuals as if they were a population. Eric Topol, M.D., expertly outlines this topic in his recent book, The Creative Destruction of Medicine.

While medicine holds onto practices that can be managed by trained individuals, other industries have yielded to and accepted business intelligence and complex computing. Machines capable of managing large volumes of co-occurrence data continuously analyze the data to pullout the interdependencies that drive desired outcomes. Transforming the health of crowds through the effective management of preclinical health journeys will also depend upon the deliberate processing of co-occurrence data. Uncovering optimal preclinical health journeys while redirection remains feasible will require simple sensors and some amount of data sharing across communities.

4.1 The Need for Passive Measurements Why is there a need to use simple sensors to track passive and often involuntary event

streams? Because modernization is changing community environments in profound ways:

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x Nations once flush with activity-based employment must now cope with far-reaching implications caused by the shift to knowledge-based employment…now employees spend long hours sitting motionless in front of computer screens.

x Factory-manufactured food is ubiquitous, cheaper, has a long shelf life and often displaces farm food from many a toddler’s diet, leading to undesirable outcomes including health as well as possibly academic and other poor outcomes.

x People are bombarded with health information that can be reassuring, alarming and contradictory (e.g., should I drink a glass of wine a day? Is 20 minutes of walking enough daily exercise? Is coffee good or bad for me?) Who can make sense of it? Clearly advancing data-driven rapid assessment skills in the community is needed.

Wireless Health is not about life logging, but about using simple sensors and enabling crowds to proactively co-produce their own health futures. At the end of the day, everyone is but a biological agent. The basic expectations of human genetic material—a daily minimum of healthy movement and wholesome, naturally-sourced foods (to name a few)—may be violated by profitable business models. In a world in which society is no longer accidentally well, there is a need to rely on the intelligence gathered by many to deliver scientific truths to help guide human ability to play an active part in their own health futures.

4.2 What Data are High Yield Data? High yield health data implies data that carries a significant contribution to accurately

predicting health futures. If the U.S. had a chance for a ‘do-over’ of the last forty years, using a wireless health infrastructure to both predict and prevent the modern day epidemic of obesity would be technologically straightforward; a small ‘ask’. Significant changes in just a few parameters would account for the major portion of modern preventable poor health. In other words, quantifying avoidable or modifiable health may be achieved through measuring a few simple parameters: 1) change in physical activity, 2) change in body weight, and 3) change in insulin resistance (IR): the body’s ability to response to the changes in food, sleep, activity, stress and other constantly changing inputs . This IR response may be soon tracked through simple home testing of Triglycerides over High Density lipoproteins (TG/HDL). [6-12]

Physical health is only one domain of human performance that appears desperately dependent upon optimal living environments. Other domains such as cognitive performance may also be very dependent upon similar environmental co-occurrences. Recall the declining test scores in schools and universities by US students? The usual assumption is that the problems lie in the education system. Issues such as class size or teacher quality are often blamed. But is it really so?

Imagine a crowd-based wireless health ecosystem in which simple sensors track the activity levels and response to nutritional intake of families. It might turn out that the growing deficit in cognitive capacity might be entirely explained by the dramatic failure to provide certain basic building requirements of the evolving human central nervous system. The human brain is expected to mature into a complex, neuro-circuitry system capable of high-level computational


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math and science. Maturing brains, like all physical products, likely require basic structural elements, presumably found in food and enabled through optimal activity. Historically, since medieval times at least, each subsequent generation appears to have been somewhat stronger, smarter, faster and better able to survive than the generation before.

Although there is little support for the "Aquatic Ape Hypothesis" (that humans primarily evolved alongside lakes, rivers and ocean shores), it is reasonable to assume that early humans stayed close to water and likely included fish and other seafood in their diets. Those foods, along with fresh vegetables, provided all the complex nutrients needed for human brains to evolve the capacity for complex, abstract thought. Furthermore, the central nervous system is tightly regulated regarding glucose and insulin balance.[6-13]-[6-18] Hence, a lifestyle pattern of little activity coupled with significant refined carbohydrate loads may wreak havoc for a developing nervous system.

Rather than a tray of fish or vegetables, the food tray of a modern toddler is far more likely to be covered with energy-dense, nutrient-poor (EDNP) foods—highly processed carbohydrates in the form of juice, crackers, and cold cereals. http://theweightofthenation.hbo.com/ In this way, important structural elements may be missing from the toddler’s diet and perhaps responsible for less-than-expected cognitive performance a few years later. Early data from the UK suggests toddlers diets may have irreversible effects on their IQ by the time they reach 10-12 years of age. According to the report, "Young children eating a diet packed with fats, sugar and processed foods consume too few vitamins and nutrients, which means their brains never grow to optimal levels."

(Read more at: http://www.dailymail.co.uk/health/article-1354683/Junk-food-diet-hits-childs-IQ-reveals-major-UK-study.html#ixzz23FWeBBaS)Ref[3]

It is not far-fetched to conjure a restaurant scene that has a group of well educated, professional parents enjoying Mahi-mahi and arugula, while their kids are in booster seats eating fries. Expecting the younger generation to develop complex grey matter in their central nervous systems without consuming the right nutrients may be unrealistic. Is it possible that the silent and insidious effects of modernization on nutrition and activity are robbing the younger generation of their full cognitive potential endowed upon them originally by their parent’s genes?

Had humans the insight to foresee the hidden challenges of unbridled modernization, the population might be living in a vastly different world. Currently people seem satisfied to wait, passively, for experts to direct their health and nutrition choices. Communities may wrongly assume they can do no harm. This top-down approach does not encourage people to develop the skills to figure out for themselves in a timely manner what the current prevailing poor health pressures are and how best to actively avoid them. Neither does the bottom-up approach, in which people, willy-nilly, try every new health fad, no matter what the source. But the transparent sharing of large numbers of similar health-related experiences could help people develop the skills and knowledge to choose better health outcomes. Some of the major goals of wireless health are to promote active participation in data-gathering, to instill habits of questioning hand-me-down or hand-me-up health information, and to rapidly learn the real, useful health insights that large-scale data sharing makes available.

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Legacy intelligence-gathering models, including randomized clinical trials (RCTs) and others, are insufficient and under-performing. Today, the problem of declining academic scores triggers a need to shed light or uncover the intelligence to explain this worrying trend. Yet the education debate suggests experts might be generating tighter confidence intervals around wrong answers. This type of faulty intelligence was once excusable, only because connecting large numbers of people was not realistically possible. However, by using wearable sensors and wireless health tools to capture high yield data from the older generation, the younger generation can have the information needed to avoid faulty assumptions fueled by often flawed and patchy intelligence.

5 A Plausible Solution: Cross-Generational Collaboration What if wireless health was the participatory mechanism by which communities collectively

gathered and disseminated health and wellness data that is both highly accurate and locally relevant? Had society or communities started with this vision earlier, they might already be in a position to define basic useful insights such as:

1. How much activity a group ought to accumulate by the time they are 10, 20, 30 years old, to avoid generating any lifestyle contributions to poor health outcomes such as the early onset of chronic diseases, cognitive difficulties and excessive incidences of largely preventable diseases.

2. How much insulin resistance (IR) burden has the group accumulated over time? What are the major contributors? Lack of public transport? Poor access to farm foods? What are the most effective local steps to reversing anticipated adverse health effects in the shortest period of time?

Organizing the collective health experiences of the older generation will become an invaluable asset for the younger generation that missed the period of the ‘accidentally well’. Life is too short and too much must be known to expect the young folks might reasonably and efficiently figure out all the mistakes that have already been made. Sustainable societies will depend upon the handing down of this organized data asset. The motivation and the basic operation of accomplishing this cross-generational hand-off will be explained in the following sections. The opportunity takes advantage of two parallel events: seniors needing help using simple devices to manage daily health challenges and young folks needing help with tuition fees.

Because of age-related decline, the older generation is not simply the last of the accidentally well; this generation also houses the bulk of chronic disease management challenges. More than half of those aged 65 or older are at high risk of developing, or are already suffering from, chronic diseases such as diabetes mellitus, cardiovascular disease and arthritis. Data from 2002 confirms that more than half of the Medicare population was treated for five or more chronic conditions.

http://assets.aarp.org/rgcenter/health/fs149_medicare.pdf

http://www.medpac.gov/documents/jun07_entirereport.pdf


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These older citizens are immigrants to the digital world (digital immigrants), many of whom recall phones with rotary dials that were wired to the wall. They may not be skilled in using simple medical monitoring devices. Figuring out how to turn a device on, how to charge it, how to synchronize and transmit data can be overwhelming for an older person. By contrast, modern students have an almost instinctive ability to figure out how to power up any device, sync data and make a variety of user-preference adjustments to the device. These students are the digital natives. However, they are facing a relentless climbing of the tuition fees.

An innovative cross-generational alliance model can be envisioned in which the health care industry remains, as always, the custodian of community illness, while the community partners with academia to become the custodians of human performance. In this model, students mentor their community seniors in using the latest mobile health devices, tools and applications for self-managing and optimizing health behaviors. Both students and seniors use simple sensors to track high yield health behaviors such as movement, nutrition and sleep. Each participant shares this low risk data in a community data commons. In turn, monetizing this commons is deliberate. The expressed intent is to direct at least some of the resulting funds towards reducing ever-rising tuition fees. Below are at least four key ways of enabling innovative business models based on the youth-mentoring community data commons:

1. Generating unique campus intellectual property that is licensed to outside parties; SMASH stands for Semantic mining of activity, social and health data. This is an early example of academic centers advancing intellectual property through the use of the data commons: http://around.uoregon.edu/story/computer-and-information-science/dejing-dou%E2%80%99s-team-smash-nih

2. Attracting technology industries to fund campus projects to cost-effectively test the efficacy of integrated sensor solutions; this is expanded in section #7 below, “Advancing Regulatory framework”

3. Charging 3rd parties (pharmaceutical industry, health care Payers and others) for trolling the data commons for valuable health forecasting and gleaming just-in-time clarity regarding the highest yield co-occurrence trends

4. Enabling small and medium businesses to launch innovative apps which depend on a subscription fee to use the data commons for access to their customer’s data stream. In this model, the app need only connect to the commons and not need to connect directly to an ever-changing sensor or device that might be in use.

Such an innovative approach to gathering high value or high yield data can unite the two generations in a value exchange system that enables both generations to protect themselves from preventable, poor health outcomes. The same approach can also be used to collect and track other non-health (but perhaps health-related) data, such as carbon footprints, exposure to toxins and many other human ecosystem concerns. Such alliances would be scalable, low-cost solutions to an ever-changing landscape of high-cost problems. Imagine the community solves the activity and nutritional problems that prevent optimal performance. Undoubtedly, as these challenges are solved, new challenges will emerge. This cross-generational approach enables the out-going generation (which houses the final truths) to effectively leave a legacy; a

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data asset designed to prevent future generations from repeating the mistakes that went before them.

5.1 Campus-Led Community Data Commons One early task will be to design and implement a standard technology infrastructure in the

form of a Campus-Led Community Data Commons (Fig. 6-7). This form of commons will be the foundation for a monetized alliance. Students, professors and motivated community members will be connected with the expressed intent of creating health value. Civic-minded student groups skilled in using social media will actively recruit their grandparents, grandparents’ friends and other senior citizens to engage in innovative ways to optimize their data gathering and data processing skills. An interoperable system of light instrumentation (low-cost, wearable, easy-to-use sensor devices) will enable the collection of high yield, low risk data to track different standard units of health value. The ease with which this happens will depend, in part, on establishing a practical set of standards to assure the inter-compatibility of data gathering and data transmitting devices, such as accelerometers, sleep sensors and cell phones.

The definition of a “commons” may not be universally known in today’s world. “Peter Barnes describes commons as a set of assets that have two characteristics: they are all gifts, and they are all shared.” “There are a number of important aspects that can be used to describe true commons. The first is that the commons cannot disproportionately benefit only a few—if they do, they cease to be commons. The second aspect is that unlike private property, the commons are inclusive rather than exclusive—their nature is to share ownership as widely, rather than as narrowly, as possible. The third aspect is that the assets in commons are meant to be preserved regardless of their return of capital. Just as we receive them as shared gifts, so we have a duty to pass them on to future generations in at least the same condition as we received them. If we can add to their value, so much the better, but at a minimum we must not degrade them, and we certainly have no right to destroy them.”


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Fig. 6-7: HealthGrid, a Community Data Commons Model

At the highest level, the cross-generational monetization alliance has three main components:

1. Open Ecosystem Platform: a standard community data commons framework with interoperable, low-cost sensors and technical support for student-led solution mash-ups over the course of a few semesters

2. Rapid Prototyping Innovation Engine: multi-disciplinary student teams to design, mash-up and rapidly deploy locally-relevant trial solution sets to be just as rapidly evaluated in the field. These solutions can target any area the community is interested in and range from the using apps to increasing activity to other specifics (e.g., reducing the amount of soda consumed). This approach favors highly innovative solutions with rapid prototype cycling, in contrast to the models major health companies use. A large player may typically spend upwards of $100,000 or more to develop a single app that should be all things for all members. Such an expensive level of investment, failure is viewed as unacceptable. Instead of risking failure, the health industry is forced to play-

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it-safe, thus inventing and re-inventing the wheel, often missing opportunities for major innovation.

In contrast, the youth mentoring model enables student innovators to attract not just seniors, but other community and family members to road-test or stress-test their solutions. In this model, rapid failure is welcomed learning opportunity; a necessary ingredient for attaining success quickly. Many proposed solutions, perhaps most, will fail, with only a few having elements of success. But the failures are valuable, because they will generate key insights into sub-optimal solutions before consuming too much time and money. Those with elements of success will be incrementally improved, as new student teams rotate in every few semesters.

3. Health Data Transaction Transparency and Informatics Engine: The donor data transactions can be tracked and assigned a value via a value exchange platform of sorts. The Targeted Currencies Network, founded by Arthur Brock, uses a process it calls FlowSpace™ to “…increase the flow of resources within a community. These are specifically designed to help solve specific community challenges (http://www.targetedcurrencies.net/). As transactions grow and data commons’ data volume and diversity increases, the data value reaches a tipping point. Soon health industries are willing to purchase access to the commons and a host of innovative business models begin to surface.

In the data commons model, the host organization such as the university, will track data transactions and offer data donors their share of the “money”, as well as the opportunity to “click here” and donate all or part of their share toward reducing tuition fees.

5.2 Examples to Elucidate Key Points of The Proposed Solution The general approach proposed above has already been working in the weather reporting

industry. The National Oceanic Atmospheric Administration’s (NOAA) deploys sensors in the environment, collects the rich flow of synchronous data about many different weather and climatic parameters, streams the data into a data commons and thus provides the fuel to power predictive modeling engines and to support a variety of different business models (e.g., weather reporting services, air traffic control, economic forecasters and others). The NOAA shares its sensor data freely with many users, who comb through the high yield data and compare the weather outcomes of previous co-occurrence patterns, to better predict significant weather events. In this way, the ability to predict high-risk outcomes such as tornadoes, droughts and mass flooding is improved by all previous events. Old weather provides the insight to predict new weather.

Imagine the NOAA model in health. A youth-led project seeks to track the co-occurrences that affect the future community burden of Breast cancer. Each semester, new student groups would help the community remain engaged in tracking accumulated activity through series of innovative apps. Over a few decades the community would be able to link group activity with group breast cancer outcomes. Eventually, the community would accumulate the locally relevant knowledge of how many steps the group must collectively generate to achieve the lowest possible breast cancer rate. By comparing with other cities through the community data


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commons, residents can also gain insights into the per capita frequency of farmer’s markets and other healthy lifestyle elements that may collectively keep health outcomes optimized. The ongoing role of youth would be to mash up simple, low cost, solutions to provide a many different options for users. Apps become “Kleenex-like”; low cost and easily replaced. This is in stark contrast to many employee wellness solutions which often suffer rapid drop-off rates.

6 Household Data Trumps Institutional data If given the chance to do the last few decades of health over again, the technology

requirement would be small. The previous decade did not achieve 66% overweight or obese because of a profound failure of medical records to connect with each other, nor was it a failure of patient portals, or medication compliance and so on. Most of the current feverish activity in health IT involves complex areas unlikely to affect the onset or progression of population-wide avoidable health outcomes. Data from hospitals and clinics, otherwise known as institutional data, suffers from this problem as well. There is little ability to take institutional data and extrapolate backwards to gain insight into the co-occurrences that drove the premature onset of illness in the first place. Instead, household data accumulated by a simple tracking of activity, sleep and response to nutritional intake will generate more reliable predictive capacity regarding future adverse health outcomes.

The World Health Organization (WHO) confirms two simple factors are largely responsible for the recent dramatic shift in phenotype: a profound drop in daily physical activity, along with a significant switch from farm foods to factory foods as noted previously. Furthermore, these shifts appear to be consistent regardless of which population on the planet this pattern of modernization reaches.

Said another way, the potent biological mechanism behind the common evolution of premature poor health profiles is the same mechanism whether the group is American, Asian, German, Canadian, Australian, South Asian, Russian, or any other genetic group or mixture. No matter which group one belongs to, smoking, not exercising regularly and eating poorly may ultimately manifest as a blocked coronary artery, gripping chest pain and the beginning of many visits to Emergency.

Ironically the rampant spread of preventable poor health across the globe has delivered an unthinkable truth, a silver lining. If the same factors generate the same effects on most populations, one should be able to design a universal wireless health tracking solution that optimizes public health everywhere. Furthermore, the widespread commonalities make data tracking on the ebb and flow of lifestyle effects on health relatively easy. It can be done through low-cost, easy-to-use sensors, such as activity trackers, wireless weight scales and simple home biometric-testing devices. In general, these are low-risk data elements and once gathered, in large volume, will give us a robust ability to “stress” the data commons, to determine where “cracks” or data breaches might occur. Such testing will be crucial to reassuring and readying the public to share more sensitive data such as medications, diagnosis and others. 7 Advancing the FDA and Regulatory Framework

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The Food and Drug Administration (FDA) has a formidable task. They are expected to separate what might be safe for public use from that which might generate significant risk, before the public has access to it. The public, for their part, must wait, on standby, while this solitary agency figures things out. Even as more and more technology is used to help with health management solutions, the FDA struggles to act, efficiently, in the public’s best interest. How can they assure minimal harm, while avoiding unnecessary delays in ensuring innovative solutions are available to the public? With wireless health solutions, the campus model can be used to approach the problem with a shared responsibility that will improve how well our regulatory framework functions.

Traditionally, the FDA’s task often involved approving a chemical agent (a drug) to alleviate a human frailty. Let’s use the example of a drug used to lower glucose in diabetics. Typically, this drug would have been tested in study populations and, if the evidence were sufficient, the drug would be “FDA approved” for this specific purpose.

However, when a patient takes a drug, his physiology will often mount a compensatory response. Thus the size of chemical beneficial or effect—the drug potency— tends to decrease over time. The clinician may have little choice but to increase the dose, add other medications or change medications altogether. In short, chemical agents may provide maximal benefit at the start and diminishing benefit may follow.

Contrast the foregoing chemical agent example with the use of a non-chemical agent, such as software. WellDoc (www.welldoc.com) has developed and achieved FDA approval for essentially software that helps diabetic patients track high yield contributors to their blood sugar readings.These contributors may be meal carbohydrates, medications, and exercise to name a few. A new patient with no previous experience with the WellDoc app might essentially start at zero or no-benefit because they are not familiar with the solution. Over time, the software teaches them to understand which activities and foods have the greatest effect on blood sugar readings. Thus, unlike the declining potency of a chemical agent, a software-based treatment can be expected to have dynamic and increasing potency as the user learns from previous experience. Furthermore, learning can grow exponentially as similar patient groups share their experiences through crowd-based collaboration models.

In this way, youth-led wireless health solutions can be used in advancing community resilience in the face of future epidemics. Communities experience flu epidemics periodically, a pattern throughout history and into modern times. Large numbers of people become ill and some eventually require extensive medical care. Consider a recent influenza example: the H1N1 epidemic in 2009-10. This community health event was remarkable, as it seemed to be randomly fatal among otherwise young, healthy individuals. As of today, there is no test or technology to help families determine if flu-afflicted members are likely to see their symptoms deteriorate soon and the need expert care arise. Without this information families have no way of deciding when to seek care or “wait it out” at home.

During the last H1N1 epidemic, an alarming number of young people presented to medical facilities too late to avert their deaths. There were simply no means for predicting which flu symptom pattern would likely remain uncomplicated from other flu patterns which would more likely herald fatal outcomes. Yet this event occurred at a time when the wireless health


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community was potentially capable of building a reliable ‘fix.’ The fix would involve a wearable sensor and the cross-generational community data commons.

The sensor would track changes in common flu markers (such as temperature, heart rate and blood oxygenation levels). Community members already pre-registered within the community data commons, expect to participate in creating intelligence around health outcomes. These civic-minded citizens have been mentored by students and have been donating sleep data, activity data, physiological data, and more. Students invite the community to track the next flu season with new sensors and instructions are provided regarding their pick up of a 10-pack of sensor patches designed specifically for monitoring the flu. Participating members in the community put on new patches each day to track their heart rates, temperatures and oxygen levels. At the outset, these patches have zero predictive capacity. They are used simply to illuminate and share the pre-clinical courses of many people with flu-like illnesses. Local hospitals send admission and outcome data to the commons to match preclinical patterns against post-admission results.

As the flu season evolves and eventually peters out, the insights gained through the data commons can be made rapidly available, enabling analysis to identify suspicious patterns of symptoms that may lead to fatalities. Subsequent advances to the sensor patch may be suggested, such as switching out heart rate sensors for ones that measure heart rate variability. What’s more, this valuable information can be accumulated in the space of a single flu season.

In this way, the pre-clinical flu course of the community is not lost but purposefully captured, leading to the possibility of incrementally enhanced resilience each flu season. Eventually the patch goes from being a simple gatherer of data, to becoming a diagnostic aide for families wondering if their loved one is safe at home. At this point, the patch would instruct symptomatic individuals or parents of symptomatic children, when the symptom co-occurrence pattern may be treated with simply resting at home, or instead treated with expert medical attention because the risk of deteriorating function has reached a data-driven threshold. This would be a relatively low-cost approach to advance the sensor from a transparency tool towards a diagnostic aide.

In contrast, the current cost to run traditional randomized control trials (RCT) often reaches many thousands of dollars per participant. Using the RCT approach with the flu patch example would not only be expensive, but it might also be operationally infeasible. It is simply unreasonable to expect to be able to register large numbers of participants at the moment they experience the onset of illness. Pre-registration is also difficult, since there is no reasonable way of determining in advance who will get flu.

It is unlikely therefore, given the current regulatory environment, that a flu patch would be approved any time soon. By sharing the intelligence gathering process with the community and using the power of mass participation the once unknowable may become known. Evolving data streams will expose the data combinations from wearable sensors that reliably differentiate between evolving benign and malignant outcomes. Through the voluntary use of simple sensors as transparency agents, it will be possible to know who needs to get to a hospital and who can safely remain at home. The burden of proof is shared with the FDA and the community for which the technology is intended. Those unlucky enough to suffer the flu will not do so in vain.

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They will be important change agents helping to design better sensors, to educate and to better protect others who may become victims during the next epidemic.

There is simply too much to learn to allow old regulatory models to delay the advance of new tools like wireless health. The information wireless health provides can incrementally improve knowledge affecting health outcomes; this knowledge can become an indispensible foundation for the health future of generations.

8 Safety and Security

8.1 The Many Eyes Transparency Model Imagine experts crafted a superb document safeguarding the privacy and security of health

data. Now imagine a 65-75 year-old woman at home with diabetes and hypertension. The reality is that no matter how good the rules are, if she doesn’t comprehend who might be looking at her data or how they might be using it, she is not able to steward her own privacy or security.

Now imagine this person knowing her data is always going to the campus data commons. In this setting, there are student groups, professors, as well as savvy community members with keen and knowledgeable eyes watching whoever may be viewing the data and monitoring how it is being used. With many eyes watching, wrongful or malicious use of her data may be far less likely. This culture is already in place in the open source software industry, where people are expected to behave properly and use open source products responsibly. Recently, a large open source industry player was identified as behaving otherwise. In short order, the community became aware and the market adjusted unfavorably to this player; in this example, no lawsuit was needed. Thus increasingly, non-legal means may become useful and reliable deterrents to undesirable data behaviors.

A “data is safe if everything is transparent” model will likely be sufficient for low risk data such as activity or sleep. However, moving to higher risk data such as medication use and diagnostic labeling will require extra efforts. Enhanced and automated de-identification practices are expected to advance in the coming years. In the meantime, data donors within the campus model are likely to focus on low risk data sharing and adopt locally relevant open-source-like cultures regarding the expectations of privacy and security.

8.2 Principles of Reachability Management Thanks to a plethora of technologies and media, people are more connected now than at

any previous time in history. Any given individual may be connected through landlines, cellular phones, email, electronic messaging, video conferencing, and social media. Add lightweight, wearable sensors to the mix, and valuable information can be easily and widely shared to create new insights and drive new economies.

Had such health information been available and reachable during the 1970s (the heyday of accidental wellness), society might now possess a wealth of detailed information. This wealth might have exposed some surprising reasons why the rates of obesity, type II diabetes and breast cancer have doubled or tripled over the last 20 to 30 years.


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Still today, many assume that weight gain per capita may predict the states with the highest rates of obesity. However community-centric wireless health approaches to gathering intelligence may effectively confirm the key event was not accumulated weight gain but in fact accumulated weight loss that was the strongest predictor of future obesity rates. Unfortunately, tracking accumulated weight loss is not a simple task without widespread wireless weight scales and engaging apps to entice ongoing tracking.

Recall, through centuries of exposure to long periods of extreme hardships in finding enough food, human physiology has generated relatively robust protection against weight loss. This recent phenomenon of excess nutrition, excess may have occurred too rapidly and as a species, humans have yet to develop any effective protective mechanisms against weight gain.

Perhaps not all weight loss is created equal. Some weight loss may not be health promoting but in fact profoundly dis-regulatory, setting up the individual for future weight gain. Imagine a motivated woman aggressively restricting calories so that over time her body “learns” to store any available calories in anticipation for the next ominous fasting event. As weight loss is achieved through profound calorie restriction, gene expression may be shifting towards hyper-efficient calorie storage; a storage that will set humans up for future weight gains. Without access to accumulated weight loss through a wireless weight scale at home, there is no insight into the ebb and flow of weight in response to repeat dieting and extremes of consumption. The path to being a hundred pounds overweight likely started with many years of serious attempts at weight loss. While still close to ideal weight, the individual is assumed to be risk free, good blood pressure, and good exercise tolerance; but in reality the one hundred pounds or more weight loss occurred undocumented and over many years. A few pound more and a few pounds less… may have left the individual silently on the way to an obese future.

Thus, fundamentally understanding “what is reachable” by a free-living system will depend in large part on the infrastructure as well as the social contract enabling the data sharing upon which defining ‘reachability’ depends. For example: What is ‘reachable’ in terms of insulin resistance burden for San Diego, California? What is reasonably reachable in terms of HDL (high density lipoprotein) for this city? How does one arrive at the best group HDL, and how much of an effect will this community adjustment have on the future cardiovascular event rates? If a significant portion of the adverse health events on the books of any health delivery system are in fact avoidable; what is reachable in terms of effective avoidance? These and many more questions like these will depend upon the effective sharing of data. The only way a 20-year old knows what is reachable in terms of their HDL is if many 20-year olds share their HDL and informatics experts rack forward to define optimal HDL for 20-year old males in a given geography.

In short, society has been operating within a framework that provides little to no insight during the crucial time of a person’s health journey, when flexibility and reversibility are maximally available. Society has continued to struggle with legacy approaches to gathering intelligence. Yet these approaches have produced only partial insights after much use of human and cost resources. Now through the connectivity of crowds, more complex questions can be asked, and answers can be proposed at a level of granularity that dwarfs previous approaches, and at a fraction of the cost of legacy approaches.

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9 Recommendations for Next Steps Work must begin towards freer, more open communications between governments,

educational institutions, health care institutions, students, ordinary citizens, entrepreneurs, technology incubators and commercialization centers, wireless carriers and other technology companies (large and small). Communities should no longer be expected to wait passively, ”on standby” while the institutes of higher learning and the scientific experts sort out what they should know and do. Society can no longer tolerate the snail pace of new insights given the current ability to re-invent environments. Modern man’s ability to shift widespread co-occurrences is guaranteed to out-pace the inherent physiological ability to adapt. The citizenry must be able to roll up their sleeves and contribute effectively to proactively co-produce the optimal health outcomes they seek.

Governments need to re-look at their regulatory functions, giving people a more free hand to use wearable devices to collect and use data within a set of interconnected data commons. Because this type of ‘deregulation’ will be overseen by thousands of multidisciplinary eyes, the new, freer processes for collecting and co-locating health data are less likely to be abused, co-opted for selfish reasons, or derailed by incautious participants. It may also prove much easier to integrate the collection of useful health information into the fabric of society.

The technology industry has seen a rapid increase in the numbers of incubators for start-ups in digital health. Rock Health in San Francisco, HealthBox in Chicago, Sanotron in Canada and BluePrint Health in New York City are just four incubators with ambitious plans to nurture startups to bring their innovative ideas and products to market.

Wireless health innovation is the key to solving the health industry’s main challenges of cost reduction, improved quality of healthcare delivery, increased revenues and sustainability. Youth mentoring models will fundamentally help solve patient engagement and workforce development challenges.

Modern campuses will see the benefit of a community-centric youth mentoring approach and early adopters are likely to reap the greatest benefit. These campuses will prepare themselves to become more accountable to both their students and their wider social community. Modern societies will not be waiting patiently (and passively) for institutes of higher learning to endow them with better lives. As a matter of course, the value generated by this model will be evaluated regularly, and one may discover that high value campuses may differ from the traditional big name campuses. Campuses with the greatest community connectivity, the most social capital and dynamic social contracts supporting evolving security and privacy will likely be highly ranked by incoming student bodies. The most effective data commons will succeed in attracting the diverse data from local and remote participants, respectively. Such innovative campuses may attract the best students, the best researchers, the most industry funding and more. Deliberate design enabling maximal transparency to all stakeholders will underpin a value exchange distribution model. This model will help ensure a future in which the data donor is the data owner.

10 Summary


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This chapter discussed the traditional, centuries-old, man-made medical models and economic systems which are keeping us trapped in an underperforming paradigm. It provided an approach to advancing the collective, non-expert community beyond their current passive, “stand-by” roles. A few short decades ago, community-wide access to pre-1980s forms of activity and nutrition enabled much lower rates of diabetes and obesity. These low rates were “accidentally reachable” by every community. Since that time, unbridled modernization has delivered suboptimal human performance at increasing rates. This fact must no longer be ignored. Out-going generations must expect to leave a data legacy to benefit future societies. Individuals who were once simply waiting for experts to sort out what they should do, must instead, become pro-active participants in co-generating community health intelligence through a cross-generational collaboration framework using enabling technologies such as wireless health.

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Brigitte Piniewski, Cristiano Codagnone, David Osimo.”Nudging lifestyles for better health outcomes: crowdsourced data and persuasive technologies for behavioral change” JRC European Commission, Institute for Prospective Technological Studies, EUR 24785 EN – 2011

wh ch 6 piniewski final cut 20131028

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