Ebook: Strategy for the Future of Health
Bringing together some of the strongest and most advanced voices in the field of medicine and technology, Strategy for the Future of Health examines the constantly changing horizon of ideas and technologies which must be addressed by decision makers involved in health-related resource allocation. Future progress and the provision of long-term solutions in healthcare and medicine depend on the decisions to invest in research, development and education today. This book touches upon all aspects of the system and is rich and diverse enough to provide direction in goal formation for those concerned in making such decisions. Strategy for the Future of Health addresses the unprecedented technological revolution in healthcare which is manifesting itself in the convergence of molecular biology, computer and medical science, electrical, mechanical, genetic and biomedical engineering. Health professionals look towards a future where caring machines will assist them in much of their work and consumers will diagnose and treat themselves with self-health tools, personalized designer drugs and automatic surgery bubbles. Such developments could lead to both dramatic cost reduction and eventually to the delivery of error-free healthcare.
The “Strategy for the Future of Health” book is the result of ongoing communication between the editor and chapter authors, with the annual Future of Health Technology Summit™ being a pivotal event maintaining the momentum. It is a “strategic dialog” between the strongest and most advanced voices in the field of medicine and technology.
This book is a snapshot of the constantly changing horizon of ideas and technologies, which I hope will be useful to decision makers involved in health-related resource allocation. It can be compared to an impressionistic painting – it will make a different sense depending on the reader, but I hope that it is rich and diverse enough to provide direction in goal formation – the most important part of strategy development.
Chapters of Strategy for the Future of Health are grouped into six sections:
1. Goals and Synthesis
2. Health Care Strategy for the Future – General Directions
3. Strategy for the Future of e-Health – Technologically Connected Consumers & Machines
4. Life Extension Strategy – Longer Lives and New Life Forms
5. Nano-Strategy – Extending Human Potential with Nanomedicine
6. Strategy for General Wellness – Emotional and Physical Fitness This book commemorates 40th anniversary of man landing on the Moon with the hope that by 2039 we will be able to “land nanorobot Chromallocyte on the liver”.
Renata G. Bushko, Bushko@fhti.org
Editor, Future of Health Technology book series
Founder, Future of Health Technology Institute, www.fhti.org
This article shows the importance of goal setting in strategy development and presents the Future of Health Technology Institute's www.fhti.org goals as an example of goals that have transformative power. It also provides synthesis and developmental history of the “Strategy for the Future of Health” book while examining collective book design as a strategy development tool. It emphasizes unprecedented technological revolution manifesting itself in the convergence of molecular biology, computer and medical science, electrical, mechanical, genetic and biomedical engineering (including cell, molecular and tissue engineering) resulting in the merger of information technology (IT) with medicine and the formation of “ITicine”.
Central problems in health care involve availability, access, quality, and cost. A major part of a health care strategy also involves disease prevention and promotion of healthy lifestyles, which go well beyond the purview of the health care system itself. Implementing any strategy involves health policy, finance, and management expertise. What then is the role of informatics? We take the position here that informatics is a key enabler both for addressing availability, access, quality, and cost, and also for supporting the work of health policy, finance, and management experts. Informatics provides the necessary information technology (IT) infrastructure, standards, tools, and data to be able to address these key topics and for carrying out the work of the experts. In that sense, we regard informatics as a means for social engineering – the availability of these capabilities brings stakeholders to the table who might otherwise not have reason to or be able to work together.
We describe a future in which health and wellness are transformed by (1) the availability of definitive and unambiguous tests to prove or disprove each diagnosis, (2) new methods based in systems biology to help unravel the web of messages transmitted across cellular and subcellular networks, and (3) universal access to data that has been freed from data silos to produce true data liquidity for a constellation of purposes ranging from personal health management to population health research. We believe the resulting “connected health” environment will have a profound impact on every aspect of modern life.
This paper describes the application of a holistic design process to a variety of problems plaguing current healthcare systems. A design process for addressing complex, multifaceted problems is contrasted with the piecemeal application of technological solutions to specific medical or administrative problems. The goal of this design process is the ideal customer experience, specifically the ideal experience for patients, healthcare providers, and caregivers within a healthcare system. Holistic design is shown to be less expensive and wasteful in the long run because it avoids solving one problem within a complex system at the cost of creating other problems within that system. The article applies this approach to the maintenance of good health throughout life; to the creation of an ideal experience when a person does need medical care; to the maintenance of personal independence as one ages; and to the enjoyment of a comfortable and dignified death. Virginia Mason Medical Center is discussed as an example of a healthcare institution attempting to create ideal patient and caregiver experiences, in this case by applying the principles of the Toyota Production System (“lean manufacturing”) to healthcare. The article concludes that healthcare is inherently dedicated to an ideal, that science and technology have brought it closer to that ideal, and that design can bring it closer still.
This chapter looks to the future through the prism of pilot projects well in progress at the time of this writing: use of a malaria electronic tutorial in Mifumi village, development of a mental health electronic tutorial in northern Uganda, and development of an electronic health management system at Tororo Hospital. Each demonstrates a strategy, rooted in African soil, whose ultimate objective is to improve health through IT and medical informatics. The projects connect users, health professionals, and decision-makers, bringing together interdisciplinary teams. These projects all seek to address the question: Can an information and communication technology (ICT) intervention make a difference in morbidity and mortality in African settings? The findings indicate that not only can these interventions be implemented but can be enhanced with community collaboration, making a positive outcome in terms of community adaptation more likely. Finally, this chapter proposes a health informatics center, a Menlo Park for innovation and entrepreneurship in East Africa in which new ICT inventions and interventions for better health can be created from around the region.
The U.S. healthcare delivery system is in crises. Costs are too high and increasingly becoming unaffordable to federal and state governments, employers and consumers. Americans are dissatisfied with the current system and believe it should be fundamentally altered or rebuilt. A solution needs to be found, and it is not the single-payer system espoused by many in Washington and elsewhere.
We believe consumers can cure healthcare if (a) professionals, providers and policy experts shift their mindset from treating diseases and conditions to taking a holistic approach to the caring of people, particularly Baby Boomers and their parents; (b) technology becomes widely available to increase engagement, personalize healthcare, share experiences, make better choices and embrace convenience and (c) a cost-effective and reimbursed primary care navigator (coordinator and/or health manager), consistent with the medical home concept espoused by the American Association of Family Practitioners (AAFP) becomes a central component of public policy.
We live our lives in digital networks. We wake up in the morning, check our e-mail, make a quick phone call, commute to work, buy lunch. Many of these transactions leave digital breadcrumbs – tiny records of our daily experiences. Reality mining, which pulls together these crumbs using statistical analysis and machine learning methods, offers an increasingly comprehensive picture of our lives, both individually and collectively, with the potential of transforming our understanding of ourselves, our organizations, and our society in a fashion that was barely conceivable just a few years ago. It is for this reason that reality mining was recently identified by Technology Review as one of “10 emerging technologies that could change the world” .
Many everyday devices provide the raw database upon which reality mining builds; sensors in mobile phones, cars, security cameras, RFID ('smart card') readers, and others, all allow for the measurement of human physical and social activity. Computational models based on such data have the potential to dramatically transform the arenas of both individual and community health. Reality mining can provide new opportunities with respect to diagnosis, patient and treatment monitoring, health services planning, surveillance of disease and risk factors, and public health investigation and disease control. Currently, the single most important source of reality mining data is the ubiquitous mobile phone. Every time a person uses a mobile phone, a few bits of information are left behind. The phone pings the nearest mobile-phone towers, revealing its location. The mobile phone service provider records the duration of the call and the number dialed.
In the near future, mobile phones and other technologies will collect even more information about their users, recording everything from their physical activity to their conversational cadences. While such data pose a potential threat to individual privacy, they also offer great potential value both to individuals and communities. With the aid of data-mining algorithms, these data could shed light on individual patterns of behavior and even on the well-being of communities, creating new ways to improve public health and medicine.
To illustrate, consider two examples of how reality mining may benefit individual health care. By taking advantage of special sensors in mobile phones, such as the microphone or the accelerometers built into newer devices such as Apple's iPhone, important diagnostic data can be captured. Clinical pilot data demonstrate that it may be possible to diagnose depression from the way a person talks – a depressed person tends to speak more slowly, a change that speech analysis software on a phone might recognize more readily than friends or family do. Similarly, monitoring a phone's motion sensors can also reveal small changes in gait, which could be an early indicator of ailments such as Parkinson's disease. Within the next few years reality mining will become more common, thanks in part to the proliferation and increasing sophistication of mobile phones. Many handheld devices now have the processing power of low-end desktop computers, and they can also collect more varied data, due to components such as GPS chips that track location. The Chief Technology Officer of EMC, a large digital storage company, estimates that this sort of personal sensor data will balloon from 10% of all stored information to 90% within the next decade.
While the promise of reality mining is great, the idea of collecting so much personal information naturally raises many questions about privacy. It is crucial that behavior-logging technology not be forced on anyone. But legal statutes are lagging behind data collection capabilities, making it particularly important to begin discussing how the technology will and should be used. Therefore, an additional focus of this chapter will be the development of a legal and ethical framework concerning the data used by reality mining techniques.
Nosocomial or hospital-acquired infections (NIs) are a frequent complication in hospitalized patients. The growing availability of computerized patient records in hospitals permits automated identification and extended monitoring for signs of NIs. A fuzzy- and knowledge-based system to identify and monitor NIs at intensive care units (ICUs) according to the European Surveillance System HELICS (NI definitions derived from the Centers of Disease Control and Prevention (CDC) criteria) was developed and put into operation at the Vienna General Hospital. This system, named Moni, for monitoring of nosocomial infections contains medical knowledge packages (MKPs) to identify and monitor various infections of the bloodstream, pneumonia, urinary tract infections, and central venous catheter-associated infections. The MKPs consist of medical logic modules (MLMs) in Arden syntax, a medical knowledge representation scheme, whose definition is part of the HL7 standards. These MLM packages together with the Arden software are well suited to be incorporated in medical information systems such as hospital information or intensive-care patient data management systems, or in web-based applications. In terms of method, Moni contains an extended data-to-symbol conversion with several layers of abstraction, until the top level defining NIs according to HELICS is reached. All included medical concepts such as “normal”, “increased”, “decreased”, or similar ones are formally modeled by fuzzy sets, and fuzzy logic is used to process the interpretations of the clinically observed and measured patient data through an inference network. The currently implemented cockpit surveillance connects 96 ICU beds with Moni and offers the hospital's infection control department a hitherto unparalleled NI infection survey.
Technological advancements in recent decades have made the concept of Connected Health feasible. These innovations include hardware innovations (such as wearable medical technology), and software (such as electronic personal health record systems e.g., Google Health and Microsoft HealthVault). Technology innovations must be accompanied by process innovations to truly add value. In health care that includes clinical process innovations and business process innovations. This chapter outlines how the healthcare system is being affected by innovations in connected health. It provides examples that illustrate the various categories of innovation and their impact. Now more than ever, health care reform is required in the U.S. The systems outlined in this chapter will allow care that is of high quality, while extending providers across more patients (i.e. increasing access) at a lower overall cost (improved efficiency).
Increasing understanding of how to categorize patient symptoms for efficient diagnosis has led to structured patient interviews and diagnostic flowcharts that can provide diagnostic accuracy and save valuable physician time. But the rigidity of predefined questions and controlled vocabulary for answers can leave patients feeling over-constrained, like the doctor (or computer system) is not really listening to them. In addition, not hearing the patient's own words can lead to the physician overlooking subtle details that are diagnostically relevant. How can we reconcile the need for patients to express themselves with the doctor's need to understand the patient's experience in medically appropriate terms?
We present I'm Listening, a system for automatically conducting patient pre-visit interviews. It does not replace a human doctor, but can be used before an office visit to elicit complaint details. This information can be used to triage care and prepare patients for visits with educational materials and appropriate tests, making better use of both doctor and patient time. It uses an on-screen avatar and natural language processing to (partially) understand the patient's response. Key is a Commonsense reasoning system that lets patients express themselves in unconstrained natural language, even using metaphor, and that maps the language to medically relevant categories. For example, if a patient describes his or her pain like, “someone sticking in a knife and then turning it”, the system could categorize it as sharp, intense, and localized.
In this paper we consider self organizing frameworks for healthcare in a model mimicking biologic frameworks. We support self organization via attribute based memory mapped information in original random form. ‘Spontaneous interoperability’ and subsequent adaptability is facilitated by the memory map which describes the serialized electronic ordering of transferred information, its evolutionary history, and meta-data associated with the information. Memory maps are transferred in a discovery process facilitating interoperability by means of application adaptation. We demonstrate the potential for network entities utilizing the framework to work together even though they were initially unaware of the potential. We recognize the challenges faced, define initial targeted infrastructure to aid in the advancement of adaptive networks, and suggest future work. The goals of adaptability in healthcare require timely access to information relevant to the decision process and the elimination of slow and costly integrations. It will allow for accurate representation, diversity, longevity/viability, and increased depth/ range of computable information for computational intelligence enhancement. Increased availability of information provided by an adaptive network may help to improved patient outcomes and provide earlier detection, prevention of disease, and a reduction in medical errors.
This paper is effectively subtitled “Considerations of Requirements for Programmable Laws of Probabilistic Higher Order Logical Thought”. Why such a need? Issues such as privacy, security, bandwidth, and computational power demand not a central analyzing agency, but roaming agents to analyze the global explosion of medical data in many hundreds of petabytes distributed across many sites. They will send back only the conclusions, not the source data. But how will they reach those conclusions? This future pressing need will driving workers to consider Best Practice in inference. Right now, there are diverse approaches to inference, and it is not clear how to unify them into a self-consistent system. For example, there is not even universal agreement on how to treat probabilistic higher order logic. Quantum mechanics is held by many to be a universal system, but produces bizarre predictions for the everyday world of human experience. However, by rotation of the imaginary number i =
In the next decade e-Health, or Connected Health, based on traditional Information Technology (IT) will expand to include molecular communications due to the emergence of nano-sensors capable of monitoring the health of individual cells. Artificially created boundaries between medicine and IT must be removed in order to take advantage of the new opportunity to dramatically improve the precision of medical diagnosis. Human physicians and nurses who remain unaided will not be able to utilize the new flow of molecular data to their patients' full advantage. Medicine will evolve into a new discipline that could be called “ITicine,” reflecting the ubiquitous use of caring machines by both consumers and health professionals to reason with and learn from petabytes of behavioral, physiological and molecular data. Data about our human lives collected digitally will increase from 10% to 90% due to physical-sensing, socio-sensing & nano-sensing . The final goal is to allow consumers to cure themselves with the help of caring machines supported by omnipresent computing. The strategy for the future must then involve immediate and massive investment in intelligent medical software and adaptive networks , radical improvement of software development methodologies and social transition to a “machine trust” mentality, where health decisions can be made by computing agents and medical devices rather than doctors or patients. This could eliminate the error-prone features of the current healthcare system described by Dr. Craig Feied et al in “Indistinguishable from Magic: Health and Wellness in a Future of Sufficiently Advanced Technology”. 
Putting an end to human aging is now becoming a reality, and immortality is no longer just a dream. Through what we are calling “Fantastic Voyage,” we provide a guide to achieving life extension through various means, thereby slowing down aging and disease processes.
The three components of Fantastic Voyage are: Bridge One - Aggressively applying today's knowledge. Bridge Two - Putting biotechnology, such as gene technologies, to use with therapeutic cloning and rejuvenation medicine. Bridge Three - Putting nanotechnology to use by developing a means to rebuild our bodies and brains with nanobots. Many of these technology solutions can be simulated today through the use of targeted supplements, designed to address the specific needs of an individual, such as insulin resistance, cholesterol and homocysteine levels, and inflammation.
To slow aging now, we propose a program of supplementing aggressively, eating foods that impede aging and disease processes, and reversing inflammation through diet. We also provide guidance to customize each program to the specific needs of the individual.
Emerging technologies in rational drug design, tissue engineering, gene therapy, and nanobots (among others) promise a future of automated life extension. The use of such technologies, and the resulting dramatic increases in productivity in all areas of human endeavor, will enable us to live in a world in which all our physical needs can be met.
Aging, being a composite of innumerable types of molecular and cellular decay, will be defeated incrementally. I have for some time predicted that this succession of advances will feature a threshold, which I here christen the “Methuselarity,” following which there will actually be a progressive decline in the rate of improvement in our anti-aging technology that is required to prevent a rise in our risk of death from age-related causes as we become chronologically older. Various commentators have observed the similarity of this prediction to that made by Good, Vinge, Kurzweil and others concerning technology in general (and, in particular, computer technology), which they have termed the “singularity.” In this essay I compare and contrast these two concepts.
In this chapter we consider a distinct example of the link between biology and technology, with particular reference to the brain. We look at the example of a brain cultured in the laboratory which is then linked to a physical robot body. The overall entity therefore consists of a physical robot body controlled by a purely biological brain. The entire system provides a wonderful base for the study of the fundamental features of diseases such as strokes and Alzheimer's disease, as well as allowing for a basic investigation into the mechanisms for neural signal transfer.
A promising means to address the limited supply of donor tissue is through the generation of artificial organs consisting of cells and materials. Progress towards this goal is limited by three main obstacles namely the generation of a sufficient number of cells specific to the organ, the arrangement of these cells in a functional tissue architecture and the delivery of nutrients and removal of waste from the tissue mass. This chapter describes the emerging approaches that may be achieved by the control of stem cell differentiation, control of the local tissue environment on the microscale, and the generation of complex structures containing multiple cell types.
Quantum mechanics (QM) provides a variety of ideas that can assist in developing Artificial Intelligence for healthcare, and opens the possibility of developing a unified system of Best Practice for inference that will embrace both QM and classical inference. Of particular interest is inference in the hyperbolic-complex plane, the counterpart of the normal i-complex plane of basic QM. There are two reasons. First, QM appears to rotate from i-complex Hilbert space to hyperbolic-complex descriptions when observations are made on wave functions as particles, yielding classical results, and classical laws of probability manipulation (e.g. the law of composition of probabilities) then hold, whereas in the i-complex plane they do not. Second, i-complex Hilbert space is not the whole story in physics. Hyperbolic complex planes arise in extension from the Dirac-Clifford calculus to particle physics, in relativistic correction thereby, and in regard to spinors and twisters. Generalization of these forms resemble grammatical constructions and promote the idea that probability-weighted algebraic elements can be used to hold dimensions of syntactic and semantic meaning. It is also starting to look as though when a solution is reached by an inference system in the hyperbolic-complex, the hyperbolic-imaginary values disappear, while conversely hyperbolic-imaginary values are associated with the un-queried state of a system and goal seeking behavior.
This chapter describes the negative consequences of medical technology development and commercialization that is too slow, and makes the case for an immediate large scale investment in medical nanorobots to save 52 million lives a year. It also explains the essence of nanotechnology, its life-saving applications, the engineering challenges, and the possibility of 1000-fold improvement over our current human biological abilities. Every decade that we delay development and commercialization of medical nanorobotics, half a billion people perish who could have been saved.