This is the point that we have arrived at in healthcare.  For over 30 years, we have been digitizing health.  While progress has been slow and frustrating as we have taken existing processes, many of which were never optimized, and digitized them; we are almost through the digitization phase.  Parallel to this, consumers are now carrying supercomputers in their pockets and we are all connected to the internet all the time.  Understanding that most information is now digital, that all the end-points of healthcare interactions are using digital devices, and connectivity is ubiquitous, then you can see that healthcare is on the precipice of becoming information enabled and subject to exponential forces.
 
If you lead a healthcare organization, the realization should be even clearer.  The efforts to put EHR’s in place and the struggles to digitize physician workflows were not an end unto itself but are the beginning of a digital strategy in health.   
 
What I mean by digital strategy, is one where you are able to leverage digital infrastructure to transcend the limitations of:

• Time
• Distance
• Presence 

Put another way, a digital strategy in health is one where every interaction is analyzed across these three transcendent digital dimensions:

Is the interaction best delivered

• in person or remotely
• in real time or asynchronously
• ​by a health practitioner or through technology (ie. Remote monitoring) 

​When each interaction is viewed through these lenses, and every interaction is measured and that information is fed back to improve the next, then healthcare has truly become information enabled.  Once healthcare organizations start this process, they will begin to deliver exponential improvements in health outcomes.  The changes will be small at first, but they will continue to double every couple of years, transforming healthcare.
 
Embracing a digital strategy for health, allows for audacious goals like Lucien Engelen’s vision for Radboud UMC in the Netherlands to remove 70% of services from the hospital in the next 10 years.  These services will either be done elsewhere, at home or in the community, or they will not be done at all because of prevention strategies that address the health need upstream.
 
Leaders in healthcare organizations that embrace and develop real digital health strategies will have massive transformations in the next decade that will improve health outcomes in unimaginable, exponential ways; sort of like those who imagined twenty-five years ago that the world’s fastest computer would one day be in a device they carry in their pocket.

​Samuel Hoskins is a 52 year old man, recently diagnosed with Type II Diabetes.  His story is not atypical for many in Western cultures.  Over the years, his weight increased while his activity decreased.  At first it was not noticeable, relaxed fit instead of slim cut, but now on this October night leaving his family doctor, Samuel is now a diabetic.  His doctor, who had known him since his early 30’s, was knowledgeable and reassuring.  Diabetes was manageable, with effort it could be controlled and complications avoided.  The first step for Samuel was to start a new medication Metformin.  His doctor was also concerned about his blood pressure and cholesterol.  “If they don’t improve, we have meds that will help” – for Samuel this part was more ominous than reassuring.

In medical terms we would say that Samuel has diabetes, hypertension and borderline cholesterol and he is being treated by his primary care physician, but that does little to describe the actual work being done.  To better understand we need to introduce a concept: “health jobs”.  I’m not talking about employment, doctors, nurses, pharmacists, etc.  When I use the term health jobs, I am referring to the specific tasks that are being done as part of the provision of care for a person.  Health jobs are the tasks that patient’s need done.  The health jobs themselves may or may not be specific to a disease or diagnosis; they may be done by health professionals for patients; or they may be done by patient’s themselves.  They may be related to diagnosis and treatment, but also to prevention.
 
When looking at health jobs, it is useful to step back and examine the goals a particular patient may have.  For Samuel and his diabetes, his goals are straightforward, and at this stage are all about avoiding the bad things that diabetes can bring:

• Macrovascular complications of diabetes – or more specifically to avoid heart disease and peripheral vascular disease.
• Microvascular complications of diabetes – the small vessel disease manifested by retinopathy (damage to the retina that can lead to blindness), nephropathy (damage to the kidneys that can lead to renal failure), neuropathy (damage to sensory nerves that can lead severe leg ulcerations).
• Infections – diabetes affects the body’s ability to fight off infections, resulting in a propensity for more severe infections with a greater chance of complications.
• Co-morbidities that go along with Diabetes – including fatty liver disease, sleep apnea, hearing impairment, periodontal disease, fractures, cognitive impairment and depression.

 
Looked at from this perspective of goals, Samuel’s health jobs become simpler to understand.  First and foremost, he needs to control his blood sugars as that has been definitively shown to reduce both microvascular and macrovascular complications as well as infections and co-morbidities.  Secondarily, he needs to monitor and control his risks for these complications.  And finally he will want to screen for and diagnose early any complication or co-morbidity so that it can be optimally managed.  The table below outlines his specific health jobs, as well as whether these are jobs for Samuel or for his team of health professionals.

These four reasons are reasonable, broadly accepted and widely applicable across all health jobs.  Given the potential adverse effects and interactions, most accept that healthcare professionals should be responsible for prescription medications as well as the administration of immunizations.  Having healthcare professionals required to order screening, monitoring and diagnostic tests makes sense from a cost-control basis but also because of the expertise necessary to interpret the data from tests in the full context of a patient.  Many diagnostic and therapeutic procedures require a level of technical ability only mastered after 15+ years of post-graduate training so it makes sense that consumers do not attempt these things at home.  Finally, the role of trust and confidence cannot be underestimated when making important decisions about our health and wellness.
 
The emergence of the digital health stack promises to disrupt this logic directly by leveraging artificial intelligence to  provide consumers with personalized insights allowing them to correctly interpret, and safely act upon information, at marginal costs that over time will approach zero.
 
For Samuel, one can imagine the application of the digital health stack for diabetes pharmacotherapy, providing a learning algorithm that uses a vast array of data relevant to him:

• Genomics
• Metabolomics
• Real-time physiological data
• Real-time blood sugars
• Activity tracking
• Medication and Allergy history

 
This algorithm would compare this information to the complete set of medical literature as well as vast datasets of outcomes from other diabetics along with Samuel’s previous data in order to make a therapeutic recommendation.  This insight would be precise and personal and probably significantly safer than anything coming from a healthcare professional relying only on traditional means.  With the emergence of 3D printing for medications, Samuel’s dosage could even be personalized with delivery to his home by drone.  His response to the medication would be tracked immediately and fed back so that the algorithm could adjust and learn.  Over time the efficacy of the algorithm will improve and the cost will come down.
 
The real promise for diabetes with the emerging digital health stack lies in leveraging learning algorithms to predict and prevent the initial onset of diabetes.  In many ways this will be the path forward; shifting care from a healthcare, disease orientation into the wellness, betterment perspective of prevention and optimization.  The same approach used for the pharmacotherapy of diabetes could be used to direct activity and lifestyle changes with the potential use of therapeutic nutrition and supplements.

Beyond diabetes one can see some common, broad categories for Health jobs:

​From this list we can see that learning algorithms will play a key role in shifting health jobs from healthcare professionals to consumers because they will improve safety by having more complete information and built in feedback monitoring of any and all interventions.  The technical ability required to perform health jobs will diminish as it becomes consumer grade with computation and machine intelligence providing better quality assurance and interpretation than any individual technician’s capability.  The marginal cost of the underlying exponential technologies will approach zero.  Finally, the measurement of health outcomes and the feedback loop necessary will allow consumers to trust and have confidence in learning algorithms over time.
 
For Samuel these technologies hold the promise that he will be fully in control in of his health.  He will have the tools to understand, manage and change the course of his diabetes.  To do this he will be willing to share his data with the applications, algorithms and services necessary to achieve his goals.  When it comes to avoiding the complications of diabetes, privacy fears seem like remote risks.

Medicine is on a profound journey, similar in scope to the transition made in the 1930’s and recounted in Lewis Thomas’ seminal work “The Youngest Science”.  At that time, physicians shed many of the horrific practices from the 19th century and embraced the scientific method.  Our understanding of physiology, began to match our knowledge of anatomy and modern medicine was born.  The discovery of antibiotics, notwithstanding the serendipitous nature of discovery  involving mold floating through an open lab window,  powered a sense that health, illness, life and death were now the realm of human understanding.  

At the turn of this century, the human genome was sequenced and a new journey began in earnest, albeit more slowly and involving more complexity than was imaginable.  This movement is progressing   to a complete molecular understanding of health, wellness, illness and disease.  The underlying mechanisms of human physiology are being illuminated and we are casting off the assumptions of the last century.  Drugs that we have thought of as “hypertension” drugs are simply drugs that work on particular biochemical pathways in the body; useful in hypertension but perhaps similarly so in other conditions or circumstances.  Diseases that in the 20th century were thought to be worlds apart, like coronary artery disease and pemphigus (a rare and severe dermatological condition) are found to be closely related at a genetic level.  Our division of specialities by anatomy, will look awfully quaint when we look back at the last hundred years.  For example, conditions treated by gastroenterologists concerning our gut, run that gamut from cancer, autoimmune, endocrinological, and now increasingly microbiological as we learn more and more about the effects and consequences of the 10 trillion bacterial residents of our microbiome.

What powers this transition in medicine, is a what I call the new digital health stack.  It starts with the digitization of everything:  

• Molecular:  genome, epigenome, transcriptome, proteome, metabolome. 
• Anatomical: Digital CT, MRI, Functional MRI, PET, Ultrasound
• Physiological: external biosensors capable of measuring every aspect of our physiology in real time at very low cost.  Developing internal biosensors capable of measuring both molecular and physiological.
• Lifestyle – sensors that track activity in all aspects – exercise, diet and even personal exposure to harmful substances
• Environment – sensors that monitor environmental changes in terms of pollution, air quality,  UV index, solar activity
• Clinical records – finally the linkage to traditional clinical outcomes from clinical records that have been digitized over the past 20-30 years

What follows this mass digitization will be data aggregation, putting together datasets that for the individual will provide a complete picture of what they experience and the interplay between all of these aspects of what determines our health and wellness.  This data aggregation will be powered by big data techniques and  will start with correlative discoveries that will ultimately need to be hardened by science.   The potential for this discovery, will lead to digital health platforms where patients seeking to fully understand their health will demand their data.  

Data aggregation will provide the substrate for artificial intelligence and the creation of predictive, diagnostic, therapeutic and prognostic algorithms.  The platform with the most users, with the most data will benefit from the most innovators building the most useful algorithms.  As in everything digital, this virtuous feedback loop will exert “winner take all” pressures to the market and accordingly there will be a very limited number of digital health platforms.

Algorithms will initially be supported by human expert workflow, initially as part of a virtual care encounter, but evolving from being a part of every encounter to more of a quality control role and eventually disappearing entirely.  These algorithms, once functioning autonomously will require compelling user interfaces, with profound understanding of human behavioral change built in to their design.

For many health and wellness needs, people will of course continue have the need to see doctors and health practitioners, but even these encounters will be strengthened and improved by the integration digital health stack.
​
Finally the stack is driven by the complete capture of outcome information, fed back into the platform and available to improve the algorithms.

​This emerging digital health stack foretells a future in health care where a few digital health platforms emerge to disrupt, demonetize, dematerialize and ultimately democratize health care globally.  How long will this take?  It probably depends on where you live and the strength of  the counteracting forces (regulation, health care culture, entrenched interests…etc.) in your location.  In parts of the world that have not developed modern health care systems,  the shift to digital health may come first, in ways similar to the developing world’s adoption of cellular technology without first building land lines.

Over the next few months, I will continue to develop and expand this thesis, identifying, unpacking and validating the forces that will combine to determine the trajectory of digital health.

​In the past several weeks I have sat on two health care panels at multi-disciplinary conferences.  The dialogue has been interesting, informative and occasionally challenging and disruptive. But on both panels, and in both conferences I was struck by the absence of an important voice, that of the patient.  In healthcare we speak of being patient-centered, or encouraging patient engagement, but when we get together to talk about health and ideate about the future, patients are tellingly absent.  That we are missing this important perspective is not surprising given the provider centric nature of modern healthcare, but the time has come to change.
 
Advances in digital health, wellness and personalized medicine are putting information into the hands of individual patients in a way that we have not experienced previously.  Many patients already know more about the genes responsible for their metabolism and mental health than doctors do.  Blogs devoted to bio-hacking help individuals make sense of this information and make it actionable.  This information is outside the purview of regulation and traditional healthcare and some of it is provided by self-serving shills who are only interested in selling supplements.  But dismissing this movement of individuals who have empowered themselves to know and understand their own health is to miss a powerful trend in health.

From this an organization has arisen around the following charter:
Charter clauses:

1. Patients or caregivers with experience relevant to the conference’s central theme actively participate in the design and planning of the event, including the selection of themes, topics and speakers.
2. Patients or caregivers with experience of the issues addressed by the event participate in its delivery, and appear in its physical audience.
3. Travel and accommodation expenses for patients or carers participating in the advertised programme are paid in full, in advance. Scholarships are provided by the conference organisers to allow patients or carers affected by the relevant issues to attend as delegates.
4. The disability requirements of participants are accommodated.   All applicable sessions, breakouts, ancillary meetings, and other programme elements are open to patient delegates.
5. Access for virtual participants is facilitated, with free streaming video provided online wherever possible.

 
I am supportive of this approach and have begun to work with the organizations that I interact with to adopt this charter for programs delivered in Canada.  I encourage each of you to do the same.

Check it out: patientsincluded.org

Three weeks ago I had the opportunity to attend this year’s Exponential Medicine conference.  It has taken me this long to process some of my initial thoughts, as the event was quite simply a mind-blowing four days that has altered my thinking and radically expanded my perspective.
 
Technological change in healthcare is happening quicker than anything that we (meaning those of us who work daily in healthcare) might think.
 
The reason for this acceleration, is what the noted futurist Ray Kurzweil (speaker Day 4) has called the Law of Accelerating Returns (LOAR).  Kurzweil extended Moore’s Law, first observed in 1965 by Intel’s Gordon Moore, who observed that the number of transistors on an integrated circuit doubled every every two years and would do so for the foreseeable future.  Kurzweil realized that Moore’s law actually preceded semiconductor technology and could be extended back through multiple technologies, transistors, vacuum tubes, electromechanical devices all the way to Charles Babbage’s Difference Engine first proposed in 1822!  Kurzweil went further to postulate that it is information that powers this phenomenon and that any technology that is information enabled will double in performance every 1-2 years, and that once this starts it will not stop as underlying improvements in computation will continue to improve.  This effect become even more dramatic as multiple fields become information enabled and begin to synergize and converge.
 
In healthcare today, we are seeing a convergence of multiple exponential technologies that are being combined to create possibilities that only years ago would be considered science fiction.  The combinations of cheap and ubiquitous sensors, computational power, data aggregation platforms, big data engines, machine learning and deep learning along with ubiquitous mobile computing foretells a coming time when access to healthcare will be personal, precise, predictive, preventative and amazingly universal.
 
Dr. Aenor Sawyer from UCSF Digital Health Innovation Centre talked about the potential for digital health to reinvent health by connecting doctors and patients at the point of choice (rather than point of care) leveraging what she calls the panome: all the potential data now available at a patient level: genome, transcriptome, metabolome, proteome, microbiome, physiome (think sensors tracking every aspect of physiology, lifestyle and activity, antome (digital medical imaging) along with electronic medical records.  Bringing this data together in the cloud opens possibilities for machine intelligence and deep learning.  Dr. Jeremy Howard from Enlitic, described the capabilities of current generation deep learning systems outperforming radiologists and pathologists after relatively limited periods of training.  Excitingly, this ability to use deep neural networks to detects patterns and correlations across a multitude of  variables, well beyond human capability, coupled with expert clinicians opens the door to the computer augmented physician and improvements in diagnosis today.  The insights derived from the aggregation and analysis of data will power advances in healthcare as well as in wellness.  In fact the ability for consumers to understand their own health and the use of applied behavioral science and gamification to create compelling user experiences, based on data driven insights is likely the holy grail for chronic disease management.
 
Keeping all of the data secure may involve the use of the blockchain as Chelsea Barabas of MIT Media Lab proposed.  Most of us, if we know anything about the blockchain, understand it in conjunction with bitcoin, or as The Economist points out: the technology that allows people who do not know or trust each other, to create a dependable ledger. What if patient data was held in a secure, dependable ‘ledger’ where patients control who has access, revealing only the data required to make a clinical decision.  This intriguing potential may solve one of the biggest challenges to the era of personalized medicine: privacy.  This concept has also been explored by Dr. Eric Topol and Leonard Kish who argue that patients need to own and control their own data.
 
Prior to ExMed, I had read many articles and books that would breathlessly describe how a smartphone app could replace your family doctor.  As a GP myself, I felt that this was nonsense.  I honestly could not see it.  Now, I am humbled to say that the capabilities required to do this are almost here:

• Ability to understand written and verbal language
• Ability to process meaning from conversation
• Ability to understand emotion
• Ability to compare symptoms and history to a database of differential diagnosis
• Ability to learn from interactions and outcomes
• Ability to create compelling mobile app experiences (think games)

In medical school, I learned that 80% of a diagnosis came from a good history, imagine the capabilities of a deep learning system combined with increasing precise data powering a Bayesian probability engine. (I will leave that to another blog post…)
 
As you can tell by reading this far, I am very excited about the potential of exponential technologies to transform healthcare, but I could not post this without pausing to reflect some of the challenges that will occur along the way.  How healthcare systems embrace the new technology without exploding costs will depend on how well leaders view and understand the full determinants and drivers of health. Dave Chase has recently written on the “Copernican” realignment required to organize health around the individual and not around providers and medical technology.  Dave has written eloquently about a multitude of next-generation healthcare delivery organizations that are appearing in the US that understand this shift and are making it real.  In Canada, primary care reform and medical home models are emerging but in a frustratingly slow and uneven way.
 
This ability for healthcare systems to resist the changes necessary to put patients first is what concerns me most deeply at this most exciting of times.  It does give me pause to wonder if the new technology is  most likely to flourish and develop first in areas that are not deeply rooted with entrenched self-interests, regulation and change-avoidant cultures.  Two areas to watch are consumer health and wellness and emerging healthcare systems in the developing world, where in a manner similar to their adoption of cellular technology without landlines, patient centred healthcare around digital technology may be their first modern healthcare systems.
 
It will take me months to process what I learned and saw at Exponential Medicine, and I am sure that many blog posts will come from ideas that were planted in the many lectures by leading thinkers across genomics, genetic engineering, stem cells, AI,3D printing, robotics, big data, nanotechnology, virtual/augmented reality, wearables, sensors and many more!  As important as these lectures were, the hallway, dinner and late-night conversations were even more impressive.  Kudos to  Dr. Daniel Kraft and his team at Singularity University, I have to say that this was the most impressive conference I have ever attended and I look forward to next year’s.

​The topic of personalized medicine is attracting a lot of attention in the scientific community and raising controversial questions about healthcare privatization, data privacy as well as its cost-benefit. Indeed, personalized medicine represents a whole new paradigm of diagnosing and treating disease and, as such, comes with many as-yet unresolved concerns. 

This article takes a step back and instead considers some of the basics. It aims to serve as a primer on the promise of personalized medicine, the most prevalent ways that it is starting to be applied, what’s driving its emergence, and what this fundamentally means to the future of medicine and healthcare. 

Understanding an individual’s molecules changes everything
A few vignettes to help set the stage:

• A 67-year- old woman battling colon cancer learns that her tumor has recurred and that conventional treatments will no longer be effective. Her condition is terminal. At the same time, the cancer research team sequences her tumor and discovers that it is over producing a specific protein that can be controlled with an existing medication for high blood pressure.  The medication is prescribed and the cancer disappears.
• A 40-year-old man must prepare to broach funeral arrangements for his 7-month-old son who suffers from Leigh Disease, a rare neurometabolic degenerative disorder that results in pain, neuro motor degeneration and eventually death. He receives a call from a research team indicating that his son's disease can be traced to a metabolic disruption that can be treated.
• A 41-year-old woman with severe Rheumatoid Arthritis is pondering whether to start taking Azathioprine, understanding that there is a rare but severe potential adverse reaction involving suppression of her bone marrow that could be fatal.  She is given the option to have a test from a local start-up to check for the gene variant that causes this complication before treatment.

These three stories share one common thread: They all illustrate how our new molecular understanding of disease at the individual level can provide cures, save lives and avoid potentially devastating adverse reactions.

These stories also highlight the first three areas of personalized medicine – oncogenomics, rare disease and pharmacogenomics – that are breaking through into conventional medicine with impressive results.

Progress in these fields is providing exciting glimpses into a new clinical world where diagnosis and treatment will come from a molecular understanding of disease and treatment at the individual level, rather than the traditional broad-based population level.  

Unprecedented data volumes that hold untold opportunity
This massive change is only just at its outset and is being powered by the convergence of multiple technologies. Computing power, gene sequencing, biosensors, nanotechnology, machine learning, big data and 3D printing, when applied to health, will provide the ability to understand it at a molecular level for each individual.  The resulting data set available for analysis will be unprecedented in terms not only of volume (1-2 terabytes per person), but also scope, and ultimately uses.

This data will come from:

• Genome – the complete set of an individual’s DNA including all of their genes – representing more than 3 billion DNA base pairs
• Transcriptome – the complete set of RNA transcripts produced by the genome at any one time
• Proteome – the complete set of proteins that are expressed by an individual at any one time
• Metabolome – the complete set of metabolites present within an individual at any one time
• Microbiome – the complete genomic information for the microbes that live inside and on the human body – approximately 100 trillion cells.
• Physiome – the physiological dynamics and characteristics of the individual, as measured through sensor technology
• Anatome – the individual’s unique anatomical characteristics digitally represented through advanced medical imaging

With new abilities to capture and analyze this data, healthcare will be transformed through a precise understanding of how health outcomes are influenced by genetics, environment, diet and lifestyle on an individual basis.  This understanding will result in new opportunities to treat, predict and prevent disease and, at the same time, build continuous feedback between health outcomes and the molecular changes that precede them.   

Changing how we understand disease
This new personalized paradigm will change the way that disease is understood.  

Traditionally diseases have been characterized by the physiological effects they have on a population.  For many diseases, especially complex chronic diseases, there are multiple molecular causes that result in similar physiological signs and symptoms.  For example, even in a rare disease like Leigh's Disease there are dozens of genetic variants causing multiple, different neurometabolic breakdowns that all clinically result in the condition that presents to a clinician as Leigh's Disease.  

For a disease like Type II Diabetes there may be hundreds of different molecular causes, each with a different set of treatment options. Without the ability to differentiate at an individual level, current medical therapy applies a one-size-fits-all approach and treatment options are studied across these heterogeneous disease populations. 

The impact of one-size-fits all treatments is quite varied. Some may work extremely well for one molecular variant of a disease, but may not work for others or can even be harmful. In fact, only half of the population will respond to most medications. This variability can be avoided when clinicians have an understanding of the molecular underpinnings of a disease for an individual patient.  In personalized medicine only treatments that are certain to benefit the individual are prescribed.  

The transformation of medicine from “one size fits all” to a personalized approach will allow doctors to be more precise, predictive and ultimately preventive in their practice.

Three gates to personalized medicine
Personalized medicine is not yet an established paradigm and there are a number of gating factors that will need to be cleared before this vision becomes a reality. Interestingly, technology is not one of these gating factors. The current state and exponential progression of the underlying technologies actually bodes well for a near-term realization of personalized medicine.

Instead, the three challenges, in increasing order of difficulty will be:

1. Complexity – much of current health data is and incomplete. Data from sensors will come from contexts we do not yet understand, our ability share information usefully between multiple systems is inadequate. Finally, we simply do not know what to do with much of this data (yet).
2. Regulation – the legal frameworks for ownership of the data and algorithms derived from this data needs to be fully clarified internationally. The privacy and security mechanisms to allow robust but secure sharing and secondary use of data need to be expanded, and laws to protect individuals against discrimination must be enacted.
3. Culture - in a matter of years, conventional medicine paradigms will be turned over completely and patients will demand treatments based on diagnoses derived from computer algorithms – healthcare professionals will need to adapt to roles that are dramatically different to what they have historically done.

These challenges, rather than the technology, may be the biggest obstacle in the way of personalized medicine.  Early recognition of the changes required and engagement of a broad set of stakeholders will be required to enable the appropriate system changes necessary for society to realize the benefits of this new and profound way of understanding health.

Originally published on TELUS Talks (www.telushealth.com)

​I have just started a new job with the compelling  title – Chief Innovation Officer, TELUS Health.  When I tell friends and colleagues they give me a quizical look before asking some variant of “what does that mean?”  While innovation is almost universally viewed as positive, there is great variation in what people mean by it; ask 25 people and you will get 25 different definitions, all representing their own outlook and biases.  After a few of these conversations, I realized that I need to define innovation for myself and for my organization in a way that is clear,  illuminating and actionable.

If one looks up the definition in the Oxford dictionary, innovation is defined as making “changes in something established, especially by introducing new methods, ideas or products.”

The Google ngram shows an almost exponential 400% increase in the use of the word innovation since 1940.  Much of this is likely attributable to Joseph Schumpeter, who in 1939 defined invention as an act of intellectual creativity undertaken “without importance to economic analysis” while innovation is an economic decision consisting of any one of these:
1.    Introduction of a new product
2.    Creation of a new method of production
3.    Opening of a new market
4.    Conquest of a new source of materials
5.    Implementation of a new form of organization
Schumpeter went further in decoupling the dependance of innovation on invention when he said:  “Innovation is possible without anything we should identify as invention and invention does not necessarily induce invention.”

Stepping back a bit, I think the best definition I have uncovered comes from Scott D. Anthony when he simplified this to define innovation as:  “doing something different that has impact.”
From this definition, we can see that like Schumpeter he sees  innovation as involving significantly more than new products, but Anthony goes a step further and does not include “new” or never been done in his definition.  When something is new, an act of creation,  but has no economic impact, it is invention, not innovation.  On the other hand, when something already discovered, i.e. “not new”, is applied in a different way and has impact, it is innovation.

This latter aspect of innovation is especially important when one understands the power of convergence, the coming together of different lines of inquiry.  Convergence is truly the most fertile ground for innovation, as methods and insights gained in one area, result in application and breakthroughs in others.  We live in a golden age for convergence in healthcare as we are seeing multiple exponential technologies converge:
•    Computing power
•    Networking
•    Gene sequencing
•    Biosensors
•    Big Data
•    Machine learning

Individual development in each of these areas is compounding at(or greater than Moore’s law) resulting in capabilities that were cutting edge only twenty years ago becoming affordable or even virtually free now.  For example from 1992-2012,  the cost of computing has decreased from $222 to $0.06 per million transistors, computing storage from $569 to $0.03 per gigabyte, bandwidth from$1245 to $23 per 1,000 Mbps.  Applied to healthcare the cost of sequencing a human genome has dropped from $100M for the first genome sequenced in 2000 to $1000 today.  Cameras and sensors that cost millions in the 1970’s are now 1,000x better, lighter and cheaper and found in every phone sold!

Over the next decade, using these exponential technologies, our understanding of the molecular basis of life in the individual will simply explode as we will have within our reach affordable ways to sequence not only an individual’s genome, but also their transcriptome, proteome, metabolome and microbiome.  With cheap and ubiquitous sensors we will have the ability to monitor all aspects of human physiology in real-time.  Through big data and machine learning advances, we will also have the power to synthesize all of this new information with existing electronic health records  and realize insights and breakthroughs allowing us to predict and prevent disease..

This convergence of exponential technology will lead to a golden age of innovation in healthcare, transforming medicine from a pathology focused, one size fits all discipline to a precise, predictive and preventive model that improves vibrant life expectancy.  These innovations will come from all angles: new products, diagnostics, therapies and cures, completely new capabilities, and new business models (think disruption).

The realization that we are in the midst of a golden age of healthcare innovation has led me to my own definition of innovation as I begin my new role as Chief Innovation Officer at Canada’s leading health IT company:

“Innovation is something different that has the impact to exponentially transform healthcare”.