Building a Company at the Intersection of Health and Digital Technologies

HALE.life is optimizing health and performance through precision medicine. We organize, integrate and assess multiple forms of health and physiological information and provide decision plans that can guide health and disease management.

We believe that genomic technologies partnered with technologies that provide contextual information beside the genome will be the technologies that finally change the practice of healthcare. In healthcare markets, we believe medicine will progress from 1) diagnosing and treating disease generally to 2) the precision medicine promise of more accurate diagnosis and personalized treatments to 3) managing disease precisely over the course of life to 4) focusing on health and extending the period of time in which a human is healthy. Health is the ultimate goal as it permits individuals to have a high quality of life over a longer time frame.

  • Symptom-based
    Treatment of
    Populations
  • Precision
    Medicine
  • Managing Disease
    Over the Course of
    Life
  • Precision
    Health and
    Longevity

We believe there are four trends that will drive this new precision health and medicine opportunity: 1) as both investment banks Macquarie and Barclays point out in recent reports, there is a general shift towards consumerism in healthcare decisions; 2) the movement of digital technologies into the lifesciences market is putting far more information at the fingertips of the individual and placing a high value on business models that can translate the power of aggregated data to the individual decision maker; 3) the merging of new genomic data with accompanying contextual information will finally provide medically actionable information; and 4) new analytical fields such as network science and machine learning are making sense of the more complex interactions we can now measure.

First, the investment banks Macquarie and Barclays both recently wrote research reports on the changing state of markets impacted by genomics. Both focused on the growing consumer movement in healthcare decision making. This consumer shift is characterized by the rising involvement of patients in their own healthcare decisions. We, as individuals, are becoming more intelligent about disease and healthcare than we have been historically. Baby boomer populations have now often lived beside loved ones and aided in their long-term care from chronic disease, sometimes in the case of cancer, over decades. Large millennial populations will be doing the same for their baby-boomer parents – but with more technology, and with better agility to use that technology – to help make decisions for their parents as well as for themselves. In the process, both groups have become or are becoming better educated on the current practices in the medical community, and how they can seek the best care and information.

We are already seeing a new movement towards concierge medicine in the United States. Because of changing healthcare patterns in the United States, we believe concierge medicine could climb from less than 3 percent of the healthcare population to closer to 20-30 percent over the coming two decades. We believe more intelligent consumers of healthcare will seek opportunities outside the historical relationship of the doctor and patient, and by doing so will drive the relationship from providing therapeutic aid alone to providing care for extending and prolonging higher quality of life. In doing so, these consumers will also take control of the doctor patient relationship shifting it from the medical perspective to the consumer perspective.

Both research reports note that this shift should support a movement towards preventative care versus reactive treatment, as well as acting as a catalyst for the adoption of precision health and medicine. They posit that we are finally at a tipping point because significant changes to health insurance benefits in the coming years will support a shift towards the consumer becoming more economical around the type of services and sites of care they choose, and what they do to prevent disease. Both note that there is clear demand to better understand the role genes and the environment play in future healthcare outcomes, but that biggest near-term obstacle to the adoption of these technologies is managed-care reimbursement.

Secondly, the movement of digital technologies into the lifescience markets is putting far more information at the fingertips of the individual and placing a high value on business models that can translate the power of aggregated data to the individual decision maker. The increasing value of internet-enabled digital technologies is based on the power ascribed to a large, engaged user base. The ability to gather data cheaply across thousand of subjects will mean more health relationships will emerge from the data and weaker signals can be detected and statistically validated.

The large-scale collection of human data collection will benefit R&D, clinical practice and health. The ubiquity of data will eventually eliminate the trade-off between data quality and data cost. Additionally, this data acquisition trend will lead to the collection of high quality data on "healthy" populations that will permit the deeper analysis of the etiology of the transition from health to disease. By studying health and disease earlier and with greater resolution, we will be able to become more proactive about early disease intervention and eventually reach a goal of true prevention.

The difficulty will be tracking all the data: medical records, wearable data, genomic information and lifestyle data, and then integrating it such that intelligent inferences about health and disease can be made. Gathering this data and harmonizing it will be unprecedented and extremely powerful, but it will also raise privacy and data ownership issues and pose unique analytical challenges. As Tom Caskey, at Baylor has said, "translating this technical data and organizing it in an interpretive manner for the physician will present powerful new business opportunities for those that can leverage the technical and medical models together."

Third, the merging of new genomic data with contextual information or environmental information, will finally provide medically actionable information. Genomics alone is not the full answer for human health and disease. Health is better described by the product (*) of genomics (G) times the environment (E), or G * E. Sequencing the human genome permitted researchers to comprehensively explore G, but it did not promote a detailed characterization of E. As it turns out, genes and gene products do not function independently but participate in complex interconnected pathways, networks and molecular systems, that taken together, give rise to the workings of cells, tissues, organs and organisms. Defining these systems and determining their properties and interactions are crucial to understanding how biological systems function.

We believe that 1) the completion of the Human Genome Project (HGP); 2) the research that has both been completed and remains ongoing as it relates to non-coding DNA (ENCODE) and the microbiome (HMP); 3) additional research in the areas of metabolomics, transcriptomics, proteomics, epigenetics, single cell analysis, regenerative medicine, synthetic biology and gene editing tools; 4) physiological information available from digital devices and wearable devices; and 5) new large data analytical approaches including network science, machine learning, visualization and pattern recognition science, and computational advances has led to a new view of human health and biology. We believe, that over the next decade, this new view will guide the way to breakthroughs in human health and wellness that an earlier generation hoped the HGP would begin to answer.

Fourth, new large-data, analytical approaches including network science, machine learning, visualization and pattern recognition science, and computational advances have emerged simultaneously with precision medicine. In addition to analytical tools, computational advances and visualization tools that have emerged to help translate data into discovery, two additional data tools are being applied to precision medicine: network science and machine learning.

Network science is the study of network representations of physical, biological, and social phenomena leading to predictive models. Network science includes data sciences such as group theory, topology, and fractal geometry that deal with universal applications of connectivity within networks. These data sciences permit us to visualize, represent and understand biological complexity. Network science provides biologists with an array of visualization tools that allow them to see and manipulate the genomes within the network that they exist rather than from a one-dimensional perspective.
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. It is closely related to computational statistics and includes deep learning. We are living in a period of transition from database reporting, which utilizes structured data cleaned and entered by humans, to correlation engines that enable computers to "see and hear" information from bit streams.
In conclusion, we are building HALE at the intersection of health and digital technologies with the goal of providing consumers with the information and the interpretation of that information to guide their health. Barclays and Macquarie have estimated that the market size for these applications will grow from $1.9 billion to over $23 billion. By organizing, integrating and assessing multiple forms of health and physiological information and providing decision plans that can guide health and disease management, we believe HALE has the opportunity to optimize health and performance and then to reimagine the primary care experience.