How can information technology improve health care?

I recall first asking this question in leadership forums in our online network in 1997, hoping that a Nobel laureate or Turing Award winner might have a quick answer.  A few weeks earlier I had escorted my brother Brett and his wife from Phoenix Sky Harbor airport to the Mayo Clinic in Scottsdale, seeking a better diagnosis than the three-year death sentence he had just received from a physician in Washington. Unfortunately, Mayo Clinic could only confirm the initial diagnosis for Amyotrophic lateral sclerosis (ALS).

In my brother’s case, the health care system functioned much better than did the family; it was the dastardly disease that required a cure, along with perhaps my own remnant hubris, but since his employer covered health care costs we were protected from most of the economic impact. I then immersed myself in life science while continuing the experiential learning curve in our tech incubator. It soon became apparent that solving related challenges in research would take considerably longer than the three years available to my brother, his wife, and their new son. Close observation of health care has since revealed that research was only part of the challenge.

Symptoms of an impending crisis

During my years in early stage venture capital, symptoms of future economic crisis in health care appeared in several forms, including:

  • R&D failed to consider macro economics
  • Technology that increased costs were most likely to be funded and succeed
  • Technology that decreased costs were often unfunded and/or not adopted
  • Cultural silos in scientific disciplines were entrenched as effective guilds
  • Professional compensation packages were growing rapidly
  • Regulatory bureaucracy was devolving
  • The valley of death was expanding rapidly
  • Trajectory of HC costs and customer means were in opposing X formation

Over the course of the following decade, while observing my father’s experience with diabetes—including billing, it became obvious that few stakeholders in the life science and health care ecosystem were provided with a financial incentive for preserving the overall system; meaning the challenge was classically systemic. Clayton Christensen sums up the situation in health care succinctly: “clearly, systemic problems require systemic solutions.”

12 years to design an answer

When looking at the challenges within information technology and health care first as individual systems, and then combined as a dynamic integrated system, we came to several conclusions that eventually led to the design of our semantic healthcare platform.

Ten essentials:

  1. Patients must manage their own health, including data
  2. Universal computing standards; likely regulated in health care
  3. A trustworthy organization and architecture
  4. Simple to use for entry level skills
  5. Unbiased, evidenced-based information and learning tools
  6. Highly structured data from inception
  7. High volume data meticulously synthesized from inception
  8. Integrated professional and patient social networking
  9. Mobile to include automation, analytics, and predictive
  10. Anonymous data should be made available to researchers

Probable benefits

While we must build, scale, and evaluate our system to confirm and measure our predictions, we anticipate measurable benefits to multiple stakeholders, including:

  • Higher levels of participation in preventative medicine
  • More timely and accurate use of diagnostics
  • Reduced levels of unnecessary hospital visitation
  • Higher levels of nutrition
  • Lower levels of over-prescription
  • Reduced levels of human error
  • Improved physician productivity
  • Reduced overall cost of health care
  • Much greater use of analytics and predictive algorithms
  • Expedited paths to discovery for LS researchers

In May of 2010, we unveiled our semantic health care platform with a companion diabetes use case scenario, which is written in a story telling format, and is freely available in PDF.


About Mark Montgomery
I am a technologist, serial entrepreneur, business consultant, recovered VC, and inventor with interests that are both broad and deep across multiple disciplines, including organizational management, computing, communications, economics, sociology, science and nature, among others. For the past several years I have been founder and CEO of Kyield, which offers a distributed operating system for achieving optimal yield of executable knowledge across large data networks. The patented AI system core acts to unify networks with adaptive data tailored to each entity with continuous predictive analytics designed to significantly reduce ongoing costs while accelerating productivity, and generally make life more satisfying and productive for knowledge workers and their organizations. We provide popular free white papers, use case scenarios, and other information at .

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