Bugs & Bits: Smart Software in Health Data

In 1960, JCR Licklider released his seminal piece on intelligence amplification entitled “Human Computer Symbiosis” which outlined his vision for the future of computing – a world not where computers replaced humans – but rather a world where “men will set the goals, formulate the hypotheses, and determine the criteria” while “machines will do the routinize-able work that must be done to prepare the way for insights.”

The core thesis behind his paper was the idea of technology abstraction – or the belief that as we come to more fully understand a concept  – we can create rules-based infrastructure around it, use computers to automate the process, and then spend more time focusing on high-level problem solving.

Today, we’ve seen this technology already begin to reshape different industries – early examples include PayPal – whose fraud detection system is driven by machines searching for abnormalities in the data, which are surfaced, to human analysts for interpretation or Palantir who uses similar technology for catching criminals.

Today, there’s a similarly unique opportunity with human health data to ride the wave of declining cost curves in compute and medical testing and the resulting explosion in health and personal data to build next generation of smart software for the category.

Declining cost curves for storing and processing large amounts of health data

As part of the human genome project, scientists developed the first full human genome sequence at a cost of almost $3 billion.  A similarly focused private project during the same time accomplished a similar goal at a total cost of $300 million.

By 2013, True investment Moleculo will begin offering full long read DNA sequencing at a sub $4,000 price point with the direct cost to the business nearing the magic $1,000 number.

Similarly, companies like Ariosa and Sequenome have begun offering direct to consumer pre-natal screens at sub $400 price points for tests that would historically require multiple lab technicians, require multiple blood draws, and tens of thousands of dollars of cost.

Explosion in health and personal data

As the cost for tests continue to decline driven by economies of scale in reagents and computing resources, the healthcare world will slowly begin to shift from:

  • A culture of reactive testing to a culture of proactive testing.
  • A culture of episodic testing to a culture of continuous testing

This shift will give us far more full picture of human health on top of which we’ll eventually be able to build rules-based models for understanding how certain factors – both internal and external affect individuals.

A modern example is startup WellnessFX who provides inexpensive blood tests for individuals on a more frequent basis.  Because they are able to test more frequently, they’re better able to understand what an individuals actual health levels are and prescribe better long-term health solution.

Taken to a further extreme, a recent RockHealth graduate has developed a simple patch that users stick on their arm and provides readings of a users current blood stats via Bluetooth to the users cellphone for a sub $100 price point.

Creating a rules-based health culture

Long-term, we believe these trends will result in our ability to build a rules-based health culture – where we’re far better to understand and model out cause and effect in health – and better understand how we can make and keep individuals healthy.

On the way there, we believe there are a tremendous number of infrastructure, data layer, middleware, and application layer opportunities in building the next generation platform or healthcare – the specific market segments we think are most interesting include:

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I work for True Ventures, an early-stage venture capital fund with offices in San Francisco and Palo Alto. We partner with promising entrepreneurs at the earliest stages in the technology market providing hands-on management support to guide our portfolio companies through the challenges of early growth.