If you visit our booth at HIMSS, you'll see we’re constantly trying to drive this to an outcome-based solution — we’re not a gadget company or a box company or a narrow software company — we’re focusing on outcomes for our customers.

The health care industry has been behind other industries in applying data and analytics to the field. What we're witnessing now is the dawn of a new era in health care, driven largely by digital-based data and analytics.


HCB News: How do the challenges of software development compare with the challenges of device development?

JF: Historically, hardware development was a fundamentally longer cycle. A lot of major changes in hardware would be two- or four-year engineering and development cycles.

The challenges were technical in nature, physics in nature; trying to solve an engineering problem with a belief that if they could produce that technical solution then the market would want that product.

Software is very much the opposite. It starts with defining the problem. What is the customer trying to solve? What information are they trying to ingest, analyze and derive conclusions from? You start off already understanding the problem, understanding the solution, understanding the value proposition.

Also, the development cycles are faster. While it takes significant investment to get the platform up and running, once you have that, it’s a rapid process of saying, "Hey I’m going to drop a denials application in here, drop a revenue cycle application in here, drop a patient contact application in here."

HCB News: In health care, would you say we're in an era where software analytics are trying to catch up with devices? Or are devices and analytics developing in equal partnership?

JF: It’s fair to say that we’ve been in a catch-up cycle on the software side as an industry, where the hardware was generating significant amounts of information that was fundamentally going unused. In that regard we’re harnessing the data and analytics capabilities that the hardware has already enabled for a number of years.

For example, with UCSF we are co-developing an application to look at lung screening images, and very quickly — through analytics and pattern recognition — the algorithm sorts through the images and identifies what's normal, so that the radiologist can focus on what's abnormal.

So you start to automate with the hardware as it is today, but ultimately these things will start to move forward together.

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