The Four Digital Disruptions That Will Drive Healthcare Innovation
By Merrilyn Datta, Definiens
April 8, 2014 | Contributed Commentary | The convergence of biology and information technology is considered one of the key macrotrends across the globe. But what underlies that convergence? Below are the top four digital disruptions driving the macrotrend and healthcare innovation.
1) Ability to Datify Images. Fifty years ago, diagnosing cancer from an image consisted of a pathologist taking a look at a slide and assessing morphology. While this worked well for simple morphological changes, translational researchers have begun to
see the need for detection of more complex, multifeature assessments that lend themselves to bioinformatic approaches.
Bioinformatics on images was once thought to be impossible, remaining firmly rooted in the qualitative and not the quantitative sphere. But over the past few years that has rapidly changed, first with the realization that quantitative measurements provide better standards for scoring and second that computers have the ability to track and measure multiple image features, beyond what is possible by eye.
The most cutting edge tissue diagnostics groups are not only datafying images, but are now using an approach known as tissue phenomics that has been developed by Gerd Binnig, the 1986 Nobel prize winner in physics. In this approach, the data related to thousands of image features are measured in parallel and bioinformatically correlated with data on patient outcomes, genomics, and demographics. This "hypothesis-free" approach to developing tissue diagnostics is similar to some of the approaches being taken in genomics with next generation sequencing. Tissue phenomics paired with genomics will be the key to realizing personalized medicine.
2) Big Data Approaches to Evidence-Based Medicine. Evidence-based medicine helps provide better patient care by creating guidelines for treatment based on evidence of least to best outcomes. However, in most cases guidelines are still based on small and limited data sets, which make it difficult to account for variations between patients. By expanding evidence-based medicine guidelines to the scale of big data, guidelines can be created that are far more individualized. Small Data also isn’t big enough to use subtle clues as inputs because they occur too infrequently in the data set—valid conclusions cannot be drawn from such small sample sizes.
According to a 2011 report from McKinsey Global Institute, if the U.S. healthcare system used big data approaches to drive quality and efficiency of care, the value could be as high as $300 billion per year. With the explosion of genomic data and the ability to datafy tissue diagnostics, big data approaches could also be used to identify new and enhanced diagnostic approaches. The mining of big data first to develop new diagnostics tests, followed by the ability to create computer assisted diagnosis portals for physicians, is predicted as the linchpin for change in the industry over the next decade.
3) Interoperability of Data Systems. Anyone who owns both an Apple iPhone and iPad knows the efficiency of sharing data and applications across hardware devices. However, in most healthcare IT settings, a maze of homegrown and legacy systems exists across patient management, clinical care and administrative functions that are largely silo-ed. Some hospitals are still even struggling with the transition from paper to electronic records. IMS Healthcare Institute recently published a study indicating that healthcare and life science companies see interoperability of systems as a critical need to streamlining efficiency of the overall system. With the incentives put in place by the Centers for Medicare and Medicaid services for EHR Meaningful Use, key standards are well on their way for EHRs. Getting just the basics in place for interoperability for electronic health records will be a boon for many, and is the foundation for implementing big data approaches routinely in clinical settings.
4) Mobile Health and Wearable Sensors. Mobile health is perhaps the most-used buzzword of 2014, with market estimates between $1-$6b, depending on how the market is defined. While it is predicted that as many as 30% of smartphone users will be using a mobile health app by 2015, most experts agree that the most significant benefit would be in improving patient compliance with healthcare regimens prescribed—a problem that costs an estimated $100-289b annually. Approximately 35% of diabetics are non-compliant, but with Google's announcement in January of a contact lens that measures glucose levels and transmits them, these patients and their doctors may have the means to better ensure proper treatment and compliance.
These wearables and their associated smartphone readouts are not just something for the future. Already available are smartphone diagnostic innovations such as Colorimetrix (an app from the University of Cambridge that uses the iPhone's digital camera to convert test strip readouts to concentration) and Netra (a smartphone based device from MIT that cheaply measures eyesight and the need for glasses).
The convergence of biology and IT is already well on its way with some tangible examples of advancements. Additional disruptions on the horizon in these 4 areas catalyzing change are sure to yield tools for physician and patient empowerment to better outcomes in years to come.
Merrilyn Datta is Chief Commercial Officer of Definiens, the tissue phenomics company and leading provider of image analysis and data mining solutions for quantitative digital pathology. She can be reached at email@example.com.
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