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A New Data Sharing Architecture for Medicine

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Introduction: Dave Chase and Leonard Kish have been crowdsourcing 95 Theses for a New Healthcare Ecosystem. They have also asked those leading the development of the new ecosystem to offer insights into their own take on each of the theses. This is  the first installment from Ben Heywood, Co-founder and President, PatientsLikeMe (PLM). Dave and Leonard believe the recent moonshot for cancer proposed by vice-president Joe Biden, highlighting the need for more data sharing, and the related uproar over research data sharing from the NEJM editorial, show that the need for a new architecture and a new ecosystem, based on sharing, all the more immediate. PLM, as one of the first successful peer to peer health data sharing applications, may serve as a model.

Thesis # 5. Ben Heywood:

A new science will arrive at evidence-based understanding of what works through a great wealth of shared longitudinal health data captured through mobile devices, sensors and health records. This science will be mindful of the concept of transforming Data, to Information, to Knowledge, to Wisdom.
Ben Heywood, Co-founder and President, PatientsLikeMe

If we’re going to talk about evidence-based understanding in the context of a reinvented and redefined health system, we need to first reassess what we mean by evidence, and redefine how we understand it.

When most people think of medical evidence, they think of carefully controlled studies in peer-reviewed journals. The “pyramid of evidence” runs from animal studies and editorials through case series and clinical trials, all the way up to systematic meta-analysis. There’s an emphasis on clinical trials, and it’s strong. But the pyramid reflects a very black and white view of the world—good quality evidence exists, or it does not.

In reality, the evidence we rely on to practice medicine every day is a lot more ambiguous, and grey. Physicians and patients make crucial decisions on the basis of limited evidence and incomplete records. They do so for comorbid or “hard to reach” populations that never take part in research in the first place.

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