NephJC welcomes a new writer, Marc Goldman, MBA, MSW. Goldman is Director of Patient Experience for Einstein Healthcare Network in Philadelphia. He has experience as a social worker, management consulting (selling large-scale technology implementations) and most recently providing leadership development & executive coaching. He works with physician and nursing leaders to improve patient experience.

In Open Sesame, the author sketches out a future of how “big data” in the form of open knowledge access will impact medical treatment and research and make genomic medicine a reality. In this chapter Dr. Topol cites many examples of how “crowdsourced” and open access data are beginning to take root, many of which I found fascinating and compelling. Disappointingly, the author neglected to discuss what those difficulties will be, how much it will cost, or how those barriers may be resolved with any meaningful level of granularity.

Dr. Topol asserts that medical research and practice will follow a similar path as other industries or areas being shaped by big data. Examples:

The open source software movement allows technology giants such as Apple, Google, and IBM to recruit developers from around the globe and achieve a “colossal crowdsourcing network effect.” In other words, individuals’ new found ability to see and build upon each other’s work facilitated the explosion of software development that we all benefit from each time we go to the App Store.
MOOCs (massive open online courses) allow hundreds of thousands of learners with internet connections unprecedented access to the most exclusive college level courses at dramatically lower cost.
The open patent movement, as evidenced by a worldwide database (www.lens.org) and Tesla’s release of patents “in the spirit of the open source movement.”

Dr. Topol has coined the term MOOMs (massive open online medicine) to describe the data collection and analysis potential from opening up. He writes “imagine that all the patients who have a diagnosis of cancer become part of a global knowledge resource” where data from every scan, test, treatment and clinical trial conducted around the world are pooled along with each patient’s unique genomic, physiologic, anatomic, and biologic data (and any other kind of “omic” I’ve left out, see My GIS) to not only enable the most precise and targeted treatments possible but even explain and predict poor responses. This type of information could fuel targeted future drug development. Dr. Topol further makes the case that this type of “crowdsourced” data could do the same for any number of disease states, including rare or unknown diseases that when viewed cumulatively affect almost 7% of the population

Opening up research to anyone who wants to access it will enable patients who want to actively guide their own treatment, ushering a previously untapped source of innovation. A fun example of the latter is a baby delivery aid invented by a car mechanic. He points to the growing numbers of open access journals that put research in the hands of the general public, the same public that funds the research via NIH and other government grants.

All of this is beyond my personal scope; I have no experience in research or diagnosis to help me understand the implications of the above. But a couple of practical questions come to mind:

With so much data, from so many sources, how will data integrity and quality be assured? How will researchers and practitioners alike know what data is valid? I fear that whatever evolves as an equivalent to the star ratings one finds on Amazon will not be enough. Our current system of peer review may have its limitations and issues, but working around it without replacing it doesn’t seem like the answer.
On a similar theme, I'm concerned about the ethics of implicitly encouraging experimentation by well meaning, but possibly untrained and unsupervised laypeople making use of these new resources. Again, while current IRB and other institutional controls are problematic, I hope for intentional and thoughtful replacements rather than a wide-open data melee.
Finally, the validity of Dr. Topol's argument that impending data revolution is inevitable seems self-evident, and a new kind of medicine is as exciting as it is needed. Yet, my heart goes out to the physicians on the front line who's lives will just as inevitably be upended. I work with clinicians every day who are working harder and longer than when they first went into practice decades ago, and I fear this data overload will only make their work even harder and more time consuming. Clearly something has to give, and I hope we are wise enough to devise systems that support researchers, practitioners, innovators and consumers alike. I would have liked to have read more about Dr. Topol's vision of what those systems look like. Perhaps I just haven't read far enough in his book.