Is more data always better?

By Jeff Rowe
01:17 PM

If you had to choose the one idea driving the HIT transition, it would probably be along the lines of, “Information is good, and more information is better.”

But is that always true?

This regular observer takes on that question in the context of what she calls “Big Data”, which, roughly, is the move by big-name companies to get in the game of collecting, storing and sharing health information.

On the plus, she notes the potential savings that could be realized from the digitization of health data. Specifically, she points to a recent McKinsey report that “is predicting $300 billion per year in savings due to utilization of Big Data to drive the execution of strategies proposed by health care experts. In the area of clinical operations, the report lists projected savings from Comparative Effectiveness Research (CER) when tied to insurance coverage, Clinical Decision Support (CDS) savings derived from delegating work to lower paid resources and from reductions in adverse events, transparency for consumers in the form of quality reports for physicians and hospitals, home monitoring devices including pills that report back when they are ingested, and profiling patients for managed care interventions. Administrative savings are projected from automated systems to detect and reduce fraud and from shifting to outcomes based reimbursement for providers and, interestingly, for drug manufacturers through collective bargaining by insurers.”

It remains an open question whether those savings will actually add up quite as much as the report claims, but an even more important question concerns the potential downside of all that data.

As she puts it, Are all those petabytes of minute details about everything and everybody really useful, or are we just mixing a little wheat with a lot of chaff? There are various opinions on this, but the prevailing wisdom seems to be that the more data you have, the more likely you are to be able to extract something useful out of it. . . . There is much power in Big Data, but there is also danger. As big as Big Data may be, it does not guarantee that it is complete or accurate, which may lead to equally incomplete and inaccurate observations. Big Data is not available to all and is not created by all in equal amounts, which may lead to undue power for Big Data holders and misrepresentation of interests for those who do not generate enough Big Data. Collection and analysis of Big Data has obvious implications to privacy and human rights. But the biggest danger of all, in my opinion, is the forthcoming relaxations in the rigors of accepted scientific methods, and none seems bigger than the temptation to infer causality from correlation.”

For a number of good reasons, HIT policymakers are all about banging the drum to bring ever more providers onto the HIT train. But let’s face it, the potential downsides are significant, and it seems reasonable to suggest that discussions about those downsides should go hand-in-hand with discussions about how to get providers across the HIT frontier.