This blog is totally independent, unpaid and has only three major objectives.
The first is to inform readers of news and happenings in the e-Health domain, both here in Australia and world-wide.
The second is to provide commentary on e-Health in Australia and to foster improvement where I can.
The third is to encourage discussion of the matters raised in the blog so hopefully readers can get a balanced view of what is really happening and what successes are being achieved.
Quote Of The Year
Quote Of The Year - Paul Shetler - "Its not Your Health Record it's a Government Record Of Your Health Information"
Friday, October 28, 2011
Where Does ‘Big Data’ Fit In the Health IT Story? It Looks To Be Evolving Rapidly!
Over last weekend Radio National had a segment on ‘Big Data’
Big Data is the abundance of information now available online, it includes everything from medical results, to your buying patterns, to your social media interactions. It's the latest, greatest thing in the tech world, but is Big Data all it's cracked up to be? And should we be asking serious questions about the ethics involved in accessing this information?
Senior Director and Digital Strategist with Text100
Associate Professor at the Journalism and Media Research Centre at the University of NSW
Co-author with Danah Boyd of 6 Provocations for Big Data
The era of “Big Data” has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and many others are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing information from Twitter, Google, Verizon, 23andMe, Facebook, Wikipedia, and every space where large groups of people leave digital traces and deposit data. Significant questions emerge. Will large-scale analysis of DNA help cure diseases? Or will it usher in a new wave of medical inequality? Will data analytics help make people’s access to information more efficient and effective? Or will it be used to track protesters in the streets of major cities? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Some or all of the above?
Kate Crawford and I decided to sit down and interrogate some of the assumptions and biases embedded into the rhetoric surrounding “Big Data.” The resulting piece – “Six Provocations for Big Data” – offers a multi-discplinary social analysis of the phenomenon with the goal of sparking a conversation. This paper is intended to be presented as a keynote address at the Oxford Internet Institute’s 10th Anniversary “A Decade in Internet Time” Symposium.
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.”
Health care is in the process of getting itself computerized. Fashionably late to the party, health care is making a big entrance into the information age, because health care is well positioned to become a big player in the ongoing Big Data game. In case you haven’t noticed computerized health care, which used to be the realm of obscure and mostly small companies, is now attracting interest from household names such as IBM, Google, AT&T, Verizon and Microsoft, just to name a few. The amount and quality of Big Data that health care can bring to the table is tremendous and it complements the business activities of many large technology players. We all know about paper charts currently being transformed via electronic medical records to computerized data, but what exactly is Big Data? Is it lots and lots of data? Yes, but that’s not all it is.
Americans live for approximately 78 years. They see a doctor about 4 times per year and spend on average 0.6 days each year in a hospital.
To keep a life time record of blood pressure readings for all Americans, including metadata (date/time of reading, who recorded the measure and where, etc.) takes approximately 6 TB (terabytes) of storage space, or about 12 laptops with standard 600 GB hard drives. Not too big. What if we start using mobile wearable devices to quantify ourselves, as some folks already do, and we record blood pressure, say, every hour? We will require 1460 TB of storage, or almost 3000 laptops, or the equivalent of 6 times the digitized contents of the Library of Congress, and this is for blood pressure monitoring only.
Overall, what comes from this discussion are a number of points:
First there is a huge amount of data being collected and health systems are increasingly going to be collecting more.
Second a range of technologies now exist to analyse and attempt to interpret these micro pieces of information - i.e. the raw data.
Third the evidence is not in as to just how reliable such approaches are in getting to the truth of what the data is revealing - and so there needs to be caution in interpretation until we are sure we know what we are doing.
Fourth it is possible some data sets may be used for less than ‘above-board’ purposes.
This paragraph certainly lays out these risks clearly.
“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.”
It seems to me both as a society that is being marketed to and as those interested in where the use of technology can go in the health sector we need to pay close attention as things evolve.