Tag Archives: Big data

Big data + Big science = Big health

Big data and big science are buzz phrases in health research at the moment.  It is not at all apparent what the exact definition of these are or should be and whether they will be short lived in our lexicon, but I think it reasonable to assume that where there is buzz there is honey.

I think of big data in health as information routinely collected by our interaction with health systems, both formal (eg GPs or hospitals) and informal (eg networked devices that continuously monitor our heart beat).  Through ever improving connectivity such data may become available (anonymously) for the health researcher and policy maker.  The statistical tools needed to analyse this volume of data without producing spurious correlations are still being developed and there are some genuine ethical concerns that must be addressed.  Within New Zealand we have a unique alpha-numeric identifier for anyone who has encountered our formal health system.  This is very unusual internationally and puts us in a good position to pull data together from multiple sources and to monitor change over time.  Recently I have used this system to assess the performance of new emergency department chest-pain pathways at multiple hospitals throughout the country.  These pathways had been developed in research programs in Christchurch and Brisbane. Following a Ministry of Health initiative for each emergency department to adopt such a pathway, and with the financial support of a Health Research Council grant (and my personal sponsors), we were able to establish efficacy and safety parameters of the change in practice.  If we had used a traditional model of employing research staff at each hospital the costs would have run into many millions and would simply not have been possible given how health research is financed in this country.  This model of monitoring changes made to how health care is delivered is both pragmatic and affordable.  It is also necessary if we are to be reassured that change is really improving practice. We expect to see more big data used in this way.

Big science is often thought of in terms of hundreds or thousands of researchers in facilities like CERN costing hundreds of millions of dollars. I think big science need not be so large or expensive.  Rather it is large international collaborations whereby sufficient good quality clinical research data is gathered to answer important clinical questions.  The key is “sufficient”.  Because of the prevalence of a disease or the size of a population base any one research group may not be able, in a reasonable time frame, to collect sufficient data to answer the important questions.   Over the past two years I have been involved in several international studies where we have pooled data, some of which our group has led, some of which are led by colleagues overseas.  We are now formalising a “consortium” to further ensure data is well and appropriately used and collected.  This move had been particularly important as even million dollar studies of a thousand patients do not have sufficient data to answer some of the key safety questions around the diagnosis of heart attacks (my current focus).  A criticism of much academic clinical research is that it is just not useful1.  This is in large part because the studies are too small to give results that would change practice.  They are also often not pragmatic enough (eg by excluding significant portions of patients likely to be assessed or treated by the intervention under study).  Recognition that it is through large collaborative studies that useful practical change can occur will lead to more such collaborations.  They require people to be involved with a slightly different skill set than those whose research is purely local – in particular the “people” skills required to form productive and lasting cross-cultural relationships.  They also require flexibility in funding which may lead to how rules for some grants change (eg by allowing some portion of funding to be spent offshore).

The era of Big data and Big science for Big health is both daunting and exciting.  While there will no doubt be blind alleys and false starts as with any research or new venture, there will also be practical and meaningful evidence based changes to health delivery. Something to look forward to.

  1. Ioannidis, J. P. A. (2016). Why Most Clinical Research Is Not Useful. Plos Medicine, 13(6), e1002049. http://doi.org/10.1371/journal.pmed.1002049.t003
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A new entity is born: CDaR

Have you ever been told the blood test is positive and the disease in question is shocking – Cancer, an STD (but you don’t sleep around!), MS?  Have you every wondered why it is that some drugs get withdrawn years after, and millions of prescriptions after, they were first approved?  Surely, you’ve read a headline that coffee is good for you and chocolate bad, or was that chocolate good and coffee bad or were they both good, or both bad? Probably you’ve read all those headlines.  What does it all mean?  Am I sick or not (I heard some tests falsely give positive results)? Does it matter if I’ve been taking that drug or drinking three cups a day?  The answer to all those questions depends on one thing – clinical data research.  That is, it depends on how we collect the numbers, and what story those numbers are telling us.  Today, I am thrilled to announce that I have had my department’s (Department of Medicine, University of Otago Christchurch) endorsement to establish a new group, Clinical Data Research (CDaR), which will focus on the stories numbers in medicine tell us.

Source: Pickering et al http://ccforum.com/content/17/1/R7

Source: Pickering et al http://ccforum.com/content/17/1/R7

My recent expertise, as readers of this blog may have picked up, is in Kidney Attack (or Acute Kidney Injury). My contribution, as someone with a physics background, has been in data analysis and mathematical modeling.  It has been a privilege to have been involved with many discoveries and helping bring to light the stories of the biomarkers of that disease and the results of a unique randomised controlled trial.  Kidney Attack is a notoriously difficult to detect, and, partly because of that, one that has no effective treatment.  I’m currently working on the story of the association of Kidney Attack with death following surgery with cardiopulmonary bypass.  I am now looking to take those skills and work with other researchers in other medical specialties who generate data and are looking to tell its story (although I will still work on the kidney data!).  I’m particularly keen to engage with more students and pass on some of the data analysis skills I have acquired.  Moves towards open data as well as collection of data in large databases is providing more opportunities to assess the efficacy of health interventions and detect disease risk factors. The prospect of personalised medicine is one of both hope and hype. To sort fact from fantasy in all these areas will require development of new analytical techniques and careful assessment of evidence. This is what I wish to devote the rest of my career to, and to inspire others along the way. John Ioannidis, a highly respected biostatistician, once wrote an essay entitled “Why most research findings are false”  It is a scary thought that many interventions and diagnostic techniques in medicine may be based on biased studies (usually inadvertently biased!). More data will help reduce the bias, if it is treated nicely.  I promise to do my best to treat my data nicely, after all it is your and my health that is at stake.

I posted a few weeks ago my ten commandments of a data culture.  This is the ethos of CDaR.  Below is the lay summary of the new entity.

Group Name:            Clinical Data Research (CDaR)

Department:             Department of Medicine

Institution:                University of Otago Christchurch

Aim: To provide transparent evidence, with the lowest possible risk of bias, of the utility of biomarkers and efficacy of treatments in health or disease.

Lay summary of our aim: We aim to save lives and reduce the burden of disease by applying new ways to collect and analyse clinical data to better diagnose diseases, to predict the course and outcomes of diseases, and to assess how well treatments work.  We do this because we all want the best possible health outcomes for our communities, our families, and ourselves, with the least possible harm done along the way.  We are excited by the new ways scientists, including those at the University of Otago Christchurch, have come up with to measure disease, disease risk, and treatment outcomes. We are also living in an age of unprecedented data generation. To discover both benefits and harm in all this data and to make those discoveries available to all those making clinical decisions requires people dedicated to analysing this data in a transparent and open fashion that exposes both the good and the bad. That is who we want to be and who we want our students to become.

Definition:  A biomarker is any measureable quantity related to disease risk or diagnosis, or disease or health outcomes.