The Art of Data Perfectionism

The title of this post includes the word perfectionism. The reasons why are elucidated below. Between when I started drafting this post and now I had some thoughts that I wanted to add as a preamble of sorts. I keep coming back to why I don’t post on the blog regularly. I could of course blame the fact that I work most nights after dinner, have a family, a social life and am currently taking a online course in Jira. But, as I’ve said before, we make time for the things in life that we’re passionate about and want to do. I am really passionate about this blog, so what’s the hold up? This might be the one area that where perfectionism is holding me back.

I’m not a perfectionist by trait. I’ve never used that as the answer to the, “Tell us a weakness” question on interviews. I firmly believe in ruthless prioritization and the 80/20 rule. Also, having been a research scientist I tend toward iterative creation, design, etc. Getting trained as a Scum Master was almost like second nature because of course you would design and produce iteratively, only putting into each development cycle what was really needed. So it’s a hard feeling to reconcile now, this perfectionism with the blog. It’s not like I have tons or even tens of followers so the fear of messing up should be low. Except that it’s not. This goes back to a topic I wrote about in another blog post (Identity Crisis). Having gone through the PhD process in the US and spending the majority of my career thus far in research science, I have this ingrained and ridiculous notion that only people who have studied something for their whole lives (or non-stop for 4 years) have the authority to speak about it. The culture of “elder respect” in research science is strong. I just haven’t gotten my head around the idea that not only am I qualified to talk about a range of topics due to my experience to date but that I am qualified to talk about clinical trials and data since I live that work day-in and day-out. I’m currently reading a book called, “Playing Big” by Tara Mohr which is a study on why women have a harder time “playing big”, so to speak, and what to do about it. I’ll let you know how it goes but hopefully one consequence of the process will be me getting my voice out there more.

Of course, the stakes for me with this blog are pretty low. The only real risk is a reputational one if I something wrong. In the world of clinical trials, the risks for inaccurate data can be much higher. (See what I did there, slick, huh?). The individuals who are on the front lines of keeping data quality high in clinical trials are clinical data managers and clinical data coordinators. These individuals are often certified and are, out of necessity, perfectionists. Every little detail matters when you’re setting and managing the data from a clinical trial, from the initial data entry forms to the dataset creation at the end of the trial and locking the database.

Clinical data management is the “collection, integration and validation of clinical trial data”. Done right, clinical data management can reduce the time to market for important health interventions by ensuring the generation and retention of high-quality, reliable and statistically sound data. (Krishnankutty, 2012). High-quality means that the data conforms to protocol specifications and that it contains little to no errors or missing data.

The process starts with the development of the protocol. For the uninitiated, the protocol is a document (often very lengthy) that describes how the trial will be conducted, and ensures the safety of the patients and the integrity of the data. Depending on the organization, clinical data managers are often involved at this early stage. From there, the clinical data managers are integral to setting up the study and how the data will be collected, including what checks will be done during the course of the trial to make sure that the quality and integrity of the data remains intact.

While the trial is ongoing, clinical data managers use a variety of tools to track the data, try and solve discrepancies in the data or find missing data and help to ensure patient safety. If this sounds like individuals have too much control over the data, rest assured that there are pages of regulations that govern operations of clinical trials and the data associated with them and clinical data managers are often at the front line of meeting those regulations.

So with all this to juggle and the results of a trial hanging in the balance, how do clinical data managers do their job. Having worked with them for over a year, i can tell you that they are very committed and very detail-oriented people. They also have fairly clear guidelines in the regulations for how the data should look, or how to ensure data quality (i.e. audit trails, etc). Additionally, there are several professional societies that offer certification, ongoing education and a community of practice. One such organization, and a good place to find information, is the Society of Clinical Data Management (SCDM).

So why this whole post about first, my insecurities, and second, the briefest of overviews of clinical data management? With this post, I’m straddling the dual purposes of this blog; 1) To share my experiences as they happen and as I grow in my career; 2) To highlight the lab data management portion of clinical trials. This first post is to introduce the concept of data management as it pertains to clinical trials in the traditional sense. As I post more (which I will, I promise), I will contrast this to how lab data is viewed and managed in the context of clinical trials and hopefully how those practices can assist in non-clinical research as well.

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