Welcome to my  website.  Here I’ll be sharing my thoughts on improving clinical research in my blog, as well as posting observations on data standards, technologies, innovations and other current developments in the clinical research world.  For those of you who’ve been watching some of my presentations in recent years, I’m also sharing my reading list to let you know what else is influencing my thinking and upcoming events.  You can also find information on advisory/consulting services I’m offering and upcoming Events that involve me.


Since I’ve joined HL7 as CTO effective Feb. 28, 2016, I’ll be posting somewhat less frequently on this site for awhile.  But I’ll try to keep updating the reading list and also posting links to other things I’ll be producing with HL7.

Stay tuned for more to come and follow me on Twitter at @WayneKubick and on Linked In at https://www.linkedin.com/in/waynekubick 

Why Reimagine Research?

Biopharmaceutical and medical device manufacturers are facing escalating pressure these days –focus on patients, make data more accessible to outsiders, and, above all, produce better therapeutic products for much lower cost while also meeting expanding and shifting global regulatory requirements. As the world around us evolves, so should our thinking about what comprises data relevant to clinical research, and how best to use it.  Such a change in thinking may involve:

  • Effectively applying today’s data standards not just for compliance but to realize time and cost benefits
  • Charting a course toward applying a more robust way of representing standard data tomorrow – standards that will better align with healthcare and other research data sources and ensuring study data become permanent knowledge assets
  • Pursuing a future vision that applies the best that technology can offer centered on first principles such as patient-centricity, transparency, and collaboration.

We need to look beyond the randomized clinical trial, SAS dataset and the CRF, and gain much greater control of our information with pragmatic approaches that fit todays environment as well as blue skies thinking about tomorrow.

I’ll be sharing my thoughts on topics such as these, and exploring innovative ideas proposed by other kindred spirits that help advance my personal mission to improve clinical research.

So join me as I look for ways to reimagine research.

2 thoughts on “Welcome

  1. Hi Wayne,
    I lead the OMOP project in the Israel Ministry of health, the project is mapping the local Hospital EHR to OMOP CDM to provide data for research.

    We already created a data lake for data research, in parallel, there is a team that began to map the local EHR to FHIR for record-level interaction.

    I see a lot of activity in translating FHIR to OMOP, and I participate in some WG.

    As I do not familiar with FHIR, I keep wondering what is the incentive of mapping FHIR data to OMOP instead of conducting data research direct on FHIR.

    As one that sees the whole picture, I’ll appreciate your insight on this issue.



    1. Hi Guy, Thanks for reminding me that I need to begin posting to this website again. The advantage to OMOP is that it is a widely used analysis data model that provides access to a massive library of analytical methods and procedures, and is already widely used and well understood by researchers. In this sense, FHIR will provide timely access to real world data, but OMOP provides an easy way to work with the data. OMOP also has addressed some basic problems, like improving data quality and modeling patient encounters. Of course it is also possible to work directly with FHIR resources, and there are efforts (such as R on FHIR and the HL7 projects for clinical decision support and evidence-based medicine) to do just that. But FHIR was initially designed to support interoperability rather than analysis and research, and it’s generally easier to get users to work with FHIR if they’re using techniques they already know, rather than relearning. To me it makes sense to leverage the wide usage and body of OMOP knowledge in the OHDSI community by capitalizing on FHIR data feeds first. But over time, I expect we’ll see more and more uses directly on FHIR resources.


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