CR 3.0 – A Manifesto for The Next Generation of Clinical Research Data Standards

During these last lingering days of summer before Labor Day, 12 months since retiring from CDISC and now 6 months with HL7, I’ve been contemplating a new vision for clinical research data.   My first exposure to this industry was during the teaming chaos of paper CRFs and double data entry – call that CR 1.0.  CR 2.0 involved EDC and the internet and the first generation of data standards, courtesy of CDISC.  In formulating my personal vision for CR 3.0, I thought of starting with a set of core principles of how things might be through the lens of my past and most recent experience – call it a manifesto for the next generation.  I’ve decided to try to describe my current thinking as a set of core principles:

  1. Whenever data can be captured directly at the source, it must be. EHR data is source data.  If the source is wrong, we need to fix the source, not just correct it downstream in a separate conflicting copy.  Traceability problems vanish when the data captured are the data reviewed.
  2. We must avoid data transformations wherever possible. Each transformation can introduce error and reduce data fidelity. Instead of twisting data to fit into different specific formats, we must learn to fit analytics directly to the data as captured in its native form – using standards that exist at the source.  This is how analytics are applied throughout the modern world of technology – why not in research too?  Why not research on internet time?
  3. Pharma should take advantage of the movement to structured data catalyzed by Meaningful Use and new value and outcomes-based reimbursement models in the USA. This means pharma adopting prevalent common healthcare standards like UCUM and LOINC used extensively in healthcare data records without requiring transformations to other coding systems used only in pharma research.  Pharma should also consider  including SNOMED codes applied at the point of capture in addition to MedDRA because they can provide additional contextual information that may be valuable to reviewers or researchers.
  4. The HL7® FHIR® standard, which is already being widely adopted throughout the world of healthcare, offers the best opportunity to date for research and other pharmaceutical processes to capitalize on the availability of rich EHR data – and can eliminate many of the inconsistencies and variations seen historically with secondary use of EHR data. FHIR can make it possible to reach inside of EHRs not just to capture data, but to monitor protocol progress, provide safety alerts, and allow much greater visibility into trial conduct and can lead to dramatic improvements in study efficiency and drug safety.  We need FHIR for better research.
  5. The current HL7 C-CDA standard provides a useful, persistent archival format for source data from EHRs, despite certain inconsistencies among different implementers. However, the next generation C-CDA on FHIR initiative should resolve many of these current limitations along a smooth migration path from the current C-CDA.
  6. While CDISC standards are currently the language for regulatory submission standards – and should continue to be so for many years to come given the lag time between study and submission – it’s critical for research to also begin adapting to new ways to power research, fueled by EHR data, based on HL7 FHIR. Now is the time to begin work on the standards for tomorrow.  But the CDISC SDTM should prioritize stability over constant change.
  7. With the widespread adoption of cloud technologies, and the ability of FHIR to access distributed data on demand wherever it resides, we are nearing the time when it will no longer be necessary to submit static copies of data from point to point. Instead, we should be planning to use FHIR to access and coalesce data in near-real time from the source, with full provenance and rich metadata, as a definitive single source of truth.  We must eliminate unnecessary redundancy, and use the full capabilities of modern technologies to move forward to the next generation of clinical research.

I recognize some of these may be too radical for some, and I’m sure there are many different ideas of what CR 3.0 may look like.  So I’m interested in starting a dialogue.  I’m also working on some sketches to help illustrate my manifesto which I may share eventually.  Looking forward to hearing what others may think until the next time I find a quiet summer afternoon to stare out my window at a luscious green garden and think other idle thoughts of where the future may take us.  Happy last days of summer!

FHIR® is the registered trademark of HL7 and is used with the permission of HL7.

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Playing with FHIR®

“A standard is not used because we created it. It is a standard because people use it.”  This familiar quote from Dr. Chuck Jaffe, CEO of HL7, could have been the motto for the inaugural FHIR Applications Roundtable Meeting held last week at Harvard Medical School in Boston.

As so many of the smiling attendees attested, this was indeed a very different kind of meeting.

The premise was to get an indication of how widespread FHIR usage already is, and the answer was – more than we could have ever imagined.  Although FHIR is currently designated as a Standard for Trial Use (STU), it has already captivated the development community drawn to its advanced, elegant technology platform.  The Roundtable, like most FHIR events, cements the impression that interoperability through FHIR is not a pipe dream, but a burgeoning reality.

The meeting opened with a rousing talk from Dr. Shafiq Rhab on “FHIR as Enabler” describing how its already transforming communications and processes at Hackensack University Medical Center.  We then transitioned into the meat of the meeting – a series of thirty-four 15-minute speed-dating sessions (including two of the recently announced winners of an ONC challenge grant) with applications and tools covering development and testing environments, patient and provider-facing apps using FHIR, genomics, Clinical Decision Support, and many more application areas.  We also learned how FHIR is being supported by major technology providers such as Microsoft, Computer Associates, and Lockheed Martin and academic institutions including Harvard, Duke, University of California San Francisco and Georgia Tech.

What was most impressive was that this roundtable only scratched the surface of what’s really going on.  Many attendees commented on other activities already underway – several in the audience who learned about the meeting after the submission deadline spoke of their own apps and their desire to get their chance on the podium.

The innovative meeting format was continually fast-moving, dynamic, and fun for all with a palpable sense of energy and community, and the invigorating appeal of a pep rally before the big homecoming game.

My read of the overall sentiment after the meeting was “FHIR is real, FHIR is already in widespread use, FHIR offers an unprecedented opportunity to transform the way we access and use healthcare information. More FHIR!”

A quick poll indicated unanimous support for repeating the whole experience, and plans are already underway to hold the next Roundtable at Duke University in Durham, NC in March 2017.

We expect to see a lot more progress by then since it’s now clear that FHIR is catching on like, well, fire.

FHIR® is the registered trademark of HL7 and is used with the permission of HL7.

A Modest Proposal for Patients (and Researchers)

One thing I can always expect in my personal adventures as a patient navigating the Healthcare System is a good dose of introspection, namely because I have to answer the same questions over and over again. After a back injury, I had to make the all the usual rounds, starting with multiple visits to my primary care physician, followed by trips to an orthopedic, a surgeon, two visits to separate MRI facilities, and a stop at a walk-in clinic to get an EEG before being cleared for surgery. The entire experience, which I’ve had too many times before, might be compared to touring a job fair – except that this one takes place over many weeks and places, almost always with a lengthy wait in-between.

The one thing I can always count on, though, is being asked to fill out yet another medical history form each time. The forms seem in retrospect to be more or less the same, but invariably involve slightly different questions, in no consistent order, and I don’t recall any of my providers seeming to be terribly interested in the results. (If you don’t believe me, try Google Images for “medical history form.”)

This need to constantly repeat the same sort of information over and over again in different  ways is pretty frustrating as a patient, and maybe even more so as a clinical researcher. I can’t begin to estimate the number of Medical History CRFs I’ve seen over the years, but I can sure recall how different they all were. In many cases, most of the data collected was too messy to use for any serious statistical analysis, and the possibility of pooling this data across studies (even for a single sponsor) is dubious. The questions are worded in many different ways, usually not coded to any standard vocabulary, and, for all practical purposes, not much more useful than unstructured text. In fact, it seems my providers mostly just these as images, where they can be safely tucked away until I’m asked to fill another form again from time to time.

As a long-time believer in data standards, I’m flummoxed to explain why we’re not all using standard medical history forms. Sure, the questions may vary depending on what type of specialty is practiced by each provider, or which therapeutic area is being explored for each study, but is that really an excuse for making each unique to each institution? Surely it must be possible to define a General Medical History form, which can be made publicly available, perhaps through Meaningful Use, that covers the most common conditions of interest. And then additional standard modules can be created that can be tacked on by specialty or therapeutic area. These will always ask the questions in the same way, and can code conditions to standard terminologies, including both SNOMED CT and MedDRA for research studies, and maybe even including ICD-9/10 as well. Coding might make it possible to actually make use of this data, when looking to identify patient populations in epidemiological studies, assess protocol feasibility, or even identify potential research subjects. What’s more, as a patient, if I was given a copy of this competed form, perhaps as a CCD document in XML format, maybe I could carry it with me (or store it in the cloud) as part of my Personal Health Record so I don’t have to keep filling out more forms with the same damned data over and over again. Seriously.

So, hello, why can’t we take this one small step for patient, subject and researcher-kind?

Meanwhile, if you think that’s not frustrating enough, let me tell you about what I have to do about my meds each time. But let’s save that for Part 2.