Facilitated Data Entry of Lab Results Using OpenClinica’s New Web Services Feature

As mentioned previously, we at Geneuity Clinical Research Services are big fans of OpenClinica and are even more so now with the upcoming release of version 3.0 with its new web services capability.  This article describes how we exploit this new feature to help automate entry of lab results, a particularly important topic given that we do lots of batch testing of specimens and oftentimes test the same specimen for many different analytes.

Prior to 3.0, you had three options when it came to CRF data entry.  The first was to log into OpenClinica’s web interface and manually enter your data.  This was no problem so long as you didn’t have lots and lots of data.  But we did.

Alternatively, you could upload a flat file of your data as long as it was formatted in XML and associated with the appropriate subject id’s and visit descriptions.  Assembling this file wasn’t trivial though and manually looking up each specimen’s subject and event nearly defeated the purpose of the procedure, which was to save time and effort.

Finally, you could do what we did: write custom code to automate the job.  Lab data is amenable to this sort of approach because it is always tagged with something called an accession number that uniquely identifies it.  When designing CRF’s, we always make sure to include a field for the event’s accession number, and when a specimen first arrives through our door the first thing we do is to log into OpenClinica and enter the specimen’s accession number in the appropriate event’s CRF.  Because the number is unique to the study, this entry effectively tags the event and provides a ‘hook’ inside the database so that the event_crf_id of any data item subsequently  annotated with the accession number can be easily looked up using a database query like so: ‘SELECT event_crf_id FROM item_data WHERE value = ‘<accession_number>’.  This, in turn, gives you the requisite information to insert the lab data thusly: ‘INSERT INTO item_data VALUES (‘event_crf_id’, ‘value’ …’ provided you also know the item_id.

To implement this strategy, we wrote custom servlets that operated within the context of our OpenClinica installations.  More recently, we configured MirthConnect channels to do the same.   They worked well and data entry was greatly expedited, but the coding was complex and had to be refactored over and over again for each study and for every CRF change.  While helpful, this strategy wasn’t sustainable in the long run.

Luckily, the latest version of OpenClinica provides a way out.  It incorporates the Spring WS Framework which allows programmers to write something called a ‘web service.’  A web service digests and acts upon XML data sent to it on an on-demand basis over a network.  The source need not be a human being uploading data on a web form, but, more usefully, it can be, say, a clinical testing platform automatically spitting out HL7 messages.  This, of course, is ideal in our case.  So we wrote a web service called ‘EventDataInsert’ that parses XML containing lab data values annotated with accession numbers and item names, looks up the corresponding event_crf_id’s and item_id’s, and inserts the data into item_data accordingly.  The service is generic enough so that it doesn’t have to be refactored for each and every study, but it does make some critical assumptions.  Namely, it assumes that both accession numbers and item names are unique.  So care has to be taken to ensure both these preconditions are met.

The power of EventDataInsert doesn’t just lie in the fact that it handles inserts on an unattended basis, but also in that, like most web services, it requires only simple XML as input.  The latter makes the source of the data irrelevant as long as it can be correctly mapped and transformed into XML.  We often use MirthConnect to do this, using it’s easy-to-use graphical interface to configure channels between incoming raw data and OpenClinica’s web-service interfaces.

The figure below shows a typical deployment of OpenClinica at Geneuity.  MirthConnect is used not only to get data into OpenClinica but also to generate canned PDF reports of the results.  This scenario works for us and gets easier and easier to maintain as OpenClinica evolves new electronic data capture features and makes old ones ever more robust.

Diagram of OpenClinica at Genuity Clinical Research Services
Diagram of OpenClinica at Genuity Clinical Research Services

How a Busy Research Clinical Laboratory Deploys OpenClinica as a Laboratory Information System (LIS)

Here at Geneuity Research Services, we do laboratory tests for clients
conducting clinical trials. We are a one-­stop shop, handling everything
from routine clinical assays to esoteric molecular analyses. We use
OpenClinica as an in-­house LIS to keep track of work­flow and to help
with administrative tasks like billing and specimen archiving. This isn’t
exactly what the developers of OpenClinica had in mind originally. The
application was designed from the point of view of trial sponsors, not their
subcontractors. But it works very well for us nonetheless. In this article,
we briefly describe how we do it.

As everybody who uses OpenClinica knows, defining your event x
CRF matrix is the most important step in configuring a trial. When a
client comes to us with a new trial, we do just that. We are careful
to reproduce the anticipated flow of events and patient groupings
just as designated by the trial’s specifications. “Why bother?”
you’re probably asking. “You’re just a subcontractor. That’s not your
concern.” But it is. Invariably, specimens get mislabeled and
inappropriate tests get ordered, and we’re able to detect many such
mistakes by doing a quick visual inspection of OpenClinica’s patient
x event dashboard with its easy-­to-­take-­in iconification. We feel
that this double-checking is part of our service to the sponsor.

CRF design is our next consideration. In this case, we don’t
try to reproduce everything required from the sponsor’s point of
view. Instead, we design a set of CRF’s that reflects just our role
as a contract clinical laboratory. This means that our CRF’s are far
more narrowly focused and less complicated. But it also means we
include some unique types of fields for pricing, specimen archiving
and the like, items that a trial sponsor usually doesn’t need to
address in their CRF’s but which are very helpful to us.

We are very careful how we name our items when crafting our
CRF’s. Specifically, we use the same terminology across our trials
whenever possible. For instance, the field for a specimen’s accession
number is always named ‘accession_number‘. Likewise, we consistently
use the names ‘freezer‘, ‘received_date‘, ‘price‘, ‘shipping_deviation
and ‘%assay_date‘ (where % is the wildcard string).

Using such a controlled vocabulary is vital in our case because
we use a separate installation of OpenClinica for each study and
employ the postgresql contrib module ‘dblink’ to federate their
attendant databases for querying. A helter­skeleter vocabulary would
render the latter problematic, to say the very least.

For example, we wrote a federated database procedure we call
accessions(). When executed, it consults the accession_number field
in all our installations and returns the number of accessioned
specimens broken down by trial name. This table lists some of the
federated procedures we’ve developed and their functionality.
Collectively, they provide us with a real­time, global snap­shot of
our work­flow and freezer contents.

Because OpenClinica is web­-based and has finely grained user
roles, our clients can remotely log into their respective
installations at our site with read­-only privileges and see the
status of their specimens whenever they want. Conversely, when we
think a specimen has been mislabeled or have some other issue, we can
call a client and direct them to a particular URL displaying the
pertinent information and seek clarification. OpenClinica thus
becomes a shareable laboratory notebook between us and our clients.

The bottom­line: OpenClinica works for Geneuity. Because
OpenClinica is open source, elegantly designed and well documented,
we are able to tailor the application to our needs. In the spirit of
mutual collaboration and aid fostered by OpenClinica’s lead
developers at Akaza Research, we’re making our federated procedures
freely available. They can be downloaded here.

Using OpenClinica for ICF-Based Data Acquisition

The use of electronic data capture (EDC) systems in health care, and especially in clinical trials, has been the object of significant research given the potential advantages like improved data quality, reduced cost, and increased trial repeatability. Despite significant interest and promised benefits, real adoption has been somewhat limited to date with most successful implementations performed in the field of pharmaceutical clinical trials. This can be attributed in part to the lack of underlying consistent and reusable internal data models and the high cost and complexity in customizing most EDC systems.

A potential alternative to traditional EDC software is the use of Open Source Software (OSS), broadly defined as software that is distributed as a freely available and freely modifiable system. This freedom gives the user the opportunity to perform structural modifications and adaptations to better integrate the software with pre-existing IT infrastructures, or to adapt it to local needs and requirements. There are many examples of large scale open source software (OSS) systems in health care, including the VISTA electronic health record system, used in the US Department of Defense and in several hundred installations across the world, the Care2X system, Indivo health, and the OpenClinica EDC system. The use of open source software facilitates the harmonization of a coherent and comprehensive data model that can be reused across different systems. In our work, ICF (the WHO classification for functioning and disability) has been selected as the underlying representational model, and implemented in the OpenClinica EDC software. The experimentation involved more than 10 Italian regions, with multiple hospitals and care centers. The EDC system was designed to test the effectiveness of ICF as a basis for data collection on disability and functioning in a wide spectrum of pathologies.

The complete WHO-ICF classification was imported from the CLAmL XML representation into the LexGrid editor, a tool created by Mayo Clinic for the purpose of editing and maintaining ontologies and classifications. Starting from this intermediate representation, the classification was first translated into the Italian language and then exported back into the CLAmL representation; this form was also used as the basis for the creation of the internal EDC data model, later imported into the OpenClinica platform. From this visual representation, a group of experts designed the set of forms that comprise the web application; later, the database structure and the final application templates were fixed and published on a public web site. The joint use of ICF as a representational model for an Electronic Data Capture system, coupled with the choice of open source software, yielded a significant reduction in the cost and implementation time of a multiregional EDC system. The ease of use of the web interface also facilitated interactions with medical experts to quickly implement alternative data representations and to create a stable and fast platform that is currently being used in an actual trial. OpenClinica demonstrates that open source is stable and ready to be used even in the strictest clinical trials, and that by using open source it is possible to create clinical research applications in a faster and more cost effective way.

– Carlo Daffara, Connecta

Adventures in Web-Based Electronic Data Capture (EDC) – An OpenClinica Case Study

A recent issue of Applied Clinical Trials Online features a case study of OpenClinica Electronic Data Capture (EDC) adoption by German device company, Retina Implant, AG. Here’s a short excerpt:

“Last summer, Retina Implant started using OpenClinica for its latest trial, which is comprised of 11 studies. Currently, five of those studies have acquired subjects and are up and running with the OpenClinica CRF process. Randomization of study subjects are done outside OpenClinica, as it doesn’t have this feature. So far, Hekmat is appreciative of the flexibility of the platform, its ease of use among the global site staff, and the ability to view data in real-time via the Web browser.”

Click here for the full article.