OpenClinica Community and Enterprise Editions

Dear OpenClinica Community,

We are only hours away now from the general release of OpenClinica 3.0. There is a ton of excitement here at Akaza as we get ready to see many months of hard work come to fruition.

In advance of this milestone I’d like to describe a few changes we’re making to how OpenClinica is organized and how the name and logo can be used.

A brief background: As a founding member of the OpenClinica^® open source community, I constantly strive to ensure that our technology has a reputation for meeting the highest standards of quality. The growth of OpenClinica^® over the past few years is a testament to some success in that area. In my role as CEO at Akaza Research, a business that has invested millions of dollars into development of this open source technology, I recognize that the same reputation of quality is critical to our ongoing success. Part of how we maintain this reputation is to provide quality control over solutions that bear the OpenClinica^® name. To enable this, Akaza Research owns the registered trademarks for OpenClinica^® and Akaza and reserves the rights to their use.

With the release of 3.0, we are publishing a trademark policy on our website (also summarized below) that defines how the OpenClinica^® and Akaza Research^® trademarks may be used by members of the OpenClinica community. Our goal is to protect the quality of the OpenClinica^® and Akaza brands without inhibiting the freedom that comes with the open source software model. These trademark terms complement the flexibility of open source licensing, by clarifying and creating confidence in the quality and reliability of solutions that bear the OpenClinica^® name.

The most visible way the policy will be manifested is by separating the Community and Enterprise editions of the software. The default software download from OpenClinica.org is the Community Edition, pre-configured in a way that complies with the requirements of the trademark policy. The policy itself covers allowed uses of the trademarks for commercial and non-commercial purposes, both for modified (derivative) works and for unmodified versions of the software.

Akaza’s OpenClinica Enterprise customers and partners will be granted separate licenses that include additional permissions on how they may use the trademark in their marketing, operations, and services activities. Their installations will be distinguished as “OpenClinica Enterprise Edition” via the label in the footer of their OpenClinica pages.

I want to stress that 100% of the core OpenClinica source code remains free and under an open source software license. It is our promise that this will always be the case. Over time Akaza will offer additional proprietary services and technology offerings as part of the OpenClinica Enterprise Edition to complement this core, but it is our goal to ensure that the Community Edition always stands on its own as a fully-functioning, 100% open source EDC/CDMS platform.

I hope you share my view that this new policy will provide the clarity and confidence that allow OpenClinica to continue to thrive, without imposing undue restrictions on members of the community.

With that (too lengthy) introduction, here is a summary of the policy. Click here for the detailed, legal version:

Category	Description	Terms and Conditions
OpenClinica Community Edition	You download and install the software on your own, and are not commercially supported by Akaza.	You may not use the OpenClinica brand for marketing or sales purposes, and must include the community edition disclaimer.
OpenClinica Enterprise Edition	You are an OpenClinica Enterprise System Level Support subscribers. Other Akaza customers/partners and OpenClinica code contributors may meet the requirements of this category. Contact sales for more detail.	Includes limited use of the OpenClinica brand for marketing and sales purposes, ongoing support, and display of “OpenClinica Enterprise Edition” in footer.
OpenClinica Community Edition – Derivative Work	You download and install the software on your own, make modifications to the code, and are not commercially supported by Akaza. You want to keep the OpenClinica name/logo in the modified version.	You may not use the OpenClinica brand for marketing or sales purposes, and must include the community edition disclaimer.. You must also clearly state the software has been modified and the modifications are not supported by Akaza.
Other Derivative Works	You choose to strip out the references to the OpenClinica and Akaza names and logos from your modified version of the software. The trademark policy does not apply.	The OpenClinica source code is licensed under the GNU Lesser General Public License (LGPL). You still must follow the terms of the LGPL, including copyright attribution and requirements for redistribution of source code. Of course, if you choose to follow this course, we hope you’ll also let us know about your software modifications and will contribute these back to the core repositories, both for the benefit of the community and to help ensuring future compatibility of your flavor of the software.

If you are a community user of a prior version of OpenClinica and do not intend to upgrade to the latest release, please contact us if you have questions about how the new policy may affect you.

Best Regards,

Cal Collins

CEO, Akaza Research

Rapid Deployment of New Functionality in OpenClinica Using MirthConnect

In a previous article, we describe how we at Geneuity Clinical Research Services exploit OpenClinica’s new web services feature to automate the entry of lab data keyed to accession numbers.  Here, we describe more fully how and why we use MirthConnect.

Started in 2006, MirthConnect is an open source project sponsored by the Mirth Corporation of Irvine, CA.  It is middleware designed to transform, route and deliver data.  It supports HL7, X12, XML, DICOM, EDI, NCPDP and plain old delimited text.  It can route via MLLP, TCP/IP, HTTP, files, databases, S/FTP, Email, JMS, Web Services, PDF/RTF Documents and custom Java/JavaScript.  MirthConnect has been likened to a Swiss army knife and justifiably so.

Channels are the heart and soul of a MirthConnect installation.  A channel is user defined and has a source and a destination.  A source may be a flat file residing on a remote server or a web service call or a database query or even another channel—whatever you like, it doesn’t matter. A destination may be to write a PDF document, email somebody an attachment or enter data into a database.  Again, whatever!

To illustrate, say you want to poll a database and generate a weekly report.  No sweat! Using MirthConnect’s easy-to-use drag-and-drop template-based editor, define a channel with a database reader as a source, and a document writer as a destination, fill in details like user names, passwords and machine names, define which database fields you want to retrieve and how you want to display the data, and you’re done!  MirthConnect’s daemon handles the rest based on your channel’s configuration.

Once defined, a channel can be exported as XML for later import into another MirthConnect installation.  This is all done with the point and a click of a mouse.

At Geneuity, we use MirthConnect to get data in and out of OpenClinica.  Originally, we used custom JAVA code to do this.  But once we found MirthConnect, we quickly realized we were reinventing the wheel.  Why do that?

Here’s a concrete example.  Consider the very simple CRF from a mock OpenClinica installation shown in Figure 1.  It has three groups of items: accessioning, results and reportage.  When a specimen arrives at Geneuity, the lab tech looks up the patient and event pairing in the subject matrix as specified by the requisition and types into the CRF the accession number, the receipt date and any shipping deviations.  This is done by hand and is indicated as step 1 of Figure 2.

Then, as shown in step 2 of Figure 2, the tech tests the specimen at the testing platform.  In step 3, the platform spits out the data whereupon a collection of MirthConnect channels operating in tandem parses the results, transforms them into SOAP messages and sends them to the EventDataInsertEndpoint web service feature of OpenClinica for upload into the CRF fields designated ‘Assay date’ and ‘Analyte concentration’.

After the tech reviews the data and marks it complete, another collection of channels polls the database for results newly marked complete, generates and delivers PDF reports of the corresponding data (step 4) and then reports back to OpenClinica (step 5) via EventDataInsert the details of the reportage, including status, time and any errors (see the third and last item grouping labeled ‘REPORTAGE’ in Figure 1).

The scenario outlined above requires NO CUSTOM CODE beyond the channel configurations and these are encapsulated and standardized by design.  As such, you don’t need an army of coders on staff to develop and maintain them.

Both OpenClinica and MirthConnect are great as standalone products.  Linked together, however, they really sizzle.

Figure 1: A simple CRF from a mock OpenClinica installation

Figure 2: This shows how the different item groupings in the CRF depicted in Figure 1 are populated. Values for items under ACCESSIONING are entered manually by the lab tech. Values for items under RESULTS are populated by the Mirth channels continuously listening for in-coming data from the clinical testing platform. Values for items under REPORTAGE are populated by a distinct set of Mirth channels responsible for polling and reporting newly completed results.

OpenClinica 3.0 Completes Functional Testing; Enters Deployment Testing

OpenClinica 3.0 is almost here! The quality team has successfully completed functional testing and has moved onto the phase of software quality assurance called deployment testing.  Deployment tests cover 8 different target “platforms” that range from a clean installation of OpenClinica 3.0 on a Windows server using Postgres, to upgrades of OpenClinica 2.5 to 3.0 on a Linux machine using an Oracle Database. Of course, we also test on all combinations in between.

Before the production version of the application can be released, it must successfully pass through our Quality System. For those of you familiar with such a thing, all of the testing and documentation that OpenClinica 3.0 is going through will end up generating thousands of pages of “paper” that include user requirements, traceability matrix, and a large set of screenshots which prove the expected results of the test cases did in fact happen.

In addition to the team at Akaza that has invested thousands of hours testing the application, this release has also undergone road testing in our first OpenClinica Pilot Program.  I would like to warmly thank the participants of the program for committing their time and effort in making sure OpenClinica 3.0 is our most well vetted release to date.

Please look for an announcement from me in the coming days of when OpenClinica 3.0 is available for download.

- Paul Galvin, Project Manager

OpenClinica and the Paper Chase: A Case Study

The developers of OpenClinica like to say they are ‘powering the electronic data capture (EDC) revolution’ in clinical data management and indeed that’s the case.  However, sometimes there’s no substitute for paper, especially when it comes to tracking and handling laboratory specimens.  We at Geneuity should know: testing lab specimens for clinical trials is our business.  This article shows how OpenClinica and paper records can augment one another through the use of URL-encoding barcodes.

Readers of this blog probably already know the drill:  a specimen is collected, a paper requisition is attached and the whole kit and kaboodle is mailed to a laboratory for testing.  When a specimen arrives at Geneuity, we log into OpenClinica’s web interface, look up the patient and event pairing in the subject matrix as specified by the requisition and then finally type in the values for items like accession number, receipt date, shipping deviations, freezer location and the like.  When one or two specimens arrive, this process (called ‘accessioning’) is no problem; when 20 to 60 arrive, it rapidly becomes tedious.

In a more perfect world, things would be easier and more automated.  Here’s one potential scenario to help achieve this.  In addition to all the necessary information in human-readable form, also print on the requisition a QR barcode that encodes the URL corresponding to the appropriate event case report form (CRF) in the study’s OpenClinica installation.  For instance, consider the example shown in Figure 1.

It encodes: “https://myOpenClinicaInstall.com/InitialDataEntry?eventCRFId=1” (for illustration only, not intended to link anywhere for real).  Upon being scanned with an appropriate hand-held reader (or smart-phone even), this would automatically direct the lab technician’s browser to the correct screen to input the attendant accessioning information for study event number one.  This would eliminate the manual look-up step described above and would reduce tedium and errors as a consequence.

Implementing this idea would require enabling OpenClinica to print out requisitions.  While challenging, this would not be impossible.  Indeed, middle-ware like MirthConnect may make it relatively easy.

Here’s another example of paper and OpenClinica working together, one that Geneuity actually implements currently to facilitate quality control.  Prior to testing a batch of specimens, lab technicians here use a custom Mirth channel to print out details of the samples to be tested.  A dummy example is shown in Figure 2.  As shown, it includes a simple linear barcode of the accession number, the principle means of tracking specimens in a clinical lab.  Scanning this saves the technician from having to type it into the testing platform during set-up.  It also contains a QR barcode specifying the event-specific URL that directs the technician where to go to review the data upon automatic upload into OpenClinica.  Having this URL handy means the technician doesn’t have to look it up in the subject matrix table which oftentimes has hundreds or even thousands of entries.

URL-encoding barcodes are a cheap and reliable way of linking physical objects with on-line databases, and nowhere is this link more critical than when tracking clinical specimens.  Thanks to Akaza’s commitment to open source, it’s easy to incorporate this technology into OpenClinica and to realize the myriad of benefits.

Figure 1: a QR barcode encoding an event-specific URL in a mock OpenClinica installation

Figure 2: A list of specimens to be tested reported by a Mirth channel extracting from a mock OpenClinica installation. The linear barcode helps with data entry at the testing platform while the 2D QR barcode is used to direct the lab tech's browser to the appropriate URL in OpenClinica for subsequent data review after testing is complete.

OpenClinica 3.0 at Upcoming Industry Conferences: SoCRA, SCDM

Representatives from Akaza Research will operating an OpenClinica 3.0 exhibit at two upcoming conferences:

• Society of Clinical Research Associates (SoCRA) Annual Meeting
  September 25-27, Nashville, Tennessee (Booth #9)

• Society for Clinical Data Management (SCDM) Annual Conference
  October 4-6 in Seattle, Washington (Booth #109)

At these events, instances of OpenClinica 3.0 will be readily available for people to “test drive,” and experts will be on hand to answer questions. If you’re planning on attending let us know by sending us a message!

OpenClinica 3.0 Features Preview: Part III

Welcome to the 3rd and final installment of the OpenClinica 3.0 features preview!  This post covers the new Web Services interface that is part of 3.0 and the job scheduler that can be used to automate Data Import and Data Export jobs.

OpenClinica 3.0 allows for programmatic interaction with external applications to reduce manual data entry and facilitate real-time data interchange with other systems.  The OpenClinica web services interface uses a SOAP-based API to allow the registering of a subject and scheduling of an event for a study subject.

OpenClinica provides a WSDL (Web Service Definition Language) that defines a structured format which allows OpenClinica to accept “messages” from an external system. For example, an EHR system could register subjects for a study in OpenClinica without direct human intervention. At the same time, the EHR could also be programmatically scheduling study events for these subjects. More information about the OpenClinica API can be found on the OpenClinica developer wiki.

An early reference implementation conducted by clinical lab Geneuity used the API to create a web service which inserts data programmatically into OpenClinica CRFs directly from laboratory devices. See the post by Geneuity’s Colton Smith below.

Another major productivity tool in 3.0 is the introduction of a Job Scheduler for automating bulk data import and export.  With this feature users can define a job that will generate an export at a specified time interval.  The Jobs Scheduler can also be configured to regularly scan a specific location for CDISC ODM files and run data imports when a new file is available. This feature can be particularly helpful in automating routing functions, such as the incorporation of lab data into OpenClinica from an external system.  The lab data does need to be in a valid CDISC ODM format (this can be accomplished via another great open source tool called Mirth), but it does save a person from entering data in two applications separately.

At time of this post, OpenClinica 3.0 is currently released as a beta3, but the production ready application is soon to follow. The application is passing through the highly rigorous strictures of our quality system (think Navy Seals training for software) and the output will be fully validated and ready of use in roughly a month. Needless to say, I, and everyone else here at Akaza is very excited to be so close to releasing 3.0. It is already quite clear that this release will have a momentous, positive impact on the community.

Selling open source without mentioning open source

I am a regular reader of  “The Open Road” blog by Matt Assay on news.com. In one of his latest posts, “Getting open-source criticism wrong”, he does a great job of making the case that commercial open source software is about ease of adoption, flexibility, and choice.

It struck a chord because my sales team and I spend a great amount of time and effort explaining to prospective customers that we offer the same level of quality, stability, performance, service, and support as a proprietary vendor. In many cases we must meet a higher threshold than those vendors, because we do not have the lock-in of a commercial software license to compel customers to come back to us for repeat business. Our track record of successful long-term customer relationship is evidence we meet this threshold.

In certain sales situations, for the sake of simplicity and clarity, we have to focus only on these apples-to-apples characteristics, and do not have the opportunity to educate on the economic and technical advantages of OSS as much as we would like. It’s great to know that our open source clinical data management software technology and service offerings can stand successfully on these merits. However, as many readers of this blog already know, open source offers an additional set of critical benefits: “the ability to adopt software rapidly and at low cost, the flexibility to develop and extend their systems as they choose, and the ability to reduce risk by obtaining paid commercial-grade [or better] support”. As more decision makers are coming to understand, it is following this path, rather than the adoption of pricey, monolithic proprietary software, that leads to better outcomes and greater ROI.

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

OpenClinica on Twitter!

Chances are, you have heard of Twitter, the micro-blogging service that has become wildly popular. In every sphere, including the life sciences, Twitter has attracted a large and diverse audience. OpenClinica now operates its own Twitter account (@OpenClinica) since late spring, and in that time we’ve used it to connect with users, reach out to new people, and circulate relevant content from around the Web.

Among the people we now follow on Twitter, we have:

• Clients (@cdaffara)
• Doctors (@techydoc, @EmergencyDocs, @Home_Health)
• Other organizations that manage data (@lifesciencedata, @iDataResearch)
• Hospitals and Laboratories (@mayoclinic, @STLChildrens)
• And many more individuals and organizations.

We invite you to follow us there and also follow us on our LinkedIn group, where we can start new conversations and continue old ones.

An Opportunity for Transformational Change in Clinical Trials

Life sciences research is recognized as one of the most technologically advanced, groundbreaking endeavors of modern times. Nevertheless until very recently the preferred technology for executing the most critical, costly stage of the R&D process – clinical trials – has been paper forms. Only in 2008 did adoption of electronic alternatives to paper forms take place in more than half of new trials. This recent uptick in adoption rates is encouraging, but further transformational change in the industry is necessary to fully realize the promise of Electronic Data Capture (EDC) and associated “eClinical” technologies. Two developments that could provide the framework for such change are adoption of open data standards and the use of Open Source Software.

Data standards provide uniform ways to represent information or processes within a specific frame of reference and according to a detailed specification. A standard is “open” when it is not encumbered by patent, cost, or usage restrictions. Open Source Software (OSS) is defined loosely as software that allows programmers to openly read, redistribute, and modify the source code of that software. The combination of OSS and open standards is a proven way to deliver improved flexibility, quality, and efficiency.

A community-driven open source offering that harnesses open standards can produce robust, innovative technology solutions for use in regulated clinical trial environments. Most Open Source Software is built using a collaborative development model. The OSS development and licensing model encourages experimentation, reduces ‘reinvention of the wheel’, and allows otherwise unaffiliated parties to build on the work of others. The result is that OSS can become a key driver of increased IT efficiency and a way to wring out unnecessary costs. In many cases, users can have the best of all worlds: the ability to adopt software rapidly and at low cost, the flexibility to develop and extend their systems as they choose, and the ability to reduce risk by obtaining paid commercial-grade support.

As clinical research struggles to become more automated and efficient, we need to rely on interoperable systems to meet challenges of flexibility, quality, and speed. The OSS development model also naturally leads to the adoption of well-documented, open standards. Because OSS product designers and developers tend to reuse successful components and models where available, OSS technologies are often leading implementers of standards. For example, the National Cancer Institute’s Cancer Bioinformatics Grid (caBIG) initiative is “designed to further medicine’s potential through an open source network” based on open data standards and infrastructure that support sharing of heterogeneous data. This remarkable effort aims to connect large networks of researchers in ways that enables efficient re-use of data, eliminates duplicate systems, and enables new types of translational research.

In industry-sponsored clinical trials, standards such as the CDISC Operational Data Model (ODM), Clinical Data Acquisition Standards Harmonization (CDASH), and Study Data Tabulation Model (SDTM) have gained adoption in both proprietary and OSS software platforms. In some cases, standards are mandated for regulatory submission and reporting (SDTM, clinicaltrials.gov) and obviously must be adopted. Other cases, such as use of ODM, CDASH, and general web standards such as web services and XForms tend to be adopted to the degree they have a compelling business case.

The business case for standards centers on increasing accuracy and repeatability, enabling reuse of data, and enhancing efficiency by use of a common toolset. A well-designed standard does not inhibit flexibility, but presupposes idiosyncrasies and allows extension to support ‘corner cases’. Leading industry voices share compelling arguments how to use standards such as ODM, CDASH, XForms, and Web Services to achieve these goals. Though the details are complicated, the approach offers orchestration of disparate applications and organization of metadata across multiple systems. There is change control support and a single ‘source of truth’ for each data point or study configuration parameter, so when study designs change (as they inevitably do) or a previously committed data point is rolled back, it is automatically shared and manual updates to systems are not necessary. Because the ODM, CDASH, and SDTM are used as a common “language”, the systems know the meaning and structure of data and can process transactions accordingly. Here’s a tangible example:

Lets imagine an IVR system wanted to check with an EDC system if a subject was current in a study (current meaning not dropped out, early terminated or a screen failure).  A Web Service could be offered by the EDC system to respond with a ‘True’ or ‘False’ to a call ‘IS_SUBJECT_CURRENT’ ?  Of course hand-shaking would need to occur before it hand [sic] for security and so on, but following this, the IVR system would simply need to make the call, provide a unique Subject identifier, and the EDC system web service would respond with either ‘True’ or ‘False.  With Web Services, this can potentially occur in less than a second.

Electronic Data Capture – Technology Blog, September 28, 2008

While this integration requirement could be satisfied by development of point-to-point, proprietary interfaces, this approach is brittle, costly, and does not scale well to support a third or fourth-party system participating in the transaction. It is critical that standards be open so that parties can adopt and implement them independently, and later interface their systems together when the business case calls for it. A leading industry blogger makes the case for the openness of standards within the ODM’s ‘Vendor Extension’ architecture: ”The ODM is an open standard, the spec is available for free and anyone can implement it. This encourages innovation and lowers the barriers to entry and therefore costs. Vendor Extensions are not open, the vendor is under no obligation to share them with the market and the effect is that meta-tools and inter-operability are held back.”

Having the software that implements these standards released as open source code only strengthens its benefits. Proprietary software can implement open standards, however given the proprietary vendor’s business interest to lock-in license revenue, might the vendor be tempted into tweaking or ‘extending’ the standard in a way that is encumbered to lock users into their platform? This strategy of “embrace, extend, extinguish” was made famous in the Microsoft anti-trust case of the 1990s, where it came to light that the company attempted to apply these principles to key Internet networking protocols, HTML and web browser standards, and the Java programming language. They hoped to marginalize competing platforms that did not support their “extended” versions of the standards. Thankfully, they had limited success in this effort, and the Internet has flourished into the open, constantly innovating, non-proprietary network that we know today. The eClinical technology field is at a similar crossroads. By embracing open standards, and working concertedly to provide business value in re-usable OSS technology, we can achieve a transformation in the productivity of our clinical technology investments.