The OpenClinica Platform – Developer Round Table Discussions

OpenClinica is a clinical trial software platform that aims to provide data capture, data management, and operations management functionality to support human subjects-based research. It can be used for traditional clinical trials as well as a wide variety of other types of human subjects-based research.

Our vision for the product is to provide data capture, data management, and operations management functionality out-of-the-box, in an easily configurable, usable, and highly reliable manner. The underlying platform should be interoperable, modular, extensible, and familiar – so users can solve specific problems, in a generalizable way.

This past spring, the development team here at OpenClinica, LLC held a series of round table discussions about how this vision is reflected in the product. Our goals were to learn critical standards and information models needed for our technology to truly reflect this vision, to develop a consistent, shared vocabulary for the problem domain and the OpenClinica technology, and identify the most urgent opportunities to put these lessons into practice in the product and the community. In particular, we spent a lot of the time in these discussions about how OpenClinica’s use of the CDISC Operational Data Model helps enable this vision.

The discussions were invigorating and thought-provoking. We’ve recorded them to share with the greater community of OpenClinica developers, integrators, and others who want to better understand how the technology works, the design philosophy behind it, and where we’re going in the future. The videos are embedded below.

But before getting to the videos, here’s a bit more background on how we think about OpenClinica as a product and a platform.

First, OpenClinica functionality should be ready out-of-the-box, easily configurable and highly usable. Some of the most important features include:

  • Data definition and instrument/form design with no or minimal programming
  • Sophisticated data structures such as repeating items and item groups
  • Support for a wide variety of response types and data types (single select, multiple choice, free text, image)
  • Data management and review capabilities (including discrepancy management and clinical monitoring) with flexible workflows
  • ALCOA-compliant controls and audit history over all data and metadata, including electronic signature capabilities
  • Patient visit calendar design with management of multiple patient encounters and multi-form support
  • Reporting and data extract to a wide variety of formats (tab, SPSS, CDISC ODM)
  • Ability to combine electronic patient reported outcome (ePRO) data with clinically reported data using common form definitions (write once, run anywhere)
  • Deployment via multiple media, mobile or standard web browser

Many of these things have already been implemented, and more are under development.

The core concept around which OpenClinica is organized is the electronic case report form (CRF). In OpenClinica, a CRF is a resource that is essentially a bunch of metadata modeled in CDISC ODM with OpenClinica extensions. It doesn’t (necessarily) have to correspond to a physical or virtual form; it may represent a lab data set or something similar. An OpenClinica Event CRF is that same bunch of metadata populated with actual data about a particular study participant. Thus, it combines the metadata with the corresponding item (field) data, with references to the study subject, event definition, CRF version, and event ordinal that it pertains to. In this conceptual view of the world, CRFs (as well as CRF items, studies, study events, etc.) are resources with core, intrinsic properties and then some other metadata that has to do with how they are presented in a particular representation. Built around these core resources are all the workflow, reporting, API, security, and other mechanisms that allow OpenClinica to actually save you time and increase accuracy in your research.

Second, OpenClinica should be interoperable. The ultimate measure of interoperability is having shared, machine readable study protocol definitions, and robust, real-time, ALCOA-compliant exchange of clinical data and metadata that aligns with user’s business processes. It should be easy to plug in and pull out or mix-and-match different features, such as forms, rules, study definitions, report/export formats, and modules, to transport them across OpenClinica instances or interact with other applications. Establishing well defined methods and approaches for integration into existing health data environments is a key goal of interoperability.

Third, OpenClinica should be modular and extensible. OpenClinica already provides some of the most common data capture and data management components and aims to have a very broad selection of input types, rules, reports, data extracts, and workflows. However OpenClinica developers should also have the freedom to come up with their own twist on a workflow, module, or data review workflow and have it work easily and relatively seamlessly with the rest of OpenClinica. User identification, authentication, and authorization should be highly configurable and support commonly used general purpose technologies for user credentialing and single-sign-on (such as LDAP & OAuth).

The CRF-centric model allows us a great deal of flexibility and extensibility. We can support multiple modalities, with different representation metadata for rendering the same form, or perhaps the shared representation metadata but applied in a different way (i.e. web browser vs. mobile vs. import job). We can address any part of the CRF in an atomic, computable manner. This approach has been successfully applied in the Rule Designer, which takes the ODM study metadata and allows browsing of the study CRFs and items, with the ability to drag and drop those resources to form rule expressions. Features such as rules and report/export formats are represented as XML documents. These documents define how the features behave in standardized ways so that one rule can, say, be easily replaced with another rule without having to modify all the code that makes use of the rule.

Finally, OpenClinica aims to be familiar in the sense of allowing data managers, developers, statisticians to work in a design/configuration/programming environment that they already know. Programmers don’t all have the same experience, and it would be somewhat limiting to force OpenClinica developers to all use the same language (Java) that OpenClinica was written in. We are constantly looking at ways to make it possible (not to mention reliable and easy!) for users and developers to interact with and extend OpenClinica in a programmatic way. This can mean anything from data loading to more meaningful integrations of applications common to the clinical research environment. As proponents of open, standards-based interoperability, our starting point is always to develop interfaces for these interactions based on the most successful, open, and proven methods in the history of technology – namely the protocols that power the World Wide Web (such as HTTP, SSL, XML, OAuth 2.0). They are relatively simple, extensively documented, widely understood, and well-supported out of the box in a large number of programming and IT environments. On top of this foundation, we rely heavily on the wonderful work of CDISC and the CDISC ODM to model and represent the clinical research protocol and clinical data.

Session 1:  from 30-March-2012 (start at the 5 min 20 sec mark)

Session 2:  from 06-April-2012 (start at the 1 min 25 sec mark)

Session 2a:  from 20-April-2012

Session 3:  from 27-April-2012

Session 4:  from 11-May-2012

Why Open Source is Good for International Health Research (and Everyone Else)

A recent article titled, “Could an Open-Source Clinical Trial Data-Management System Be What We Have All Been Looking For?”, published in PLoS (Public Library of Science) proposes that “international health research organisations combine their efforts and spending power and assist with the development of systems that are open to all.” This is a bold statement with, in my opinion, solid rationale.
 

The authors, Greg W. Fegan and Trudie A. Lang, manage numerous clinical trials for the Kenya Medical Research Institute–Wellcome Trust Collaborative Research Programme in Kilifi, Kenya. Like many other research organizations in developing countries, their work largely focuses on finding treatments for “neglected diseases” such as malaria, hookworm, and encephalopathies. They clearly communicate the inability for proprietary eClinical software to be a widely useable solution in such settings due to costly and restrictive licensing. 
 

However, Fegan and Lang define the appeal of open source as something greater than financial savings (although this is a strong motivation). In addition to freedom from license fees, open source clinical trial software built with open components and open standards is more “modifiable and amenable for use with existing software already employed.” Perhaps the most significant point made is that open source can be a more powerful way to promulgate standards and better leverage the collective efforts of disparate research institutions.
 

Indeed, the authors also point out that the impact of a well designed and supported open source eClinical system “can be beneficial to all clinical researchers” and urge “international health research organisations to combine their efforts and spending power and assist with the development of systems that are open to all and truly fit for purpose.”
 

The paper closes with the following call to action:

“Research organisations and funders should combine efforts to produce an open-source solution for trial data management. A shared platform could then be easily established, and would bring wider benefits such as electronic submission to regulators, automated sharing of data, and contribution to important public databases such as pharmacovigilance and drug-monitoring registries.

We believe that an open-source approach to a truly designed-for-purpose data-management system for clinical trials is attractive. Such a system would save money by eliminating the reliance on the use of expensive database software systems and their administrators. This would empower and enable a wider variety of people to conduct trials, as the question of capturing, cleaning, and extracting data would not be overly daunting or expensive. This point is significant, as it may encourage more investigators in resource-poor settings to take part in high-standard research that would otherwise be out of reach and beyond their capacity. Surely this would increase the scope and variety of trials that are conducted. Our hope for this article is that it will begin a debate on this topic, and lead to a concerted effort to lobby the international research and donor community to make sure this barrier to trial conduct is understood and addressed.”

I encourage you to read the full article online at the PLoS website.