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Oracle Argus Safety and Adverse Event Reconciliation

Adverse Events / Adverse Drug Reactions are imperative to all interventional therapies, be it drugs, devices, vaccines or biologics. The frequency, seriousness, breadth etc. may vary from drug to drug, person to person. We have made a lot of progress in ensuring that all the adverse events are identified, processed and reported to regulators. However there are still a lot of challenges in ensuring consistency, of how this is done across organizations, in terms of people, process and technology.

Oracle’s Argus Safety Suite is a leading drug safety system in the market. It is a very good application with rich features. However, there are still certain functions, the industry needs, that needs to mature and some others that are still evolving. I would like to write about one such features i.e. Adverse Event Reconciliation. The module in Argus Suite that provides this functionality is “Argus Reconciliation”. The datasheet lists the benefits of reconciliation and the ability of this module to make it easy, to reconcile the AE data between Argus and other Clinical Data Management systems.

What is reconciliation?

Reconciliation is typically the process of identifying any discrepancies in the data captured for the Adverse Events in Clinical Data Management system and Safety System.

Why do they have similar data in two systems?

Adverse event data is captured in CDM systems as part of the clinical trial data collection process. This data is also entered in Safety Systems in order to capture, process and report it to regulators. Sponsors should ensure that the data that is submitted to regulators during the course of the trial and the data that is submitted as part of the overall submission are consistent. Hence, reconciliation of data is essential. Ideally this situation should not arise if the data is collected electronically and the systems are integrated so the information flows bi-directionally. However, that is not the case in real world.

For customers that have Argus Safety there are essentially three options for reconciliation:

  1. Manual
  2. Automated  (COTS) and
  3. Automated (Custom)

Manual: This method, to a large extent is self-explanatory. One has to extract the AE records from the Safety and CDM systems and compare the data elements line item-by-line item. Any discrepancies identified may lead to a) change to the data in CDM system or b) change to the data in Safety system

Automated (COTS): This method can be used in case a commercially available integration exists between the CDM system and Argus. If we look at some of the popular CDM systems in the market, InForm (Oracle), Oracle Clinical and Rave (Medidata) two are from Oracle. The following information outlines the integration in case of each CDM system:

1)      In case of Oracle Clinical, the reconciliation is available through the Argus Reconciliation module. Customers have to buy licenses to this module as part of the Safety Suite in order to leverage this functionality.

2)      For Inform to Argus integration, Oracle has released a Process Integration Pack (PIP) that is part of their Application Integration Architecture (AIA), which in turn is part of their Fusion Middleware strategy. This essentially requires customers to install an AIA foundation pack and then purchase the PIP (Oracle® Health Sciences Adverse Event Integration Pack for Oracle Health Sciences InForm and Oracle Argus Safety) and install/configure it.

3)      Medidata Rave’s Safety Gateway product can be leveraged for integration between Rave and Argus Safety. This is basically an E2B based integration.

Automated (Custom): In cases where the volume of cases is very high, which eliminates the manual option, and a COTS integration does not exist, customers may have to rely on a custom integration. This can be accomplished in multiple ways. However, an E2B based integration is recommended.

Hope this post helps you get basic knowledge about AE reconciliation and options available for reconciliation between Argus Safety and three popular Clinical Data Management systems. As always, your feedback will be very valuable and welcome.

Does Clinical Data qualify as “Big Data”?

I was at an Analyst conference last week where I met a couple of analysts (no pun intended :-)) focused on Life Sciences who felt that “Big Data” is a tough sell in Life Sciences, except for Genomic Data. That made me think. I always associated “Big Data” with the size of the data sets running into Peta Bytes and Zetta Bytes. What I learned in my journey since then is that the characteristics of Big Data does not start and end with the Size.

This article on Mike 2.0 blog by Mr. Robert Hillard, a Deloitte Principal and an author, titled “It’s time for a new definition of big data” talks about why Big Data does not mean “datasets that grow so large that they become awkward to work with using on-hand database management tools” as defined by Wikipedia. He goes on to illustrate three different ways that data could be be considered “Big Data”. For more, please read the blog.

One quality he explained that is of interest to me is “the number of independent data sources, each with the potential to interact”. Why is it of interest to me? I think Clinical Data, in the larger context of Research & Development, Commercialization and Post Marketing Surveillance definitely fits this definition. As explained in one of my previous posts title “Can Clinical Data Integration on the Cloud be a reality?“, I explain the diversity of clinical data in the R&D context. Now imagine including the other data sources like longitudinal data (EMR/EHR, Claims etc.), Social Media, Pharmacovigilance so on and so forth, the complexity increases exponentially. Initiatives like Observational Medical Outcomes Partnership (OMOP) have already proven that there is value in looking into data other than the data that is collected through the controlled clinical trial process. Same thing applies to some of the initiatives going on with various sponsors and other organizations in terms of making meaningful use of data from social media and other sources. You might be interested in my other post titled “Social Media, Literature Search, Sponsor Websites – A Safety Source Data Integration Approach” to learn more about such approaches that are being actively pursued by some sponsors.

All in all, I think that the complexities involved in making sense of disparate data sets from multiple sources and analyzing them to make meaningful analysis and ensure the risks of medicinal products outweigh the benefits will definitely qualify Clinical Data as “Big Data”. Having said that, do I think that organizations would be after this any time soon? My answer would be NO. Why? The industry is still in the process of warming up to the idea. Also, Life Sciences organizations being very conservative, specially when dealing with Clinical Data which is considered Intellectual Property as well as all the compliance and regulatory requirements that goes with the domain, it is going to be a long time before it is adopted. This article titled “How to Be Ready for Big Data” by Mr. Thor Olavsrud on website outlines the current readiness and roadmap for adoption by the industry in general.

The next couple of years will see evolution of tools and technology surrounding “Big Data” and definitely help organizations evolve their strategies which in turn will result in the uptick in adoption.

As always your feedback and comments are welcome.

Can “Clinical Data Integration on the Cloud” be a reality?

The story I am about to tell is almost 8 years old. I was managing software services delivery for a global pharmaceutical company from India. This was a very strategic account and the breadth of services covered diverse systems and geographies. It is very common that staff from the customer organization visit our delivery centers (offsite locations) to perform process audits, governance reviews and to meet people in their extended organizations.

During one such visit a senior executive noticed that two of my colleagues, sitting next to each other, supported their system (two different implementations of the same software) across two different geographies. They happened to have the name of the systems they support, pinned to a board at their desks. The executive wanted us to take a picture of the two cubicles and email to him. We were quite surprised at the request. Before moving on to speak to other people he asked a couple of questions and realized the guys were sharing each other’s experiences and leveraging the lessons learnt from one deployment for the other geography.  It turned out that this does not happen in their organization, in fact their internal teams hardly communicate as they are part of different business units and geographies.

The story demonstrates how these organizations could become siloes due to distributed, outsourced and localized teams. Information Integration has become the way of life to connect numerous silos that are created in the process. Clinical research is a complex world.  While the players are limited, depending on the size of the organization and the distributed nature of the teams (including third parties), information silos and with that the complexity of integration of data increases. The result is very long cycle times from data “Capture” to “Submission”.

Clinical Data Integration Challenges

The challenges in integrating the clinical data sources are many. I will try to highlight some of the key ones here:

  • Study Data is Unique: depending on the complexity of the protocol, the design of the study, the data collected varies. This makes it difficult to create a standardized integration of data coming in from multiple sources.
  • Semantic Context: while the data collected could be similar, unless the context is understood, it is very hard to integrate the data, meaningfully. Hence, the integration process becomes complex as the semantics become a major part of the integration process.
  • Regulations and Compliance: Given the risks associated with clinical research, it is assumed that every phase of the data life should be auditable. This makes it very difficult to manage some of the integrations as it may involve complex transformations along the way.
  • Disparate Systems: IT systems used by sponsors, CROs and other parties could be different. This calls for extensive integration exercise, leading to large projects and in turn huge budgets.
  • Diverse Systems: IT systems used at each phase of the clinical data life cycle are different. This makes sense as the systems are usually meant to fulfill a specific business need. Even the functional organizations within a business unit will be organized to focus on a specific area of expertise. More often than not, these systems could be a combination of home grown and commercial off the shelf products from multiple vendors. Hence, the complexity of integrations increases.

What is Integration on the Cloud?

As mentioned earlier, integration is a complex process. As the cloud adoption increases, the data may be distributed across Public, Private (Includes On-Premise applications) and Hybrid clouds. The primary objective of integration on the cloud is to provide a software-as-a-service on the cloud to integrate diverse systems. This follows the same pattern as any other cloud services and delivers similar set of benefits as other cloud offerings.

The “Integration on Cloud” vendors typically offer three types of services:

  1. Out-of-Box Integrations: The vendor has pre-built some point-to-point integrations between some of the most used enterprise software systems in the market (like ERPs, CRMS etc.)
  2. Do-it-Yourself: The users have the freedom to design, build and operate their own integration process and orchestrations. The service provider may provide some professional services to support the users during the process.
  3. Managed Services: the vendor provides end-to-end development and support services

From a system design and architecture perspective, the vendors typically provide a web application to define the integration touch points and orchestrate the workflow that mimics a typical Extract-Transform-Load (ETL) process. It will have all the necessary plumbing required to ensure that the process defined is successfully executed.

Who are the players?

I thought it would be useful to look at some of the early movers in this space. The following is a list (not exhaustive and in no particular order, of course) of “Integration on Cloud” providers:

  1. Dell Boomi : Atom Sphere
  2. Informatica : Informatica CLOUD
  3. IBM : Cast Iron Cloud Integration
  4. Jitterbit : Enterprise Cloud Edition

These vendors have specific solution and service offerings. Most of them provide some out-of-the-box point-to-point integration of enterprise applications like ERPs, CRMs etc. They also offer custom integrations to accomplish data migration, data synchronization, data replication etc. One key aspect to look for is “Standards based Integration”. I will explain why that is important from a clinical data integration perspective later. While this offering is still in its infancy, there are some customers that use these services and some that are in the process of setting up some more.

Clinical Data Integration on Cloud

Many of you dealing with Clinical Data Integration may be wondering as to “Why bother with Integration on the Cloud?” while we have enough troubles in finding a viable solution in a much simpler environment. I have been either trying to create solutions and services to meet this requirement or trying to sell partner solutions to meet this requirement for the past 4 years. I will confess that it has been a challenge, not just for me but for the customers too. There are many reasons like, need to streamline the Clinical Data Life Cycle, Data Management Processes, retiring existing systems, bringing in new systems, organizational change etc. Not to mention the cost associated with it.

So, why do we need integration on the cloud? I firmly believe that if a solution provides the features and benefits listed below, the customers will be more than willing to give it a strong consideration (“If you build it, they will come”). As with all useful ideas in the past, this too will be adopted. So, what are the features that would make Clinical Data Integration on the cloud palatable?  The following are a few, but key ones:

  1. Configurable: Uniqueness of the studies makes every new data set coming in from partners unique. The semantics is also one of the key to integration. Hence, a system that makes it easier to configure the integrations, for literally every study, will be required.
  2. Standards: The key to solving integration problems (across systems or organizations), is reliance on standards. The standards proposed, and widely accepted by the industry (by bodies like CDISC, HL7 etc.) will reduce the complexity. Hence, the messaging across the touch points for integration on the cloud should rely heavily on standards.
  3. Regulatory Compliance and GCP: As highlighted earlier, Clinical Research is a highly regulated environment. Hence, compliance with regulations like 21 CFR Part 11 as well as adherence to Good Clinical Practices is a mandatory requirement.
  4. Authentication and Information Security: This would be one of key concerns from all the parties involved. Any compromise on this would not only mean loss of billions of dollars but also adverse impact on patients that could potentially benefit from the product being developed. Even PII data could be compromised, which will not be unacceptable
  5. Cost: Given the economically lean period for the pharma industry due to patent expiries and macro-economic situation, this would be a key factor in the decision making process. While the cloud service will inherently convert CapEx to OpEx and thus makes it more predictable, there will be pressure to keep the costs low for add-on services like “new study data” integration.


All in all, I would say that it is possible, technically and economically and also a step in the right direction to overcome some existing challenges. Will it happen tomorrow or in the next 1 year? My answer would be NO. In 2 to 3 years, probably YES. The key to making it happen is to try it on the cloud rather than on-premise. Some of the vendors offering Integration on Cloud could be made partners and solve this age old problem.

Update on 03/27/2012:

This post has been picked up by “Applied Clinical Trials Online” Magazine and posted on their blog -> here

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