Tag Archive | Clinical

Clinical Platforms on the Cloud

It is amazing to see how fast things change, when right technology comes along, picks up the willfully reluctant “legacy” way of doing things and takes them on a ride of their life time. Not too long ago paper based clinical trials was the norm, and still is in some countries. Then came the electronic data capture systems and technology. While that is, people used eCRFs same way as they did paper CRFs. Slowly that started to change with data validation, edit checks etc.

With the cloud revolution came the thought of having clinical data capture/management systems on the cloud and be managed by a third party while pharma companies controlled the protocol and trial design as well as data transformation, analysis and submission management. Off late we are seeing companies being more open to store and share clinical trial data on the cloud. I think the days of ‘Clinical Platforms on The Cloud’ as the norm will soon be a reality. These platforms will not be limited to Clinical Trial Data but will host systems that provide end-to-end clinical research process support capabilities. Not only that, they will also stretch the boundaries further, by accommodating social media, mobile, big data & analytics capabilities.

Future of technology companies that are pioneers in enabling this transformation is going to be interesting and bright with ample opportunities to take the lead and leap to the next orbit.

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Patient Reported Outcomes and Mobile Solutions

As stated in some of my previous posts, I have been working in the Life Sciences industry for over 8 years now. That amounts to a little bit over half my career till date. It has been a good journey with appreciation towards the greater good that this industry does in making people’s lives better. I agree that there is an image problem due to multiple incidents that compromised patients’ safety for profits or other trade-offs. However, all in all I personally think that there is much more good done than harm.

One thing that Life Sciences industry is not is “being technologically savvy”. Even this statement, if applied with in an organization, the degree of applicability changes from one business unit to the other. More than that, it has to be stated that the units that are “more regulated” are much behind in new technology adoption. The units that I have been working with the most is Research & Development (R&D). Even within this unit, the Research Teams, specifically Drug Discovery teams are more open to adopting new and advanced technology compared to the Drug Development teams.

As part of my day job, one of my goals is to continuously monitor new technology trends, specifically within the Life Sciences realm and help our teams develop new solutions and services. Identifying the solutions to be developed is not an easy job considering the economic constraints under which most companies are working in this “New Economy”. Luckily for me, another goal of my job is to find the right partners who would help us develop these new solutions and services so we can go to market faster.

One such partnerships is with a company that developed a Mobile Solution to enable Patient Reported Outcomes (PRO). I did a joint Webinar with them today titled “Maximizing Patient Reported Outcome (PRO) compliance in Clinical Trials – Leveraging Mobile Solutions“. While trying to put together a presentation that highlight the trends in Mobility adoption in general and adoption in Life Sciences and specifically Clinical Trials, I found out some surprising facts that the Mobile industry will be growing at a CAGR of 15.6% for the next 5 years (IDC Press Release).Also, according to another IDC report, Smart Phones will out sell the Netbooks, Desktops, Laptops and such combined in the next couple of years.

Patient Reported Outcomes (PRO) is all about getting information directly from the patients, without any interpretation  by specialists. In fact, FDA defines this as “A measurement based on a report that comes directly from the patient (i.e., study subject) about the status of a patient’s health condition without amendment or interpretation of the patient’s response by a clinician or anyone else. A PRO can be measured by self-report or by interview provided that the interviewer records only the patient’s response“. In my opinion, it looks obvious that leveraging the Mobile devices to run PRO trials improves the overall quality of data as well as the response time, not to mention the compliance to a large extent. The key is the coverage of last mile, i.e. enabling the patients to be reminded of any delays in submission of assessments. This will also open up a more personal service channel that can be leveraged to build a closer relationship with the patients.

As with adoption of many other disruptive technologies like Social Media, Cloud Computing etc. the regulatory compliance questions need to be addressed for any solutions developed to be used on Mobile devices. You can refer to my previous post titled “iPads, iPhones, Androids, Windows Mobile Phones, Life Sciences and Compliance” on what are the regulatory compliance requirements (specifically, guidance by FDA) to be considered in this context. The key is to ensure that the patient data collected is secure and the solution works in situations where the patient does not have connectivity. Also, the overall cost of the trial would come down considerably if the protocol permits ‘Bring Your Own Device (BYOD)” model for patients participating in the trial. Of course, this depends on the nature of the protocol and the trial design.

All in all, with the phenomenal up tick in Smart Phone sales, more so in ‘Pharmerging” markets like India and China which are also becoming the “Hubs” of future growth for Life Sciences companies, it is essential that Mobile solutions be developed and used to cover the last mile i.e. for the user touch points like Physicians, Patients and Pharmacies.

As always, your feedback and comments are welcome. I also encourage you to share Pros and Cons of the adoption based on your practical experience. I will be even more interested in any technological challenges you may have come across in the adoption process.

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 CIO.com 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.

Conclusion

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

Clinical Information Super Highway and Integration

Generic Industry Challenges:

Expiring patents, drying pipelines, increasing budgets, stringent regulations and cost pressures are some of the challenges faced by the Life Sciences industry. While these are macro challenges, teams responsible for clinical, regulatory and safety for clinical trials, regulatory submissions and patient safety respectively have a totally different set of challenges that they deal on a regular basis.

Information Silos:

Data and documents generated during the course of the trials are managed by IT systems that have been either built in house or procured off the shelf. A major challenge that is faced by IT teams in large enterprises is to provide information and intelligence to their business teams that would cross (by integrating information) these siloes and help them make sound business decisions.

SOA based Information Integration:

Imagine a situation where all these systems in the clinical enterprise are integrated and connect seamlessly.  The glue that binds all these systems can be a fully Service Oriented Enterprise Service Bus (ESB). Not just that, it should also be standards based. This will reduce the dependency on interfaces between systems in use and future proof the enterprise from replacement, retirement and enhancements to the individual systems. In order to bring visibility to data and documents across the systems there is a need to aggregate data and documents into Clinical Data Repository (CDR) and an Enterprise Electronic Document Management System (EEDMS).

Clinical Information Enterprise Service Bus

Clinical Information Super Highway

The diagram above depicts a few information systems within the Clinical, Regulatory and Safety areas of Life Sciences R&D. This integration has to be:

  • Loosely Coupled (SOA)
  • Standards Based (CDISC / HL7)
  • Open Architecture (XML, SOAP) and
  • Survive system upgrades, replacements and retirements

Advantages:

Some of the advantages with the above mentioned integration would be:

  • Integrated Clinical Enterprise Information Systems
  • Easier and simpler integrations with partners and third parties
  • Adoption of standards
  • Integrated data and documents to enable faster decision making
  • Faster Time to Market
  • Lower Total Cost of Ownership and
  • Better ROI
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