Archive | Clinical RSS for this section

Digital Clinical – Ushering in new technologies into Clinical Research

What is Digital Clinical?

I noticed this term used quite often in my conversations with customers, colleagues and industry analysts in the last couple of years. As is the case with many things in the past, the building blocks of “Digital Clinical” have been in the works for a long time now. At its core, in my opinion, it is all about various technology advances coming together to progress and enhance the clinical research. To name a few, the core technologies/developments include Mobility, Analytics, Social, Cloud Computing, Big Data, Semantic technologies. As mentioned while this is not a comprehensive list, it is a good start to understand how these are being leveraged to improve clinical research and the impact they are having on Heathcare & Life Sciences Industry, Patients and society at-large.

Areas of Focus:

The following are some of the focus areas across the R&D Value Chain being considered as Digital Clinical initiatives by various life sciences companies in the market space:

  • Mobile Data Collection in clinical trials, patient reported outcomes and general quality of life data
  • Data aggregation from Payer, Provider, Rx, Clinical and other Health Economic & Outcomes Research Data
  • Ontology based repositories, Master data management, meta data repositories and text analytics
  • Bio-Sensor and other wearable data capture, aggregation & analysis by leveraging cloud computing
  • Mining of data from EHR/EMR, Social Media and other data sources
  • Leverage OMOP, Sentinel and other such industry initiative outcomes to kick-start Real World Evidence/Real Life Evidence strategies

Business Use Cases being considered:

Some of the use cases being considered are:

  • Site selection and Patient Recruitment
  • Use wearable Bio-sensors in Clinical Trials
  • ePRO
  • Adverse Events in Social Media & Safety Surveillance
  • Protocol Validation : Inclusion/Exclusion criteria assessment

I hope this gives a high level overview of what Digital Clinical means, some of the technologies influencing and enabling it and practical use cases being considered by the industry to leverage these technologies and provide better quality of life to patients.

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.

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.

Leveraging Technology to Improve Patient Recruitment for Clinical Trials

I was asked to respond to a costumer inquiry recently to help them with providing a solution to improve their patient recruitment in clinical trials. Initially, I thought this must be a question to one of our experts in our CRO. However, I kept thinking about it and realized there isn’t a silver bullet that would solve this problem. There is no commercial off-the-shelf solution to fix this problem. Sponsors, CROs, Site Coordinators, PIs and other stakeholders involved in the clinical research have been struggling to address this situation but it is still a challenge and is the root cause of delays in clinical trials which will result in increased and lower Returns on Investment (ROI).

Discovery and development of medical products take a long time and consume lot of resources (Effort & Cost) from sponsors from molecule to patient. This involves discovery of the molecule to pre-clinical and clinical development to marketing & sales. A lot of these resources are typically spent in late stage (Phase III) of clinical trials. One of the root causes for delays in clinical trials in phase III is delay in recruiting the patients that meet the inclusion criteria of the protocol. Another key concern is patient drop out during the course of the trial. These issues not only delay the trial but also force the sponsors and CROs to consider changes to the protocol or force them to take alternative steps to ensure the necessary patient population is recruited to continue the trial.

Adherence rates across the duration of therapy

Adherence rates across the duration of therapy

According to a report on Patient Adherence by Capgemini consulting [1], “Patient adherence levels vary between 50% for depression to 63% for enlarged prostate. On average, adherence levels drop over the course of the patient journey from 69% of patients filling their first prescription to 43% continuing their treatment as prescribed after 6 months.”

Also, if you notice in the attached diagram, 31% of the patients recruited will not last until they fill their prescription. Further, by the sixth month almost 57% patients do not show up for their refills. At this rate, in order to continue the trial, the sponsor has to continue to recruit patients during the course of the trial. This recruitment due to a drop out comes at a cost. The report has also identified that the cost of recruiting a new patient is 6 times the cost spent by sponsors to retain an existing patient.

So, what are the sponsors/CROs doing to improve the patient recruitment and reduce the drop outs? The key to fixing any problem is identifying the root cause. For this problem, there are multiple areas of failure and hence requires a comprehensive, multi-pronged strategy to fix the problems.  Some of the best practices being adopted by sponsors and/or CROs include:

  • Comprehensive and incremental approach to integrated solutions rather than silos  by leveraging :

–          Patient registries

–          Better communication between patients and clinicians

–          Electronic records

–          Better engagement of physicians

  • Refine Site selection

–          Analysis to select better performing sites

  • Patient centric recruitment process

–          Simplify informed consent process (move to informed choice)

  • Collaborative approach to Protocol design

–          Engaging Principal Investigators (PIs) in protocol design by research coordinators

  • Improve patient retention

–          Better follow-up systems to increase adherence

–          Enhanced post-trial engagement

In order to implement these best practices sponsors can adopt technology solutions like:

  1. Create strategy to improve the recruitment process by leveraging technology solutions
  2. Analyze site performance and select high performing sites and drop out non-performing sites
  3. Patient Recruitment Systems to search and match protocol eligibility criteria with patient’s available Electronic Health Records (EMRs, Narratives, Health Insurance and Claims Documents)
  4. Social Media tools to target patient communities to identify potential subjects from the available patient population
  5. Social Media tools to gauge trial buzz in patient and physician community
  6. Study & Investigator Portals to enhance engagement and collaboration between study coordinators and Principal Investigators
  7. Mobile Trial Adherence Systems to alert and notify patients of visits and other trial compliance activities to reduce drop outs
  8. Key Opinion Leader portals to better engage physicians
  9. Analytics to identify potential subjects, site initiation process and performance, patient recruitment effectiveness, patient drop out alerts etc.
  10. Advanced analytics to simulate recruitment performance based on historical data and thus tailoring recruitment strategy based on sites and other factors

Despite all these best practices and solutions, will be able to help the sponsors and CROs in:

  • Collecting historical and real time operational and scientific data
  • Performing objective analysis of data
  • Visualization of such performance
  • Identifying the bottlenecks and root causes for the problems
  • Helping in making decisions to select the right sites, recruit the right patients, avoid costly drop outs and improve the collaboration and communication among Patients, Primary Investigators and other stakeholders.

I will be writing about each solution in my “Top 10” list above. As I mentioned in the beginning of this post, there is no silver bullet for this problem. A strategic and integrated approach to create solutions that will aid in collecting, analyzing and decision making is the only way to solve this problem.

As always, please let me know your feedback. If you have more insights into the problem or solutions, I will be more than glad to discuss with you and also post some of the inferences in a subsequent post, as promised. Happy Reading!!!

References:

  1. “Patient Adherence: The Next Frontier in Patient Care – Vision & Reality, 9th Edition Global Research Report”  by Capgemini Consulting

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.

%d bloggers like this: