Clinical Data Analysis – Taking a centre-stage in Clinical Trials

cda

Healthcare, as in any other sector, has been pressing forth towards adopting and using technology as a tool for simplifying management of patient data, healthcare and healthcare related processes. Millions of dollars in clinical trials are spent to come up with newer drugs. Clinical trials are long, expensive and their timely success is imperative for the long-term growth of a life sciences organization. In this blog, Saama’s Amit Patil, talks about how the generated data can be analysed effectively to mine meaningful insights.

With increasing competition, the pharma industry is under immense pressure to cut costs during these Clinical trials where millions of dollars are spend on-site identification, site activation, patient recruitment, amongst others. In addition, stringent FDI norms and the overall inflation across the globe is ballooning the cost of conducting studies that result in lower profit margins. An estimated investment on a drug from discovery to FDA approvals is $2.6 billion says Tufts and 80% of this expense is incurred during the clinical trials and that is the criticality of this stage as an enormous corpus is at stake.

This is the reason that in recent years there’s been a wide scale adoption and evolution of Clinical Data Analysis (CDA) in pharma companies to:

  • Identify the most painful and time consuming areas in the complete life cycle of Clinical operations
  • Reduce time-to-market to have better market advantage and edge over competitors

A sneak-peak into the ‘how’s’ of clinical trials

When a new drug is to be introduced for a particular treatment, the cycle time for the drug to enter the market varies anything from 3 – 5 years to 10-15 years. This depends on several factors including the drug testing timeframes on animals and then moving on to human testing, movement to market and awareness of drug  and analysing the effects of the drug.
Clinical trials are classified in three stages mostly:

Phase I: This stage refers to evaluation of a new drug or treatment in a small group to check the effects of the drug

Phase II:  Treatment is given to a slightly larger number of patients to check for safety and efficacy

Phase III: Given to a larger group to check for effectiveness and monitor the effect and report back to the marketing group to manage rollout to the world

The entire process is a lengthy and expensive endeavour. Data is generated at nearly every stage of the clinical trial lifecycle and comes in both structured and unstructured format. This data has various semantic and contextual nuances for driving analytics and deriving meaningful insights.

To ensure this, Pharmaceutical organizations are impelled to make prudent investments for effective, easy access and seamless exchange of data in clinical trials that will result into improved success rate while achieving compliance and cost cutting and addressing slowdown in momentum of clinical trial operations.

Analysing the available data from clinical trials will help pharma companies identify:

  • Least productive zones in terms of country, districts, hospitals and CRO’s
  • Areas of maximum delay and the reason for the right shift of timelines
  • Inconsistency over data from multiple sites
  • Need for central data storage
  • Analytic environment to support cross functional and cross department needs

 Approach

For ensuring an overview on data management and analysis aspects of clinical research, companies today are adopting CDA solutions. With deep dive knowledge in various pain areas of Clinical trials and various persona based scenarios associated with the domain, the set of pre-existing KPI’s are identified.

clinical trials

Figure 1: Evolution of CDA

CDA as a solution helps:

  • Closing the manual exchange of Data between CRO( Clinical Research Organizations)
  • Supporting pro-active decision making by marking the critical risk areas
  • Reduce cycle time to market
  • Reduce process inefficiencies
  • Reduce cost

Solution:

The CDA solution will help the pharma industry to achieve their goals and suppress their long lasting issues in areas like:

  • Decreasing time to market
  • Identifying and mitigating risks in Clinical trials process
  • Providing analytics on data
  • Providing single source of truth by integrating multiple data sources
  • Adhering to industry norms
  • Providing configurability and scalability in the solution to integrate with additional source systems
  • Providing set of pre-configured reports and dashboards

 With a global transition in progress, almost all of the leading pharma companies are moving towards incorporating a CDA based solution to improve their process.

As we read, a combination of subject matter and technical experts with deep understanding of industry challenges and pain-points are helping transform this technology solution into a domain based solution to support the industry overcome long term and short term issues.

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About Amit Patil

mmProgram Manager, Life Sciences

Amit Patil works as a Program manager in Saama's life Science domain.He has 18 yrs of industry experience spanning various technologies and domain. He is currently responsible for managing and delivery of CDA projects for multiple accounts using Saama's FAE4LS.


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