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Clinical Data Management: Challenges Faced by Our Clients – Part Ⅰ

Jan 19, 2021

Breakthroughs in medical research and drug development are backed with accurate, granular insights obtained from clinical trial data. This signifies the importance of clinical data management in the field of medical research. The lack of data-driven insights can hinder an organization’s capability to provide the drug efficacy and usage validations needed to bring therapeutics to market and to the patients who need them the most.

The data management challenge in clinical research will only become more complex as the volume and variety, and veracity of data and data sources continue to increase, said an analytics expert at Quantzig.

Clinical Data Management

In the field of medical research, data plays a crucial role in decision-making. In such a scenario, where data is of utmost importance, irrelevant data can result in catastrophic events. The main requirement in this field of study is optimized, consumption-ready clinical data that can be leveraged for analysis, reporting, and drug development. Unfortunately, in today’s fast-paced life sciences and pharma landscape, a huge challenge exists with data management in clinical research, i.e., challenges in maintaining quality, accuracy, and data availability. As such, data management teams are forced to spend more time on data cleansing rather than analysis. The time spent in aggregating and standardizing clinical trial data has proven costly for life sciences companies, resulting in catastrophic setbacks that result in clinical trials reruns and delayed product launches.

Today patient safety is a top priority for all life sciences companies, which means you need perfect clinical trial data management to support your drug development needs. If you find it challenging to manage and analyze data effectively, it may be time to leverage analytics and adopt advanced solutions built for this purpose. Our experts are happy to help you tackle this. Speak to an analytics expert to know more.

In this two-part expert series article, our analytics experts shed light on data management in clinical research. It includes the reasons behind the challenges, opportunities in clinical data management, and we also explain how analytics can help you tackle these challenges.

What is Clinical Data Management?

Clinical data management is a crucial process in medical research. It is a process that results in the generation of structured, granular, high-quality, and statistically sound data from clinical trials. As such, data management in clinical research plays a crucial role in reducing drug development time, thereby accelerating drug launch and time-to-market.

Our advanced analytics solutions are designed by our in-house teams to support life sciences and pharma companies make crucial data-driven decisions with minimal support. Request a free proposal to learn more about our solution offerings.

Challenges Facing Life Sciences and Biotech Firms

Clinical data management is complex and often a time-consuming task. Since most businesses capture and store data in silos, these data sets are generally unstructured, making clinical data reviews extremely difficult and corrections to data in source systems cumbersome and time-consuming.

How to overcome clinical data management challenges?

The ability to aggregate and analyze data in silos using a standard data structure can help you answer all these questions and use data to obtain granular insights that aid medical research and drug development. Additionally, streamlining and standardizing all clinical trial data management phases, including the ability to interpret and review clinical data and examine comments in a central database, can save you time, money, and resources, all of which are crucial drivers of data management.

To gain exclusive insights on how clinical analytics can help pharma and life sciences companies, read part II of this article series.

While working with life sciences and clinical research organization, we’ve witnessed two types of scenarios that lead to typical data management challenges-

1. Clinical data analysis is done using manual methods, i.e., via spreadsheets

2. Custom-built applications used to generate data in standardized formats

We now offer free, customized pilot projects to help companies address the most pressing challenges using smart, innovative methods.

Our collaborations have helped us learn that most organizations lack advanced analytics-driven systems that can simplify this otherwise cumbersome process. While one cannot disregard the role of custom-built applications and manual processes in this sector, what businesses need to understand at this point is the benefit of using newer, advanced analytics-based systems that have been developed specifically to address the challenges associated with clinical data management.

Quantzig’s advanced data management solutions and analytics platforms offer several features such as workflow reviews, data dashboarding, and data visualization tools that highlight outliers, improve discrepancy management, and drive process transparency. Request more information from us if you’re looking for industry-specific insights.

Ready to Harness Game-Changing Insights?

Request a free solution pilot to know how we can help you derive intelligent, actionable insights from complex, unstructured data with minimum effort to drive competitive readiness, market excellence, and success.

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