Did you know, around nine out of every ten drug applications fail to get approval,
which has huge implications on the overall cost of drug development? The failure rate
for new drugs targeting Alzheimer’s disease is 99.6% however, over 100 compounds
were identified and tested as potential therapies.
The global clinical trials market size is expected to reach USD 68.9 billion by 2026,
according to a new report by Grand View Research, Inc. While on one hand there are
investments being made in research and development of new drugs to improve health
outcomes, on the other hand, there is a vast amount of health data being generated
everyday through mobile applications, health apps, social media, electronic health
records…etc. The data generated in 2019 will be higher than ever. Can this data be
used for improving healthcare, and for getting better medicinal products to market?
Handling huge volumes of data is challenging with the current methods. Data
analytics can help the industry with innovative methods to deal with the existing
challenges and also forecast the new ones. It can help with more efficient study design,
better patient recruitment and site selection strategy and focused monitoring to speed
up the results submission.
Data Analytics – the future of industries?
Data Analytics is about deriving useful and rational information from the available
data. Let us understand what is data analytics. Have you ever tried Lenskart for
buying spectacles or contact lenses? They store your number and mail ID. If you have
bought 6 pairs of lenses then you would get a reminder that it is about to expire and
you should buy it again. Another one would be Flipkart or Amazon. These websites or
apps remember what you bought few months ago or what you can buy together with
your current items. Like if you are buying lunch box, water bottle would be the
Data Analytics is about gathering the data, processing it in useful manner.
Data Analytics has always been part of the industries which generate the data.
However it is becoming increasingly popular in recent times. Data Analytics will be
the most valuable resources in future for medical and healthcare industry. Data
Analytics will change the landscape of healthcare industry where it may reduce the
risk and provide cost effective solutions. Data analyst job is one of the top 6 jobs as
per Forbes in 2018 which shows how companies are relying on data analytics to make
sense of the data and effectively use it to respond to the current and future business
Clinical Data Management (CDM) and Data Analytics
So what is the relationship between data analytics, that seem to be transforming all
industries, and Clinical Data Management?
Data Analysis and Clinical Data Management are different branches of the same tree
and if tied together, both strengthen each other. CDM is a specialized branch which
plays a vital role in data collection and cleaning during the conduct of the clinical
trials and data analytics is a very efficient tool which can expedite the data cleaning.
Clinical Data Manager ensures that the collected clinical data is complete, consistent
and is in compliance with regulatory and trial protocol requirement (3Cs). Data
analytics helps a CDM to achieve these 3 Cs.
How data analytics can be applied in CDM process?
An important step in CDM is data cleaning. Data Analysis can be a very strong and
beneficial tool for CDM during the data cleaning. Data cleaning phase occurs during
study conduct the data flow is maximum and needs continuous monitoring.
How can data analytics help a CDM with data cleaning?
Based on the data that is coming in, data analytics can give a visual analysis of the
what’s happening in trial. How sites are performing with data entry and answering the
queries? How monitors are doing with Source Data Verification and their queries?
Through data visualisation tools , tasks can be tracked real-time. for example, tasks
which are out of track are shown in RED, tasks which are just doing OK are shown in
amber or some lighter colour and task on marks are in GREEN. This visuals help
CDMs to concentrate on specific sites or specific trial subject or may be a specific
form. These visuals give data entry status, query ageing status and query status (open,
answered or closed).
Data analytics can help a CDM decide if any specific CRF needs to be paid extra
attention or any sites needs extra monitoring visits. It can also help in determining the
need of extra training with the monitors who can in tern help the sites. The visuals
and graphs can catch immediate attention and course of action can be decided.
The aim of any CDM is to achieve database lock on or before the deadline. A
descriptive and predictive data analytics can help a Clinical Data Manager achieve the
goal of database lock without hassle.
Data Analytics will have a significant influence on CDM activities in coming days and
will change the prospective and technique used by CDM.
Clinlearn Clinical Solutions
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