Big Data Analytics in Clinical Research

INTRODUCTION

Big data analytics is the process of analyzing large and complex data sets to extract valuable insights, such as identifying hidden patterns, uncovering previously unknown correlations, and understanding market trends and customer preferences. By leveraging powerful computational tools and techniques, #bigdataanalytics offers many advantages, including improved decision-making, enhanced operational efficiency, and the ability to detect and prevent fraudulent activities. These insights can help organizations better understand their customers, optimize their business processes, and gain a competitive advantage in the marketplace.

OBJECTIVES

In clinical trials big data analytics is an essential step to avoid risks. Big data analytics helps #CRO’s to properly mitigate risk through analyzing the progress and constantly monitoring of their clinical research and collecting data to provide valuable insights into their projects.

METHODOLOGY

Every kind of data has a rare quality of describing things after assigning a specific value to it, for analysis we need to organize these values processed and presented in a given context to make it useful. Data can be different forms:

1.????Qualitative data

2.????Quantitative data and

3.????Categorical data

1.???Qualitative data:

When the data present in the form of words and having description, then we call it as Qualitative data. It is very difficult to observe data, because it is subjective and also very hard to analyze the data in #clinicalresearch especially for comparing the data.

Ex: Quality data represents everything describing results, process, or an opinion that is considered quality data.

2.???Quantitative data:

Any data which can be expressed in the form of numbers or numerical figures, we call it as Quantitative data. These data can be distinguished into categories, grouped, measured, calculated and ranked.

Ex: Such as age, rank, cost, weight, scores, etc.., we can pr4sent such data in graphical formats, charts or apply statically analysis method to this data.

3.???Categorical Data:

The data which is present in the form of groups is called as categorical data. Any item included in the categorical data cannot belong to more than 1 group.

Ex: A person responding to a survey by telling his living style, marital status, smoking habit, drinking habit comes under the categorical data. Usually, a Chi-Square test is used to analyze these kinds of data.

Potential advantages of big data analytics in clinical research:

1. Improved Patient Outcomes: By analyzing large amounts of patient data, big data analytics can help identify new treatments, interventions, and #diagnostictools that can improve patient outcomes.
2. Increased Efficiency: #Bigdataanalytics can help streamline clinical trials by identifying patient populations and identifying clinical sites that are best suited for a particular study. This can reduce the time and cost associated with conducting clinical research.
3. Better Resource Allocation: With big data analytics, researchers can identify where resources are best allocated, such as identifying which areas of research are most promising and where clinical trials are needed most.
4. Enhanced Safety Monitoring: Big data analytics can be used to monitor drug safety and identify adverse events in real-time, allowing for prompt intervention to prevent harm to patients.
5. Improved Precision Medicine: Big data analytics can help identify genetic and other biomarkers that are associated with specific diseases, allowing for more personalized and targeted treatment.
6. Real-time Data Analytics: With big data analytics, researchers can analyze patient data in real-time, allowing for faster decision-making and more effective treatment planning.

Overall, the use of big data analytics in clinical research has the potential to improve patient outcomes, increase efficiency and enhance safety monitoring, among other benefits.

CONCLUSIONS:

Data collection tools are a key to analyzing the data that has been collected over a period of trial phase or a test method. Using a mixture of qualitative and quantitative metrics will give a broad range of data to be considered. In big data analytics there are 6 elements (6 V’s) i.e., volume, velocity, validity, variety, volatility and veracity.

?REFERENCES:

1. https://www.simplilearn.com/what-is-big-data-analytics-article#what_is_big_data_analytics.
2. https://www.questionpro.com/blog/data-analysis-in-research/.
3. https://www.google.com/searchq=methods+of+big+date+of+analysis+in+clinical+research&oq=&aqs=chrome.3.69i59i450l8.3248033j0j15&sourceid=chrome&ie=UTF-8

Written By: Sravani Reddy BOYAPALLE #clinoxy

Under the Guidance: Satish Kumar Vemavarapu Megha Arora clinoxy solutions ?