The impact of big data in clinical research and pharmacovigilance

 Introduction

Big data is revolutionizing the healthcare industry and changing how we think about patient care. In this case, big data refers to the vast amounts of data generated by healthcare systems and patients, including electronic health records, claims data, and patient-generated data. With the ability to collect, manage, and analyze vast amounts of data, healthcare organizations can now identify patterns, trends, and insights that can inform decision-making and improve patient outcomes. Big data transform healthcare delivery from predicting disease outbreaks to informing personalized medicine.

 Also, big data carries some challenges that must be addressed. Such challenges and limitations include managing and analyzing vast amounts of data, and ethical considerations, such as patient privacy. 

Data Collection and Management

Data collection and management are crucial aspects of using big data in clinical research and pharmacovigilance. 

The type of data collected  include electronic health records (EHRs), claims data, and patient-generated data.

Reducing clinical trials dropout rates

The dropout rate for clinical trials has been a major issue for quite some time. One main factor is that the trial, itself, may not be effective for the patient. Finding the right patient for the right trial is half the battle.

Secondly, trust and communication with the patient are imperative in the context of a clinical trial. This starts by keeping the patient informed throughout the process, which can now be achieved through the use of technology. Historically patient details we captured and entered into a database without necessarily considering the criticality of patient engagement in the clinical trial process. However, the data revolution is signaling the end of those days.

Use of big data as recruitment tool 

The days of broad-spectrum antibiotic drugs and beta blocker cardiovascular therapies are long gone. Now at a very niche market, consisting of cohorts of patients who are going to receive benefits from very specific therapies.

As these targeted therapies continue to expand, big data is expected to increase in value, especially with regard to recruitment. The traditional recruiting approach was to reach out to a network of physicians, explaining that you have a clinical development program in a specific therapeutic area, and then asking whether they have patients who would benefit from this particular drug. A physician may start out highlighting five to 10 patients. However, when shown the inclusion/exclusion criteria, the Physician may only be able to enroll one or two patients.

Cost reduction 

Currently, a lot of money is wasted on not identifying the right patients at the start of a trial. Moreover, the time it takes to recruit a patient is a long, arduous process. Making big data available for analysis sooner can take years of clinical trial timelines by allowing us to select the right patients from the start, which, in turn, can lower overall trial costs and reduce the time it takes to get drugs developed.

Issue regarding quality of data

The magnitude of data that needs to be captured in healthcare is beyond comprehension. Imaging data, genomics, proteomics, medical records, the list is endless. With this information overload comes the issue of data quality. As more data are collected, costs increase exponentially. Two solutions that are quickly improving data quality include:

  • Cloud-based data, where information is stored on the internet through a cloud computing provider who manages and operates data storage as a service

  • Internet of Things (IoT), which consists of a system of interrelated, internet-connected objects that are able to collect and transfer data over a wireless network without human intervention


Reference 


Student Name: Pratik P. Katare

StudentID:CSRPL_STD_IND_HYD_ONL/CLS_095/052023

Qualification: M.Pharmacy

e-Mail ID: pratikkatare2017@gmail.com

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