Does big data analytics work for pharmacovigilance?

The use of big data analysis facilitates safety evaluations, comparative effectiveness studies, and investigational trials.

Big data analytics in PV has numerous uses:

• Pre-marketing drug safety surveillance: Big data analytics techniques have also proven useful in research. Data mining automates standardization, statistical scoring, and signal prioritization, reducing labor costs and improving biology understanding.
  Big data analytics is used by the pharmaceutical industry for post-marketing safety surveillance to identify drug safety signals earlier, assess risk, and interpret clinical trial findings.

Drug safety signals are identified earlier, risks are evaluated, and clinical trial findings are interpreted using big data analytics by the pharmaceutical industry. This is for post-marketing safety surveillance. The pharmaceutical industry benefits from big data analytics in terms of time and cost savings. In addition, it benefits from logistical assistance in detecting signals in large data sets.

• Regulatory decision-making: Regulatory agencies rely on big data analytics for the detection of signals in ADR cases as well as vaccine monitoring. Regulatory agencies decide how to implement signals, such as label changes and benefit-risk monitoring of medications, once they identify them using big data analysis.

Clinical application: In clinical application, this analysis offers data on the illness, prior consultations, diagnostics, test outcomes, and therapies. Everything is centralized and easily searchable, including blood type, allergies, diseases, potential medications, and vital sign measurements. Big data analytics is being used in everyday practice to quickly provide accurate information in an emergency.

• Clinical trials: The following examples show how advanced analytics can add value to clinical trials.
• Increased data quality by automating the analysis of various types of data
• Effective data mining that increases the transparency of data exploration by detecting deviations and differences.

NEW APPLICATIONS OF BIG DATA ANALYTICS IN PV

• COVID-19 medications and vaccines
  In the COVID-19 pandemic, PV analysis using big data is essential for reducing false information and giving patients the right information and explanations about drug use. This analysis produces useful data about a disease, such as hidden patterns, unknown trends, correlations, and patient preferences.
  The following are some significant PV analytics applications in COVID-19:
  Benefit-risk analysis; improved drug and vaccine safety; quicker compliance; better patient protection
  Using big-scale data analysis using data mining tools helps identify novel and unidentified AEFI and AESI in subpopulations that are understudied or left out of trials. This allows for continuous monitoring of safety concerns regarding these vaccines after introduction.

“BIG DATA ANALYTICS IN PV: DATA SECURITY”
Data privacy is crucial whenever information is posted online and made readily accessible for research. Users maintain the confidentiality of patient identity-related information. Using social media to assess advancing medical knowledge still raises ethical questions. The most crucial one is the guarantee that patient identity remains private. Technology has not advanced the current set of rules and regulations protecting privacy rights. Procedures were created to ensure that only data that had been through a data minimization step should be accessible to registered end users for analysis. However, the raw data were kept accessible in very specific circumstances to allow contacting the patient if necessary.

A PV Perspective on big data analytics

PV-related fields have some limitations despite big data’s impact.

Big data analytics is also difficult due to issues such as incorrect, missing, or duplicate data, bias, confounding variables, and validation procedures. With low-quality data algorithms, decision-makers may have difficulty making assumptions about specific signal detection.
When patients seek care at different institutions or practices, their care may be recorded in multiple electronic health records. There may not be a significant enhancement to administrative data by structured data due to unstructured data.

An Outlook on big data analytics in PV

Big data can find connections between patient data in various datasets. The development of standards for signal detection, the use of an integrative approach to signal detection, the improvement of data mining software and tools, the application of data mining to other product safety and regulatory issues, and other future directions will help to overcome the current limitations of big data analytics in PV.

Summary
For drug safety, large data analytics has succeeded in locating new ADEs and ADRs connected to drugs. Big data can contribute more to PV efforts, despite the many obstacles to be overcome. The use of extensive data in PV will probably continue to advance as updated methods, tools, and data sources for drug safety surveillance are developed. Last but not least, the true test of big data’s worth will be how well it helps identify drug safety problems.