AI-powered Solutions for Casualty Assessment in Drug Safety and Patient Care

Title:AI-powered Solutions for Casualty Assessment in Drug Safety and Patient Care

Volume: 5

Author(s): Himanshu Sharma, Shushank Mahajan, Nitika Garg, Samrat Chauhan, Monika Saini, Thakur Gurjeet Singh, Sanchit Dhankhar*Pooja Mittal*

Affiliation:

• Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, Punjab, India

• Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, Punjab, India

Keywords: Adverse drug reaction, Post-marketing surveillance, International adverse drug reaction reporting, AI, Pharmacovigilance, Detection, Assessment.

Abstract:

Objective: An adverse drug reaction is defined as “an appreciably harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product, which warrants prevention or specific treatment, alteration of the dosage regimen, or withdrawal of the product, as it predicts hazard from future administration.”

Methodology: Currently used to report such responses, the International Classification of Diseases will soon incorporate WHO's Adverse Reaction Terminology. A medication's bad effects can fall into one of six types, each having its own mnemonic: withdrawal, therapeutic failure, dose-and time-related, non-dose-related, weird, increased withdrawal, and withdrawal overall (time-related). Factors such as timing, illness pattern, investigation findings, and retesting the medicine could be useful in pinpointing the reason for a suspected adverse drug reaction. Management includes treating the effects of the medication specifically as well as, if feasible, stopping it altogether.

Results: Reporting suspected adverse medication reactions is important. Monitoring techniques are able to identify responses and establish connections. There is many software that is used to report and monitor adverse drug reaction responses. Various Pharmacovigilance companies use AI technology to utilize this method to record signals, communicate, and solve new issues in order to limit or avoid harm because of large data size.

Cite this article as:

Sharma Himanshu, Mahajan Shushank, Garg Nitika, Chauhan Samrat, Saini Monika, Singh Thakur Gurjeet, Dhankhar Sanchit*, Mittal Pooja*, AI-powered Solutions for Casualty Assessment in Drug Safety and Patient Care, New Emirates Medical Journal 2024; 5 : e02506882313855 . https://dx.doi.org/10.2174/0102506882313855240809064836