Strategies for automating pharmacovigilance adverse event case processing

Review Article

  • Mythily Easwar School of Management, Walden University, Minneapolis, MN, USA
  • Kenneth Gossett Walden University, Minneapolis, MN, USA
  • Manish Shashi School of Management, Walden University, Minneapolis, MN, USA
Keywords: Adverse Events, Negative Feedback System, Qualitative Case Study, Pharmacovigilance, Task Technology Fit Model, Preventable Medical Errors,, System’s Theory

Abstract

Introduction: In 2020, the U.S. Food & Drug Administration received reports of 2.2 million adverse events, which is a 350% increase since 2010 with an associated increase in pharmaceutical companies' pharmacovigilance (PV) operational cost. Guided by the task technology fit model as the conceptual framework, the purpose of this qualitative single case study was to explore strategies used by PV systems leaders to implement innovative technology solutions. The objective of the study was to develop a system model to explore strategies to implement innovative technology to automate AE detection, processing, and reporting.

Methods: Data were collected using semistructured interviews and company data to address the research question on strategies to implement innovative technology solutions to automate adverse events case processing. The collected data was analyzed using 5-step data analysis, compiling, disassembling, reassembling, interpreting data, and concluding the findings.

Results: The implications for positive social change include the potential to identify strategies in making pharmaceutical medicines safer for human consumption.

Conclusions: PV leaders can redirect the savings from PV operations in terms of cost and workforce tasks towards investing in the actual PV tasks like benefit-risk assessments of products; thereby, improving patient outcomes and making the products more efficacious and safer for human use.

Author Biographies

Mythily Easwar, School of Management, Walden University, Minneapolis, MN, USA

Pharmaceutical Industry

Kenneth Gossett, Walden University, Minneapolis, MN, USA

Contributing Faculty Member

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Published
2021-12-01
How to Cite
1.
Easwar M, Gossett K, Shashi M. Strategies for automating pharmacovigilance adverse event case processing. jpadr [Internet]. 2021Dec.1 [cited 2022Jan.22];2(4):6-13. Available from: https://jpadr.com/index.php/jpadr/article/view/52