ArticlePharmaceutical medicine2020
Automation Opportunities in Pharmacovigilance: An Industry Survey.
Article in Pharmaceutical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- AI for Causality Assessment in Pharmacovigilance: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- RxMap: an LLM-assisted tool for medication normalization.JAMIA open · 2026Article
- Does generative AI mean the "end of history" for pharmacovigilance automation? towards a framework for the future of human-AI systems.Frontiers in drug safety and regulation · 2026Article
- In-line multi-wavelength non-destructive pharma quality monitoring with ultrabroadband carbon nanotubes photo-thermoelectric imaging scanners.Light, science & applications · 2025Article
- Might We Come Together on a Paradigm Shift to Manage ICSRs with a Decentralized Data Model?Drug safety · 2025Article
- Assessment of the Efficiency of a ChatGPT-Based Tool, MyGenAssist, in an Industry Pharmacovigilance Department for Case Documentation: Cross-Over Study.Journal of medical Internet research · 2025Article
- How LLMs can advance safety case intake-points to consider and insights from a proof of concept.Therapeutic advances in drug safety · 2025Review
- The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?Advanced healthcare materials · 2024Article
- Article
- Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST.JMIR medical informatics · 2024Article
- The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.Drug design, development and therapy · 2023Article
- An industry perspective on the use of machine learning in drug and vaccine safety.Frontiers in drug safety and regulation · 2023Article
- Smartphone-based mobile applications for adverse drug reactions reporting: global status and country experience.BMC medical informatics and decision making · 2022Article
- Article
- Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance.Drug safety · 2022Article
- "Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time?Drug safety · 2022Article
- Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.Drug safety · 2022Article
- Supervised Machine Learning-Based Decision Support for Signal Validation Classification.Drug safety · 2022Article
- Developing Crowdsourced Training Data Sets for Pharmacovigilance Intelligent Automation.Drug safety · 2021Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTransCelerate's Intelligent Automation Opportunities (IAO) in Pharmacovigilance initiative has been working to evaluate various pharmacovigilance processes to facilitate systematic innovation with intelligent automation across the entire area. The individual case safety report (ICSR) process was the first process selected for evaluation because of its resource-intensive nature, risk of errors, and operational inefficiencies.
objectivesTransCelerate's IAO in Pharmacovigilance initiative initially worked to articulate an end-to-end ICSR process that would generically apply to various pharmacovigilance organizations, despite organizational variations in specific ICSR process steps. This paper aims to address the need for a systematic review framework for automation of the ICSR process from the value, impact, perceived risk, and opportunity point of view.
methodsThe generic ICSR process, which starts with receipt of an adverse event report, was grouped into three process blocks: case intake, case processing, and case reporting. Each of these was then further detailed in individual process steps. A total of 19 TransCelerate member companies were invited to complete a survey designed to facilitate understanding of automation opportunities across the ICSR process. Heat maps of the current level of effort, expected benefit of automation, and perceived risk of automation were compiled from responses to identify intelligent automation opportunities for specific ICSR process steps. Relevant experts on the TransCelerate evaluation team analyzed and interpreted the anonymized and aggregated results.
resultsIn total, 15 TransCelerate member companies responded to the survey and indicated that ICSR process steps with current high effort, expected high automation benefit, low or manageable automation risk, and low levels of current automation present the best opportunities for future automation. Such steps include language translations, case verification, in-line quality control, prioritization/triage, data entry, alerts for cases of interest, workflow management, and monitoring. Some steps (e.g., submission) have been automated for a number of years and appear on the heat map as having low potential for further automation. The survey responses implied that, despite successful use of intelligent automation technologies in other areas, adoption within pharmacovigilance and the ICSR process in particular remains limited. The perceived high risk to patient safety is expected to decrease with additional successful applications in pharmacovigilance.
conclusionsOur results highlight the areas of greatest opportunity for intelligent automation based on the potential benefits of applying intelligent automation and the perceived risks associated with each ICSR process step. Responding TransCelerate member companies already automate many steps to varying degrees. However, a significant opportunity remains for automation to penetrate further. Additionally, the pharmacovigilance industry culture needs to change in order to reduce the perceived risk of automation and to encourage a more progressive approach to intelligent automation. Increased automation is crucial to empower agile and efficient pharmacovigilance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.