2020
DOI: 10.1007/s40290-019-00320-0
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Automation Opportunities in Pharmacovigilance: An Industry Survey

Abstract: Background TransCelerate'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. Objectives TransCelerate's IAO in Pharmacovigilance initiative initially wo… Show more

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Cited by 35 publications
(38 citation statements)
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“…For instance, from development to pharmacovigilance, Machine Learning could be used for data mining or to automate current processes. Indeed, the literature mentions that machine learning is used to automate pharmacovigilance processes [ 31 ]. This automation could improve both the detection of safety signals and risk management [ 31 ].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…For instance, from development to pharmacovigilance, Machine Learning could be used for data mining or to automate current processes. Indeed, the literature mentions that machine learning is used to automate pharmacovigilance processes [ 31 ]. This automation could improve both the detection of safety signals and risk management [ 31 ].…”
Section: Discussionmentioning
confidence: 99%
“…Indeed, the literature mentions that machine learning is used to automate pharmacovigilance processes [ 31 ]. This automation could improve both the detection of safety signals and risk management [ 31 ]. In addition, machine learning can also be used to identify relationships between biological terms [ 32 ] from medical records or social media screening and extract adverse event data [ 33 ] as illustrated with the Word2Vec algorithm [ 33 ].The latter application is related to the identification of adverse events using syntactical relationships between words [ 33 ].…”
Section: Discussionmentioning
confidence: 99%
“…More recently, novel areas of research based on artificial intelligence (AI) technologies have evolved, including machine learning (ML) and natural language processing (NLP) techniques, and are now being adopted to support pharmacovigilance processes [8][9][10]. Although these types of technology show great promise with their capability to learn based on data inputs, existing validation frameworks may need to be augmented to verify intelligent automation systems.…”
Section: Classification Of Intelligent Automation Systems In Pharmacovigilancementioning
confidence: 99%
“…The primary focus of this paper is validation rather than other adoption considerations and potential barriers to implementation. This paper considers the use of intelligent automation solutions for pharmacovigilance, specifically for high-effort activities such as ICSR case processing [ 10 ]. The principles proposed may be applied to different areas that are subject to regulatory oversight.…”
Section: Introductionmentioning
confidence: 99%
“…Since the early days of pharmacovigilance, the international reach of spontaneous reporting has increased [47], along with the number of organisations expecting copies of such reports. The original recipient of an ICSR, in addition to managing and acting on the report, has also legislative obligations to share the ICSR with multiple partners (e.g., other companies or regulators) [48]. In this context, for example, unidentified duplicate copies of ICSRs can readily propagate and hinder effectiveness of the system [49] (Box 2).…”
Section: Data Fusion and Linkage To Other Datamentioning
confidence: 99%