PNS271 Automation in Routine Use for Data Collection and Processing for Scalable Faster RWE Generation

Kassekert, Raymond; Easwar, M.; Glaser, M.; Ventham, R.; Bate, Andrew

doi:10.1016/j.jval.2020.08.1715

Cited by 4 publications

(2 citation statements)

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“…Even before the pandemic, automation and its potential benefits for PV activities were well recognized (Kassekert et al, 2022). Rules based automation, also known as robotic process automation (RPA), are well established and have been routinely used for several years by companies to assist in the processing of individual case safety reports [ICSRs; see (Kassekert et al, 2020) for an example]. Here, we have adapted the RPA benefit diagram first described by (Vitharanage et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An industry perspective on the use of machine learning in drug and vaccine safety

Painter

Kassekert²,

Bate³

2023

Front. Drug Saf. Regul.

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In recent years there has been growing interest in the use of machine learning across the pharmacovigilance lifecycle to enhance safety monitoring of drugs and vaccines. Here we describe the scope of industry-based research into the use of machine learning for safety purposes. We conducted an examination of the findings from a previously published systematic review; 393 papers sourced from a literature search from 2000–2021 were analyzed and attributed to either industry, academia, or regulatory authorities. Overall, 33 papers verified to be industry contributions were then assigned to one of six categories representing the most frequent PV functions (data ingestion, disease-specific studies, literature review, real world data, signal detection, and social media). RWD and social media comprised 63% (21/33) of the papers, signal detection and data ingestion comprised 18% (6/33) of the papers, while disease-specific studies and literature reviews represented 12% (4/33) and 6% (2/33) of the papers, respectively. Herein we describe the trends and opportunities observed in industry application of machine learning in pharmacovigilance, along with discussing the potential barriers. We conclude that although progress to date has been uneven, industry is very interested in applying machine learning to the pharmacovigilance lifecycle, which it is hoped may ultimately enhance patient safety.

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Section: Introductionmentioning

confidence: 99%

An industry perspective on the use of machine learning in drug and vaccine safety

Painter

Kassekert²,

Bate³

2023

Front. Drug Saf. Regul.

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“…Translating scientific research on automation of manual processing into actual improved capability for AE report management has taken some time [43]. More routine automation in the management of ICSRs has been seen in the past few years, with a slowly increasing number of publications on robotic process automation, and their impact on routine use [57,58].…”

Section: Agency Mah Mahmentioning

confidence: 99%

Safety of medicines and vaccines – building next generation capability

Bate

Stegmann

2021

Trends in Pharmacological Sciences

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Black Swan Events and Intelligent Automation for Routine Safety Surveillance

Kjoersvik¹,

Bate

2022

Drug Saf

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Effective identification of previously implausible safety signals is a core component of successful pharmacovigilance. Timely, reliable, and efficient data ingestion and related processing are critical to this. The term ‘black swan events’ was coined by Taleb to describe events with three attributes: unpredictability, severe and widespread consequences, and retrospective bias. These rare events are not well understood at their emergence but are often rationalized in retrospect as predictable. Pharmacovigilance strives to rapidly respond to potential black swan events associated with medicine or vaccine use. Machine learning (ML) is increasingly being explored in data ingestion tasks. In contrast to rule-based automation approaches, ML can use historical data (i.e., ‘training data’) to effectively predict emerging data patterns and support effective data intake, processing, and organisation. At first sight, this reliance on previous data might be considered a limitation when building ML models for effective data ingestion in systems that look to focus on the identification of potential black swan events. We argue that, first, some apparent black swan events—although unexpected medically—will exhibit data attributes similar to those of other safety data and not prove algorithmically unpredictable, and, second, standard and emerging ML approaches can still be robust to such data outliers with proper awareness and consideration in ML system design and with the incorporation of specific mitigatory and support strategies. We argue that effective approaches to managing data on potential black swan events are essential for trust and outline several strategies to address data on potential black swan events during data ingestion.

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PNS271 Automation in Routine Use for Data Collection and Processing for Scalable Faster RWE Generation

Cited by 4 publications

References 0 publications

An industry perspective on the use of machine learning in drug and vaccine safety

An industry perspective on the use of machine learning in drug and vaccine safety

Safety of medicines and vaccines – building next generation capability

Black Swan Events and Intelligent Automation for Routine Safety Surveillance

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