Literature DB >> 32036574

Automation Opportunities in Pharmacovigilance: An Industry Survey.

Rajesh Ghosh1, Dieter Kempf2, Angela Pufko3, Luisa Fernanda Barrios Martinez4, Chris M Davis5, Sundeep Sethi6.   

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 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.
METHODS: The 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.
RESULTS: In 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.
CONCLUSIONS: Our 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.

Entities:  

Year:  2020        PMID: 32036574     DOI: 10.1007/s40290-019-00320-0

Source DB:  PubMed          Journal:  Pharmaceut Med        ISSN: 1178-2595


  9 in total

Review 1.  Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.

Authors:  Benjamin Kompa; Joe B Hakim; Anil Palepu; Kathryn Grace Kompa; Michael Smith; Paul A Bain; Stephen Woloszynek; Jeffery L Painter; Andrew Bate; Andrew L Beam
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.606

2.  "Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time?

Authors:  Robert Ball; Gerald Dal Pan
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

Review 3.  Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources.

Authors:  Likeng Liang; Jifa Hu; Gang Sun; Na Hong; Ge Wu; Yuejun He; Yong Li; Tianyong Hao; Li Liu; Mengchun Gong
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

4.  Black Swan Events and Intelligent Automation for Routine Safety Surveillance.

Authors:  Oeystein Kjoersvik; Andrew Bate
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

5.  Automated Drug Coding Using Artificial Intelligence: An Evaluation of WHODrug Koda on Adverse Event Reports.

Authors:  Eva-Lisa Meldau; Shachi Bista; Emma Rofors; Lucie M Gattepaille
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

6.  Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

Authors:  Muhammad Imran; Aasia Bhatti; David M King; Magnus Lerch; Jürgen Dietrich; Guy Doron; Katrin Manlik
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

7.  Smartphone-based mobile applications for adverse drug reactions reporting: global status and country experience.

Authors:  Ayako Fukushima; Noha Iessa; Madhava Ram Balakrishnan; Shanthi Narayan Pal
Journal:  BMC Med Inform Decis Mak       Date:  2022-05-02       Impact factor: 2.796

Review 8.  Public Health Impact of Using Biosimilars, Is Automated Follow up Relevant?

Authors:  Antoine Perpoil; Gael Grimandi; Stéphane Birklé; Jean-François Simonet; Anne Chiffoleau; François Bocquet
Journal:  Int J Environ Res Public Health       Date:  2020-12-29       Impact factor: 3.390

9.  Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices.

Authors:  Kristof Huysentruyt; Oeystein Kjoersvik; Pawel Dobracki; Elizabeth Savage; Ellen Mishalov; Mark Cherry; Eileen Leonard; Robert Taylor; Bhavin Patel; Danielle Abatemarco
Journal:  Drug Saf       Date:  2021-02-01       Impact factor: 5.606

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.