-
Je něco špatně v tomto záznamu ?
Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices
K. Huysentruyt, O. Kjoersvik, P. Dobracki, E. Savage, E. Mishalov, M. Cherry, E. Leonard, R. Taylor, B. Patel, D. Abatemarco
Jazyk angličtina Země Nový Zéland
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
ProQuest Central
od 2008-06-01 do Před 1 rokem
Nursing & Allied Health Database (ProQuest)
od 2008-06-01 do Před 1 rokem
Health & Medicine (ProQuest)
od 2008-06-01 do Před 1 rokem
- MeSH
- automatizace MeSH
- farmakovigilance * MeSH
- lidé MeSH
- řízení rizik MeSH
- technologie MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Pharmacovigilance is the science of monitoring the effects of medicinal products to identify and evaluate potential adverse reactions and provide necessary and timely risk mitigation measures. Intelligent automation technologies have a strong potential to automate routine work and to balance resource use across safety risk management and other pharmacovigilance activities. While emerging technologies such as artificial intelligence (AI) show great promise for improving pharmacovigilance with their capability to learn based on data inputs, existing validation guidelines should be augmented to verify intelligent automation systems. While the underlying validation requirements largely remain the same, additional activities tailored to intelligent automation are needed to document evidence that the system is fit for purpose. We propose three categories of intelligent automation systems, ranging from rule-based systems to dynamic AI-based systems, and each category needs a unique validation approach. We expand on the existing good automated manufacturing practices, which outline a risk-based approach to artificially intelligent static systems. Our framework provides pharmacovigilance professionals with the knowledge to lead technology implementations within their organizations with considerations given to the building, implementation, validation, and maintenance of assistive technology systems. Successful pharmacovigilance professionals will play an increasingly active role in bridging the gap between business operations and technical advancements to ensure inspection readiness and compliance with global regulatory authorities.
Information Technology AstraZeneca Macclesfield UK
Patient Safety UCB Brussels Belgium
PV Information Management Astellas Chicago IL USA
R and D IT MSD Prague Czech Republic
RGITSC Software Validation Roche Polska Sp z o o Warsaw Poland
Safety Management Global Regulatory Affairs and Merck and Co Inc Kenilworth NJ USA
WorldWide Patient Safety Bristol Myers Squibb Company Princeton NJ USA
Citace poskytuje Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc22019713
- 003
- CZ-PrNML
- 005
- 20220804135929.0
- 007
- ta
- 008
- 220720s2021 nz f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1007/s40264-020-01030-2 $2 doi
- 035 __
- $a (PubMed)33523400
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a nz
- 100 1_
- $a Huysentruyt, Kristof $u Patient Safety, UCB, Brussels, Belgium. Kristof.Huysentruyt@ucb.com $1 https://orcid.org/0000000226419382
- 245 10
- $a Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices / $c K. Huysentruyt, O. Kjoersvik, P. Dobracki, E. Savage, E. Mishalov, M. Cherry, E. Leonard, R. Taylor, B. Patel, D. Abatemarco
- 520 9_
- $a Pharmacovigilance is the science of monitoring the effects of medicinal products to identify and evaluate potential adverse reactions and provide necessary and timely risk mitigation measures. Intelligent automation technologies have a strong potential to automate routine work and to balance resource use across safety risk management and other pharmacovigilance activities. While emerging technologies such as artificial intelligence (AI) show great promise for improving pharmacovigilance with their capability to learn based on data inputs, existing validation guidelines should be augmented to verify intelligent automation systems. While the underlying validation requirements largely remain the same, additional activities tailored to intelligent automation are needed to document evidence that the system is fit for purpose. We propose three categories of intelligent automation systems, ranging from rule-based systems to dynamic AI-based systems, and each category needs a unique validation approach. We expand on the existing good automated manufacturing practices, which outline a risk-based approach to artificially intelligent static systems. Our framework provides pharmacovigilance professionals with the knowledge to lead technology implementations within their organizations with considerations given to the building, implementation, validation, and maintenance of assistive technology systems. Successful pharmacovigilance professionals will play an increasingly active role in bridging the gap between business operations and technical advancements to ensure inspection readiness and compliance with global regulatory authorities.
- 650 12
- $a umělá inteligence $7 D001185
- 650 _2
- $a automatizace $7 D001331
- 650 _2
- $a lidé $7 D006801
- 650 12
- $a farmakovigilance $7 D060735
- 650 _2
- $a řízení rizik $7 D012308
- 650 _2
- $a technologie $7 D013672
- 655 _2
- $a časopisecké články $7 D016428
- 655 _2
- $a práce podpořená grantem $7 D013485
- 700 1_
- $a Kjoersvik, Oeystein $u R&D IT, MSD, Prague, Czech Republic
- 700 1_
- $a Dobracki, Pawel $u RGITSC Software Validation, Roche Polska Sp. z o.o., Warsaw, Poland
- 700 1_
- $a Savage, Elizabeth $u Global Medical Organization, Janssen Research & Development, LLC a division of Johnson & Johnson, Horsham, PA, USA
- 700 1_
- $a Mishalov, Ellen $u PV Information Management, Astellas, Chicago, IL, USA
- 700 1_
- $a Cherry, Mark $u Information Technology, AstraZeneca, Macclesfield, UK
- 700 1_
- $a Leonard, Eileen $u WorldWide Patient Safety, Bristol-Myers Squibb Company, Princeton, NJ, USA
- 700 1_
- $a Taylor, Robert $u Safety Management, Global Regulatory Affairs and, Merck & Co., Inc., Kenilworth, NJ, USA
- 700 1_
- $a Patel, Bhavin $u Worldwide Safety, Pfizer Inc, Peapack, NJ, USA
- 700 1_
- $a Abatemarco, Danielle $u WorldWide Patient Safety, Bristol-Myers Squibb Company, Princeton, NJ, USA
- 773 0_
- $w MED00001449 $t Drug safety $x 1179-1942 $g Roč. 44, č. 3 (2021), s. 261-272
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/33523400 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y p $z 0
- 990 __
- $a 20220720 $b ABA008
- 991 __
- $a 20220804135922 $b ABA008
- 999 __
- $a ok $b bmc $g 1823072 $s 1170956
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2021 $b 44 $c 3 $d 261-272 $e 20210201 $i 1179-1942 $m Drug safety $n Drug Saf $x MED00001449
- LZP __
- $a Pubmed-20220720
