Login Register Cart 0
Generic placeholder image

Current Drug Safety

Editor-in-Chief

ISSN (Print): 1574-8863
ISSN (Online): 2212-3911

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Enhancing the Efficiency of the Individual Case Safety Report (ICSR) Quality and Compliance through Automation

Author(s): Shannon Link*, Adam Kammler, Ritu Gupta, Mahendra Hembade, Retesh Kumar and Vinu George

Volume 19, Issue 2, 2024

Published on: 15 August, 2023

Page: [255 - 260] Pages: 6

DOI: 10.2174/1574886318666230801162002

Price: $65

Abstract

Background: Over the past few years, major inspection findings have been identified in the “management of adverse reactions” that may be due to increasing workload in pharmaceutical organizations impacting the correctness of information in individual case safety reports (ICSRs). Although retrospective quality check (Retro-QC) and late submission analyses are important steps in ensuring ICSR quality, their manual application poses several challenges that can be overcome through automation.

Objectives: To improve the efficiency of the Retro-QC analysis and late submission analysis using a computer-operated tool called Compliance and Metrics Management (CMM) tool, and to measure the tool’s effectiveness in terms of productivity, time, and cost savings by comparing against the manual process.

Methods: Time savings were calculated by measuring the difference in time taken during the manual process versus the automated process. Cost savings were measured in terms of hourly remuneration for the time saved. Productivity was calculated as the difference between the number of cases handled in the manual versus automated process. Thus, the overall efficiency was measured in terms of time and cost savings along with increased productivity.

Results: Automation resulted in time savings of 49% and cost savings of 43% for Retro-QC analysis, and the productivity level increased by 67%. For late submission analysis, the CMM tool resulted in time savings of 88% and cost savings of 87%.

Conclusion: CMM tool enhanced the efficiency of both Retro-QC and late submission analyses by increasing productivity along with time and cost savings. It also reduced the number of errors, thereby enhancing the accuracy of the process and overall compliance.

« Previous Next »
Graphical Abstract

[1]
Individual case safety reports and vigibase – the vital importance of quality 2022. Available from:https://www.who-umc.org/media/163807/vigibase-the-vital-importance-of-quality-2017.pdf[Accessed on: Dec 29, 2022]
[2]
Guideline on good pharmacovigilance practices (GVP), 2012: Module I – Pharmacovigilance systems and their quality systems. 2012. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-good-pharmacovigilance-practices-module-i-pharmacovigilance-systems-their-quality-systems_en.pdf[Accessed on: Dec 29, 2022]
[3]
Medicines and Healthcare products regulatory agency. Pharmacovigilance Inspection Metrics Report April 2018 – March 2019. 2018. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/879419/MHRA_GPvP_Inspection_metrics_2018-19.pdf [Accessed on: Mar 31, 2023]
[4]
Medicines and Healthcare products regulatory agency. Pharmacovigilance Inspection Metrics Report April 2019 – March 2020. 2019. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/965496/MHRA_GPvP_Inspection_metrics_2019-20.pdf [Accessed on: Mar 31, 2023]
[5]
Medicines and Healthcare products regulatory agency. Pharmacovigilance Inspection Metrics Report April 2020 – March 2021. 2020. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1041812/MHRA_GPvP_Inspection_metrics_2020-21.pdf [Accessed on: Mar 31, 2023]
[6]
Stergiopoulos S, Fehrle M, Caubel P, Tan L, Jebson L. Adverse drug reaction case safety practices in large biopharmaceutical organizations from 2007 to 2017: An industry survey. Pharmaceut Med 2019; 33(6): 499-510.
[http://dx.doi.org/10.1007/s40290-019-00307-x] [PMID: 31933240]
[7]
United States Food and Drug Administration Reports Received and Reports Entered into FAERS by Year Available from: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/reports-received-and-reports-entered-faers-year[Accessed on: Mar 31, 2023]
[8]
Ghosh R, Kempf D, Pufko A, Barrios Martinez LF, Davis CM, Sethi S. Automation opportunities in pharmacovigilance: An industry survey. Pharm Med 2020; 34(1): 7-18.
[http://dx.doi.org/10.1007/s40290-019-00320-0] [PMID: 32036574]
[9]
Yang M, Kiang M, Shang W. Filtering big data from social media - Building an early warning system for adverse drug reactions. J Biomed Inform 2015; 54: 230-40.
[http://dx.doi.org/10.1016/j.jbi.2015.01.011] [PMID: 25688695]
[10]
Freifeld CC, Brownstein JS, Menone CM, et al. Digital drug safety surveillance: Monitoring pharmaceutical products in twitter. Drug Saf 2014; 37(5): 343-50.
[http://dx.doi.org/10.1007/s40264-014-0155-x] [PMID: 24777653]
[11]
Institute For safe medication practices. Critique of a key drug safety reporting system Available from: https://www.ismp.org/quarterwatch/drug-safety-reporting-system [Accessed on: Dec 29, 2022]
[12]
Addressing the Data Challenges of Pharmacovigilance Available from: https://www.oracle.com/us/industries/health-sciences/address-data-challenges-pharma-wp-5018953.pdf [Accessed on: Dec 29, 2022]
[13]
Li Y, Muthiah M, Routh A, Dorai C. Cognitive computing in action to enhance invoice processing with customized language translation. IEEE International Conference on Cognitive Computing (ICCC) Honolulu, HI, USA 2017; pp 25-30 June 2017; 136-9.
[http://dx.doi.org/10.1109/IEEE.ICCC.2017.25]
[14]
Gartland A, Bate A, Painter JL, Casperson TA, Powell GE. Developing crowdsourced training data sets for pharmacovigilance intelligent automation. Drug Saf 2021; 44(3): 373-82.
[http://dx.doi.org/10.1007/s40264-020-01028-w] [PMID: 33354751]
[15]
Lewis DJ, McCallum JF. Utilizing advanced technologies to augment pharmacovigilance systems: Challenges and opportunities. Ther Innov Regul Sci 2020; 54(4): 888-99.
[http://dx.doi.org/10.1007/s43441-019-00023-3] [PMID: 32557311]
[16]
Arriello. Pharmacovigilance automation adoption survey – UK & US. Key findings and discussion. Available from: https://arriello.com/wp-content/uploads/2021/07/arriello-pharmacovigilance-automation-adoption-survey-digital.pdf[Accessed on: Mar 31, 2023]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy