Drug Safety Matters

Disproportionality analyses are a mainstay of pharmacovigilance research, but without clear guidelines, they often lead to confusion and misinterpretation. Enter the READUS-PV statement: the first-ever guide for reporting disproportionality analyses that are replicable, reliable, and reproducible.  Tune in to find out: The history of reporting guidelines in pharmacovigilance and why the READUS-PV guidelines were created Why there has been a spike in the publication of disproportionality analyses in recent years and what this means for their reliability What it means to publish “good” pharmacovigilance science  Want to know more? Read the READUS-PV guidelines, why they were created, and why they are important. In 2021, Khouri and colleagues showed that current methods and models used for disproportionality analyses are unreliable, and Mouffak and colleagues found that there is a tendency to overstate results in published disproportionality analyses. A book on data mining techniques in Pharmacovigilance by Poluzzi and colleagues delves deeper into this exponential increase in disproportionality analyses.  This paper elaborates on the Delphi technique, and how it is used to gather data from reviewers to achieve scientific consensus on a problem. Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.

Duplicate reports are a big problem when it comes to signal detection, but with the help of machine learning and new ways of comparing reports, we may more effectively detect them. This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC’s pharmacovigilance news site, brought to you in audio format. Find the original article here.After the read, we speak to author Jim Barrett, Senior Data Scientist at UMC, to learn more about the duplicate detection algorithm and UMC’s work to develop AI resources for pharmacovigilance.Tune in to find out:How the new algorithm handles duplicates in VigiBaseAbout different approaches for developing algorithmsWhy it can be challenging to evaluate the performance of an algorithmWant to know more?Listen to the Drug Safety Matters interview with Michael Glaser about his Uppsala Reports article “Ensuring trust in AI/ML when used in pharmacovigilance” and check out the episode’s extensive list of links for more on AI in pharmacovigilance. Artificial intelligence in pharmacovigilance – value proposition and the need for critical appraisal, a presentation by Niklas Norén, Head of Research at UMC, given at University of Verona in April 2024.  Finally, don’t forget to ­subscribe to the monthly Uppsala Reports newsletter for free regular updates from the world of pharmacovigilance.Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.

Ensuring trust in AI is vital to fully reap the benefits of the technology in pharmacovigilance. Yet, how do we do so while grappling with its ever-growing complexity?This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC’s pharmacovigilance news site, brought to you in audio format. Find the original article here.After the read, we speak to one of the authors of the article, Michael Glaser, to learn more about how AI and ML has been used in pharmacovigilance so far, and what needs to happen to ensure its continued use in the field.Tune in to find out:●      How AI and ML are being used today in pharmacovigilance processes●     Why a mindset change is necessary to make full use of AI/ML in pharmacovigilance●      How we may best move forward to implement AI/ML into healthcare. Want to know more? To know more about how AI and ML are being used in pharmacovigilance currently, read this scoping review.To know more about future trends of the use of AI in Biopharma, read this Accenture survey.Despite there being major interest in ML and AI to do more than task automation, there are a number of barriers to its implementation in healthcare. Check out this future-focused paper on the use of AI/ML in pharmacovigilance that details how to utilise it to its fullest potential.A mindset shift is necessary in terms of how we think about data, in terms of sharing, how to generate data required to effectively train AI/ML models.A validation framework must be developed for AI-based pharmacovigilance systems. One suggestion is to do so using a risk-based approach.While there is much interest in using recently developed AI technologies such as chatGPT, preliminary studies like this one suggest that the technology has a ways to go to be useful in pharmacovigilance.The World Health Organization have published an extensive guideline on the ethics and governance of AI for health.Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.

Look-alike medicines, unclear communication and distractions during administration – medication errors may occur for many different reasons. They all have in common that they are unintended mistakes in the drug treatment process that may or may not lead to patient harm. In this episode Ghita Benabdallah and Loubna Alj from the national pharmacovigilance centre of Morocco, and Alem Zekarias from Uppsala Monitoring Centre discuss how we can prevent medication errors from occurring – and, when they do occur, make sure that they are reported as such. Tune in to find out:What are the most common causes for medication errors?How should strategies for preventing medication errors be devised? How does the assessment of suspected medication error reports differ from “regular” ADR signal assessment?What can be done to encourage healthcare professionals to report medication errors?Want to know more?In March 2024, WHO published this systematic review of the global burden of preventable medication-related harm in healthcare.According to this 2021 article in BMJ, an estimated 237 million medication errors occur in England every year. Avoidable adverse drug events were calculated to cost the National Health Service an annual sum of GBP 98 462 582 per year, consuming 181 626 bed-days, and causing/contributing to 1708 deaths. This 2012 meta-analysis confirmed what had been suggested in several observational studies: that preventable adverse drug reactions are a significant healthcare burden.The European Medicines Agency (EMA) has a dedicated webpage with recommendations, guidelines, legal requirements and a good practice guide on medication errors. Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.