Are electronic prescribing systems increasing the risk of ‘look-alike sound-alike’ medication errors? The answer is a complex one, and it's a question that has been hotly debated in the healthcare industry. On the one hand, electronic prescribing (ePMA) systems have been touted as a way to reduce medication errors by 30%, according to the UK government. But on the other hand, there have been several high-profile cases of 'look-alike sound-alike' (LASA) medication errors in ePMA systems, raising concerns about the potential risks. So, what's the truth? In this article, I'll delve into the issue, exploring the potential risks of LASA errors in ePMA systems, the available data on these errors, and the strategies being employed to mitigate them. I'll also discuss the potential role of AI in reducing LASA errors and the challenges that lie ahead. But first, let's take a step back and consider the broader context. The push to go paperless in healthcare has been a major driver of the adoption of ePMA systems. While the goal of reducing medication errors is laudable, the transition to ePMA systems has not been without its challenges. In fact, some argue that LASA errors in ePMA systems may have simply replaced those that occurred in traditional paper-based systems. To explore this question, The Pharmaceutical Journal sent a Freedom of Information request to NHS England for data on patient safety incidents associated with LASA medicines between 2015 and 2025. However, obtaining specific data on LASA incidents proved difficult due to the transition from the original 'National reporting and learning system' (NRLS) to the 'Learn from patient safety events' (LFPSE) service, which created the potential issue of dual reporting during the crossover period. Despite this, NHS England was able to identify drug pairs that make up the most reported errors. So, what does this data tell us? While it may not show an increase in errors, it's possible that LASA errors in traditional systems have simply been replaced with new LASA errors in electronic systems. Bryony Dean Franklin, professor of medication safety at University College London, suggests that the errors may have balanced each other out. Julia Scott, a pharmacist and chief information officer at Dartford and Gravesham NHS Trust, echoes this view, arguing that the introduction of ePMA systems has simply shifted the location of LASA errors from the prescribing stage to the dispensing or administration stage. However, there are strategies being employed to mitigate LASA errors in ePMA systems. One tactic is 'tall-man lettering', in which certain letters in drug names are capitalised to distinguish them from others. While this approach has some evidence of effectiveness, it's not a complete solution. Scott suggests that changing how drugs are grouped and implementing minimum character sets could also help reduce LASA errors. The integration of clinical decision support AI is another potential solution. Scott hopes that AI could help prevent LASA errors by applying logic and providing prompts to healthcare professionals. However, she also warns of the potential risks of ambient voice technology (AVT) or 'AI scribes', which could introduce a new category of LASA errors. Scott questions how these errors could be mitigated and suggests that AI-enabled clinical decision support may be necessary to address them. Other methods, such as the 'Touchdose' system, which allows prescribing by indication, are also being explored as potential solutions to reduce LASA errors. However, under-reporting remains a significant challenge. Only about 1 in 100 prescribing errors and 1 in 1,000 administration errors are reported as incident reports, making it difficult to determine the true scale of LASA errors. Franklin hopes that AI will play a role in analysing LASA error reporting, but Scott suggests that capturing near-misses as well as incidents could also be a powerful tool for improving medication safety. In conclusion, while LASA errors are unlikely to be fully eliminated, there are strategies being employed to mitigate them. The potential role of AI in reducing LASA errors is an exciting development, but it's clear that there are still challenges to be addressed. As the healthcare industry continues to embrace ePMA systems, it's essential to strike a balance between innovation and safety, ensuring that patients receive the best possible care while minimising the risk of medication errors.
