Are electronic prescribing systems increasing the risk of ‘look-alike sound-alike’ medication errors? It's a question that has been asked with increasing urgency in the wake of several high-profile incidents, including the tragic death of three-week-old Sidra Aliabase after she was prescribed sodium acid phosphate instead of sodium chloride. This incident, and others like it, have highlighted the potential risks associated with electronic prescribing and medicines administration (ePMA) systems, particularly in relation to 'look-alike sound-alike' (LASA) medicines. But are these risks being adequately addressed? And what can be done to mitigate them? Personally, I think that the potential for errors in ePMA systems is a serious concern, especially given the increasing number of NHS trusts in England that are adopting these systems. What makes this particularly fascinating is the fact that while the use of electronic prescribing can reduce medication errors by 30%, according to the UK government, there is also a risk that LASA errors may be increasing. From my perspective, this raises a deeper question: how can we balance the benefits of ePMA systems with the potential risks, particularly in relation to LASA medicines? One thing that immediately stands out is the complexity of the issue. On the one hand, ePMA systems can reduce medication errors, but on the other hand, they may also introduce new types of errors, such as those related to drop-down menus and sound-alike medicines. What many people don't realize is that the transition from paper-based prescribing to ePMA systems may have created a new set of challenges, rather than simply replacing old ones. In my opinion, this highlights the need for a more nuanced approach to the design and implementation of ePMA systems, particularly in relation to LASA medicines. To explore this issue further, 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, it was difficult to obtain data specifically on LASA incidents, due to the transition from the original 'National reporting and learning system' (NRLS) to the 'Learn from patient safety events' (LFPSE) service, and the potential for dual reporting during the crossover period. A detail that I find especially interesting is the fact that LASA incidents are not reported separately, but rather are included in categories such as 'wrong/transposed/omitted medicine label' or 'poor packaging or design of medicines that might lead to errors'. This makes it difficult to extract data on LASA incidents specifically. However, from the available data, NHS England was able to identify drug pairs that make up the most reported errors. One of the key findings is that while the available data may not show an increase in errors, it is possible that LASA errors in traditional systems have just been replaced with new LASA errors in electronic systems. This raises a deeper question: have ePMA systems increased the risk of LASA errors, or have they simply shifted the types of errors that occur? In my opinion, this highlights the need for a more comprehensive approach to the design and implementation of ePMA systems, particularly in relation to LASA medicines. To mitigate the risk of LASA errors, several strategies have been proposed. One of the tactics used to try and prevent LASA errors in paper-based prescribing is 'tall-man lettering', in which certain letters in drug names are capitalised to distinguish it from others. However, this approach may not be sufficient in ePMA systems, where drop-down menus and sound-alike medicines are more common. Another strategy is to change how drugs are grouped, for example, by forcing things out of alphabetical order if necessary, to take penicillamine and penicillin away from each other in a list. This approach could potentially reduce the risk of LASA errors, but it would need to be balanced against usability. Personally, I think that the integration of clinical decision support AI could be a promising approach to mitigating LASA errors. If ePMA systems are integrated with electronic patient records, natural language processing could be used to apply logic and identify potential errors, such as the use of penicillamine instead of penicillin. However, there is also a flip side to this approach, in the form of ambient voice technology (AVT) or 'AI scribes'. AVT could potentially introduce a new category of significant sound-alike error risk, as it could be easily misheard, leading to errors such as the use of penicillamine instead of penicillin. This raises a deeper question: how can we balance the benefits of AI with the potential risks, particularly in relation to LASA errors? In my opinion, this highlights the need for a more comprehensive approach to the design and implementation of ePMA systems, particularly in relation to LASA medicines. Other methods for reducing LASA errors have also been proposed, such as the 'Touchdose' system, which allows prescribing by indication and matches doses to indications, potentially reducing the risk of LASA errors. However, under-reporting remains a significant issue, with only about 1 in 100 prescribing errors and about 1 in 1,000 administration errors ending up reported as an incident report. This makes it difficult to know the true scale of LASA errors, particularly in relation to ePMA systems. In conclusion, it seems clear that LASA errors are unlikely to ever be fully eliminated. However, NHS England is hopeful that the LFPSE system will improve things, by enabling better use of the latest technology, such as machine learning, to create outputs that offer a greater depth of insight and learning that are more relevant to the current NHS environment. Perhaps this system, coupled with the potential of AI, means ePMA systems may eventually help to reduce the rate of LASA errors. However, it is essential that we continue to monitor and evaluate the risks and benefits of ePMA systems, particularly in relation to LASA medicines, to ensure that patient safety is maintained.
