The tragic case of baby Sidra Aliabase, who lost her life due to a prescribing error, highlights a critical issue in healthcare: the potential risks associated with electronic prescribing systems. This incident, where a newborn was given the wrong medication at a dangerously high dose, is a stark reminder of the consequences of 'look-alike sound-alike' (LASA) medication errors.
In this article, we delve into the complexities of LASA errors, exploring how they occur, their potential impact, and the measures being taken to mitigate them. We'll also examine the role of electronic prescribing systems and whether they are exacerbating or reducing these errors.
The Rise of Electronic Prescribing
Since the 1990s, electronic prescribing and medicines administration (ePMA) systems have been gradually introduced across the NHS, with a push towards a paperless system. While the benefits of ePMA are widely acknowledged, including a potential 30% reduction in medication errors, the question remains: are these systems inadvertently creating new opportunities for errors, particularly LASA errors?
Understanding LASA Errors
LASA errors occur when medications with similar names or sounds are confused, leading to potential harm. For instance, the tragic case mentioned earlier involved sodium acid phosphate being prescribed instead of sodium chloride. These errors can happen at various stages, from prescribing to administration, and can have devastating consequences.
Data Challenges
Obtaining accurate data on LASA incidents is a significant challenge. The transition from the National Reporting and Learning System (NRLS) to the Learn from Patient Safety Events (LFPSE) service has led to potential dual reporting, making it difficult to determine the true extent of LASA errors. Additionally, the lack of a specific category for LASA incidents in reporting systems further complicates data collection.
Replacing One Error with Another?
Experts suggest that the move from paper-based to electronic prescribing may have simply shifted the nature of errors. With paper prescribing, illegibility errors were more common, whereas electronic systems introduce new challenges, such as drop-down menu errors. Julia Scott, a pharmacist and CIO, puts it succinctly: "It's almost that we've taken away illegibility errors with one hand, and given drop-down menu errors with the other."
Mitigating Errors
Various strategies are being employed to reduce LASA errors. One such method is 'tall-man lettering,' where certain letters in drug names are capitalized to distinguish them. While this helps, it's not a complete solution. Julia Scott suggests implementing tall-man lettering in e-prescribing systems and changing drug grouping to reduce LASA errors.
The Role of AI
The integration of clinical decision support AI is seen as a potential game-changer. AI can provide sophisticated prompts, especially when integrated with electronic patient records. However, the introduction of ambient voice technology (AVT) or 'AI scribes' brings new challenges, as these systems may introduce a whole new category of sound-alike errors. Julia Scott highlights the need to carefully consider these new error mechanisms and find ways to mitigate them.
Other Methods to Reduce LASA Errors
Systems like Touchdose, which allow prescribing by indication, are being developed to reduce LASA errors. Touchdose, a clinical decision support system, calculates the correct dosage based on the patient's indication and characteristics. A study showed a significant reduction in prescribing errors when using Touchdose.
Under-Reporting and the Role of AI
The limited data available on LASA errors is largely due to under-reporting. Bryony Dean Franklin emphasizes that reporting requires awareness, willingness, and time, which are often lacking in busy healthcare settings. AI is seen as a potential solution to analyze and interpret the vast amount of incident reports, making it easier to identify patterns and improve medication safety.
The Future of LASA Error Prevention
While LASA errors are unlikely to be fully eliminated, the hope lies in systems like LFPSE, which aim to improve analysis and learning from patient safety events. The potential of AI to enhance medication safety is also a promising development. However, as Julia Scott notes, we must address environmental and ethical concerns associated with AI and ensure its safe implementation.
In conclusion, while electronic prescribing systems offer benefits, they also introduce new challenges. The key lies in understanding these challenges, investing in the right skills and knowledge, and using technology, like AI, to enhance medication safety. Only then can we hope to reduce the rate of LASA errors and prevent tragedies like baby Sidra's death.
