Are electronic prescribing systems increasing the risk of ‘look-alike sound-alike’ medication errors? The answer is a complex one, and it's not as simple as a yes or no. While the use of electronic prescribing can reduce medication errors by 30%, as the UK government claims, the reality is that there are potential risks associated with these systems, particularly when it comes to 'look-alike sound-alike' (LASA) medicines.
One tragic example of this is the death of a three-week-old baby, Sidra Aliabase, who was prescribed sodium acid phosphate instead of sodium chloride at approximately five times the recommended dose. This is a stark reminder of the potential risks associated with LASA errors in electronic prescribing systems.
However, it's not all doom and gloom. The use of electronic prescribing systems has also been linked to a reduction in errors involving incorrect doses and illegible or incomplete orders. But, as one study found, errors involving duplication, omission, incorrect drug, and incorrect formulation were more common with electronic prescribing.
So, what can be done to mitigate these risks? One solution is to implement 'tall-man lettering' in electronic prescribing systems, which involves capitalising certain letters in drug names to distinguish them from others. This has been shown to help, but it's not a foolproof solution.
Another solution is to change how drugs are grouped in electronic prescribing systems. For example, forcing drugs out of alphabetical order if necessary, to take penicillamine and penicillin away from each other in a list. This can help to reduce the risk of LASA errors.
The integration of clinical decision support AI could also help to prevent LASA errors. By applying logic and prompting, AI can help to ensure that the right drugs are prescribed. However, there are also concerns about the use of ambient voice technology (AVT) or 'AI scribes', which could introduce a whole new category of LASA error risk.
One promising solution is the development of a system called 'Touchdose', which allows prescribing by indication and matches doses to indications. This has been shown to reduce the overall prescribing error rate, and it's being developed to interface with other electronic prescribing systems.
However, the current system for reporting LASA errors is flawed. Only about 1 in 100 prescribing errors and about 1 in 1,000 administration errors are reported as incident reports, which means that the true scale of LASA errors is difficult to determine.
But, there is hope. The new Learn from Patient Safety Events (LFPSE) system is designed to improve the analysis of patient safety events and enable better use of technology, such as machine learning, to create outputs that offer a greater depth of insight and learning.
In conclusion, while electronic prescribing systems have their risks, there are also solutions available to mitigate these risks. By implementing 'tall-man lettering', changing how drugs are grouped, integrating clinical decision support AI, and developing systems like 'Touchdose', we can work towards reducing the rate of LASA errors. And, with the new LFPSE system, there is hope that we can eventually eliminate these risks altogether.
