In the quest for a more efficient and paperless healthcare system, the NHS has embraced electronic prescribing and medicines administration (ePMA) systems. However, this technological advancement has brought to light a critical issue: the potential for 'look-alike sound-alike' (LASA) medication errors. The tragic case of baby Sidra Aliabase, who died due to a prescribing error, serves as a stark reminder of the consequences of such mistakes.
The question arises: are these electronic systems increasing the risk of LASA errors, or are they simply shifting the error landscape?
The LASA Error Conundrum
LASA errors occur when medications with similar names or sounds are confused, leading to potentially fatal consequences. With the introduction of ePMA systems, the error pattern seems to have shifted.
In the past, with paper-based prescribing, errors often occurred due to illegible handwriting, resulting in 'look-alike' mistakes at the dispensing or administration stage. Now, with electronic systems, the error is more likely to happen at the prescribing stage, as healthcare professionals select from drop-down menus.
Data Challenges and Incident Reporting
Obtaining accurate data on LASA incidents is a complex task. The transition from the National Reporting and Learning System (NRLS) to the Learn from Patient Safety Events (LFPSE) service has created a dual reporting issue, making it difficult to determine the true extent of LASA errors.
Additionally, the way incidents are reported further complicates matters. There is no specific category for LASA incidents, and reports are often submitted as free text, making data extraction challenging.
Mitigating Errors: A Multi-Pronged Approach
Various strategies are being employed to tackle LASA errors. One such method is 'tall-man lettering,' where certain letters in drug names are capitalized to distinguish them from others. While this technique has shown some success, it doesn't eliminate the risk entirely.
Another approach suggested by experts is changing how drugs are grouped in drop-down menus. By forcing certain medications out of alphabetical order, the system can reduce the likelihood of LASA errors.
The Promise of AI
The integration of clinical decision support AI holds promise in preventing LASA errors. By analyzing patient records and clinical notes, AI can provide sophisticated prompts to healthcare professionals, reducing the risk of errors.
However, the introduction of ambient voice technology (AVT), or 'AI scribes,' brings a new challenge. AVT, which generates transcripts and clinical summaries from patient-doctor conversations, may introduce a new category of sound-alike errors.
Touchdose: A Potential Solution
Touchdose, a clinical decision support system, offers a different approach to reducing LASA errors. By prescribing based on indication and matching doses to indications, Touchdose forces prescribers to consider the clinical rationale behind their choices, potentially reducing look-alike errors.
Under-Reporting and the Role of AI
The limited data available on LASA errors is a concern. Experts estimate that only a fraction of prescribing and administration errors are reported, making it difficult to grasp the true scale of the problem.
AI, with its ability to analyze large datasets, may offer a solution. By improving the reporting system and making it more accessible, AI can encourage healthcare professionals to report near-misses and incidents, providing valuable data for analysis and improvement.
The Future of LASA Error Prevention
While LASA errors are unlikely to be fully eliminated, the NHS is hopeful that the LFPSE system, coupled with AI integration, will improve patient safety. The potential for AI to create new error types is a concern, but experts believe that with proper investment and skill development, the benefits of AI in medication safety are too great to ignore.
As we navigate the complexities of electronic prescribing, it's crucial to strike a balance between technological advancement and patient safety. The challenge lies in understanding the cognitive mechanisms that lead to errors and using design principles to mitigate risks effectively.
