Prescribing errors are most often framed in terms of clinical risk, with healthcare discussions focusing on the potential for patient harm. However, an equally important, but far less visible, consequence is the significant administrative workload generated by prescribing errors that never reach the patient. These so-called "near misses" are caught before any physical harm occurs, and for this reason, they rarely attract attention. Yet across hospitals and clinics, they quietly consume time, staffing capacity, and system resources on a scale rarely appreciated.

A recent outpatient example illustrates this problem clearly. A patient attending a urology clinic was issued a prescription for silodosin despite already taking doxazosin, a combination that increases the risk of postural hypotension. The hospital pharmacist identified the issue immediately and prevented any clinical impact. Although the patient remained completely safe, resolving the error triggered a chain of administrative activities involving several people and multiple steps. The pharmacist needed to interrupt their workflow to query the prescription with the clinical team. The consultant's secretary became involved in managing and relaying the communication. The consultant was required to review the issue, and the prescribing doctor had to be updated. Ultimately, the patient was contacted and brought back to clinic so that the treatment plan could be corrected.

None of this activity was related to patient care in the usual sense; it was entirely focused on repairing an avoidable error. While each incident may appear minor, the cumulative effect across a health service is considerable. These episodes divert clinicians from clinical duties, interrupt pharmacy processes, increase outpatient activity, and place additional demands on administrative staff. In an already pressured health system, the overall impact of these harmless errors becomes a hidden drain on time, efficiency and capacity.

This is precisely the type of problem Katana AI is designed to prevent. A duplicate alpha-blocker prescription, such as silodosin, for a patient already taking doxazosin is the kind of issue Katana instantly detects. As a prescription is entered, Katana automatically analyses the medication list and identifies clinically relevant conflicts, including therapeutic duplication, drug–drug interactions, and guideline-based cautions. In this scenario, Katana would have alerted the prescriber to the duplicate alpha-blocker combination before the prescription was finalised, clearly explaining the associated risk and prompting a review of the plan. The issue would have been corrected within seconds at the point of prescribing, long before the prescription reached the pharmacist.

By resolving problems directly at their source, Katana prevents the downstream sequence of queries, administrative involvement, consultant review, communication loops, and patient recall. The patient remains safe, and the clinical team avoids an unnecessary administrative burden. Instead of multiple staff members becoming involved in correcting an avoidable issue, the entire process ends immediately, without ever disrupting workflow.

This is the practical value of real-time, evidence-based prescribing support. It prevents not only potential harm but also the operational inefficiencies that accumulate quietly across the health service every day. Prescribing errors may not always harm patients, but they can significantly harm system capacity. Katana provides a way to prevent both, helping healthcare organisations deliver safer, smoother and more efficient care.