Researching interactions and monographs faster

A good first-pass prompt

'A patient on warfarin is being considered for a short course of fluconazole. What is the general mechanism of interaction between azoles and warfarin, and what should I be checking before dispensing? I will verify specifics against the monograph and Lexicomp myself.' Notice the prompt asks for orientation and explicitly states that verification follows: write that discipline into your own habit, not just the prompt.

The verification step you never skip

Before any interaction, dosage, or contraindication claim from an AI tool reaches a patient — as a dispensing decision, a counseling point, or advice to a prescriber — check it against an authoritative source: a current monograph, a drug interaction database like Lexicomp or Micromedex, the local formulary, or a pharmacist colleague. Not sometimes. Every time, for every specific claim.

This is not caution for its own sake. It is the one habit that converts AI from a genuine time-saver into a genuine risk, depending entirely on whether you do it. Skip it once because the answer looked confident and reasonable, and the one time it happens to be wrong is the time that matters.

Where errors are most likely

• Interactions involving newer drugs, narrow formulations, or combinations rarely discussed in text: thin training data means more invention.
• Exact numeric thresholds — a specific percentage, a specific mg/kg cutoff — are the detail most likely to be plausible-sounding and wrong.
• Anything that depends on renal or hepatic function, pregnancy status, or age band, where the 'general' answer silently drops the patient-specific qualifier.
• Older or superseded guidance the model may reproduce as current when it is not.