The thread raises the question; it does not answer it
A July 2026 r/pharmacy discussion asked whether newer pharmacy-management systems had adopted AI. Replies ranged from optimism about workflow and documentation to blunt skepticism about installing immature software. That makes the thread a useful prompt for diligence, but not a market survey, feature inventory, or product evaluation. The commenters' identities and individual workplaces add nothing necessary to the buyer test, so the useful signal can be discussed without repeating them.
1. Make the product name one bounded task
Start with a sentence that has a verb and an object: draft a note from selected fields, classify an incoming item, identify a possible duplicate, or summarize a defined record. These are test prompts, not claims about any product in the thread. Ask what starts the feature, whether it runs automatically or only when requested, and where its output appears in the existing workflow.
Then draw the boundary. What decision remains outside the feature? What work must still be completed by a pharmacist? If the vendor cannot describe the task without returning to broad language about intelligence, productivity, or transformation, there is not yet enough scope to design a meaningful demonstration.
2. Inspect every input and the complete output
Ask the demonstrator to show which fields, documents, or user entries reach the feature and which do not. Use synthetic or appropriately protected data, and avoid placing resident information into an unapproved demonstration environment. The buyer should also ask where submitted information is processed or retained, who can access it, and what current vendor documentation supports those answers.
On the output side, look past fluent wording. Can the reviewer see the source context, distinguish supplied facts from generated language, and trace a result back to the relevant record? Copy the output into the next real workflow step during the test. An impressive panel that creates another retyping task may add polish without removing work.
3. Find the human decision point
Identify who reviews the output, what that person can accept, edit, reject, or defer, and what happens after each choice. The role should be visible in both the interface and the operating procedure. A label such as ‘human in the loop’ is not enough if the user cannot tell what requires review or if an output silently advances before that review occurs.
Use a deliberately plausible but wrong suggestion in the demonstration. Observe whether the reviewer has enough context to catch it and whether the original input, generated output, edits, and final action remain distinguishable. This does not establish clinical performance; it checks whether the proposed review gate is usable when the output deserves scrutiny.
4. Force the feature away from its happy path
Remove a required field, add conflicting information, duplicate an item, and enter something outside the expected range. Watch how uncertainty and errors appear. Does the feature stop, ask for clarification, return a confident-looking answer, or hide the item? Can the user continue the underlying task without it, and is there a clear route for correcting or reporting a problem?
NIST's AI Risk Management Framework emphasizes governing, mapping, measuring, and managing risk. For a small practice, that broad framework can become a practical list: a named owner, bounded use, documented test cases, a monitoring routine, rules for acceptable data, and a way to disable or work around the feature. A vendor still needs to provide evidence for its own implementation; citing a framework is not proof that the product is safe, compliant, or effective.
5. Define the result before the pilot begins
Choose one result the practice can observe, such as minutes spent on duplicate entry, the number of unresolved exceptions at closeout, or the completeness of a work queue. Record the current baseline and the test period, then compare the same bounded workflow. Count review and correction time as part of the result rather than treating generated output as finished work.
Use synthetic or appropriately protected data until the practice has approved the environment and data route. Adoption, generated word count, and vendor-described automation are not substitutes for a completed task. A faster draft also does not establish clinical quality, compliance, or savings unless those separate conclusions have their own appropriate evidence.