What "Why This Result" Should—and Should Not—Mean

A feature that answers a specific question

The "why this result" panel in our workspace exists to answer one narrow question well: which structured inputs and which approved source passages contributed to this particular part of a draft summary. When a pharmacist opens it, they see the intake fields the system used, the citations attached to the relevant statements, and, where relevant, which flagged interaction or note triggered a particular line. That's a useful, bounded thing to show.

Keeping the feature narrow is intentional. The moment an explainability panel tries to do more — for example, offering a confidence percentage or a plain-language claim that the result is 'likely correct' — it starts making assertions the underlying system isn't positioned to justify, and it starts encouraging exactly the kind of over-trust we're trying to design against elsewhere in the product.

What it deliberately avoids showing

We chose not to display numeric confidence scores or probability-style outputs. These figures are common in machine-learning interfaces, but they tend to convey a false sense of calibrated certainty, especially to reviewers under time pressure who may treat a '92%' as far more meaningful than the underlying statistic actually supports. Instead of a number, we show the underlying evidence and let the pharmacist form their own judgment about it.

We also avoid framing this panel as a diagnostic or explanatory tool for the clinical content itself — it explains what the software did, not why a particular guideline consideration is clinically appropriate. That second kind of explanation is squarely the pharmacist's professional domain, informed by the sources shown, not the software's.

Explainability as a review accelerator

The practical benefit of this design is speed without a corresponding loss of scrutiny. A pharmacist reviewing a draft summary can quickly check whether the system used the intake data correctly and whether the cited source actually supports a given statement, without having to reconstruct that reasoning chain from scratch. That's the productive use of explainability: making verification faster, not making verification unnecessary.

We've found this framing also helps set expectations correctly during walkthroughs of the product — people who first encounter an AI drafting tool often expect a black box, and a visible, inspectable reasoning trail changes the conversation from 'should I trust this' to 'here's specifically what I'm checking.'

An honest boundary

"Why this result" is a workflow feature built for a demonstration-grade product, not a certified explainability framework, and we don't present it as one. It shows the traceable inputs behind a draft. Everything about whether those inputs lead to a sound clinical judgment remains with the reviewing pharmacist.