I want an AI assistant to use the same definition as the person who owns the work. If the office manager says production, the software, dashboard, and assistant need to know which transactions count, which dates apply, and whether adjustments are included.
Many business questions look simple until two people answer them from different systems. How many new patients did we acquire last month? What is outstanding in accounts receivable? How productive was the imaging equipment? Each answer depends on a definition, a source, a time period, and a decision about what belongs in the count.
An AI assistant can make the disagreement appear faster and more polished. It cannot resolve an undefined metric on its own.
Microsoft's explanation of Fabric IQ illustrates the same requirement. The company describes grounding AI answers in Power BI models that define metrics, relationships, business terms, and access controls. That approach depends on definitions the organization already trusts. A practice still has to decide which definitions are correct and reconcile its source records.
The useful idea applies well beyond Microsoft's stack. Practice intelligence begins by deciding what the numbers mean and where the accepted answer comes from.
I would start with a short operating dictionary for the questions the team asks most often:
- The metric or question in plain language.
- The exact definition, including exclusions and time boundaries.
- The source system and fields that support the answer.
- The person responsible for the definition.
- The refresh schedule and the date of the underlying data.
- The roles allowed to see or act on it.
- The next action the answer is meant to support.
Consider insurance verification. “Verified” could mean that a portal returned an active status. It could mean that the practice confirmed eligibility, benefits, limitations, frequencies, and remaining maximums. It could also mean that someone reviewed an unusual response before it reached the patient conversation. A useful workflow records which level was completed, the source and time of the check, any uncertainty, and who owns the next step.
Imaging operations need the same discipline. Exam count, retake rate, exposure settings, downtime, and service history can help a team understand equipment use. They do not mean the same thing, and none should be presented as a complete measure of diagnostic image quality. The dashboard should preserve the underlying record and make room for technical and clinical review.
Once the definitions are stable, an assistant can do useful work around them. It can retrieve the right record, explain a change, flag missing information, prepare a summary, and route an exception. The answer should still show its source, time range, and unresolved gaps. Missing data should remain missing rather than quietly becoming zero.
Start with one question the team answers every week. Write the accepted definition, identify the source, and compare the assistant's answer with the manual result on a representative set of cases. When the two disagree, trace the difference back to the definition, the source data, or the retrieval step. Fix that layer before expanding the system.
Keep the accepted definition beside the report or workflow that uses it. When a source or business rule changes, update that record and repeat the comparison before relying on the next answer.