A note that appears in seconds does not demonstrate how much work it saved you. To know this, you have to include the preparation, revision, corrections, and what remains pending after the consultation. The useful measure is the time required to complete a task of comparable quality.
You can conduct a practical evaluation without installing another analytics platform. Simply define what you want to track, observe comparable events, and record when the assistant was not useful. The goal is to make a decision about your work, not to produce a promotional percentage.
Define where the task begins and ends
For documentation, a possible definition is: active time dedicated to preparing, writing, reviewing, and finalizing the note. Separate that time from the wait while a file is being processed. If you perform another task during the wait, don't automatically count it as documentary work.
Also define what “finished” means: for example, reviewed and saved in the place where you need it. A note generated but pending correction does not belong in the same category. Keep the definition throughout the comparison.
First, observe your usual practice.
Record several encounters before changing the process. Note the type of consultation and whether there were any particular conditions: first visit, brief follow-up, extensive documentation, or interruptions. You do not need to copy clinical content onto the measurement sheet; use categories and times without identifiable data.
Then observe similar encounters with the assistant. Separate the learning period: setting up a format and learning to use it may require time that does not represent normal usage. Keep it as an onboarding cost, but don't quietly mix it with subsequent measurements.
Keep a brief and consistent record
For each encounter, note five fields: type of query; active minutes of documentation; important corrections; note completed that day; and reason for non-use when applicable. You can add a simple rating on patient care, making it clear that it is a personal perception.
Do not eliminate the meetings that went poorly. If the tool only helps with short follow-ups, that is a useful conclusion. An evaluation that only preserves the best results overstates the benefit you will find in a normal day.
Compare times with quality and earrings
The median helps summarize times when there are particularly long encounters. Also look at the distribution: a usual improvement may coexist with some queries that require further review. Compare by encounter type before aggregating everything into a figure.
As a hypothetical example, going from ten to seven minutes of active activity per note represents three minutes less in measured encounters. This does not imply that each consultation in the month will save the same amount. Multiplying that result across the entire schedule requires assumptions that need to be verified, especially if the complexity of the visits changes.
Avoid attributing all changes to the tool
A week with fewer patients, a new staff roster, or administrative support can also alter the time. Record those changes. A before-and-after comparison in your practice guides a practical decision, but on its own it does not demonstrate that AI made all the difference.
A Environmental documentation study published in JAMA Network Open It evaluated both metrics of recordkeeping usage and professional perceptions. That separation is useful: feeling less burdened and spending fewer minutes are related results, but different. The study did not evaluate Itaca.
Turn the measurement into a small decision
When you’re finished, decide which meetings to continue, what format you should adjust, and which step still takes too much time. Repeat the observation after the adjustment. You don’t need to declare success or failure for the entire platform at once.
If you evaluate clinical notes with AI, Include the review step within the measurement. If you want to place your results in a broader context, our Report on AI in medical practice It distinguishes activity, adoption, and results. This distinction prevents confusion between a function that is being used and an improvement that has already been demonstrated.
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