Integration

Go-live isn't the end of the checking. This is the routine that catches drift.

A repeatable weekly check on a voice AI integration already in production: which calls to sample, what to compare against what, how to tell a capture error from a sync error, and who to take a discrepancy to.

Sample a handful of calls every week — five to ten is enough to show a pattern — rather than auditing once at go-live and never again
For each sampled call, listen to the recording and compare it against the structured summary that came out of it
Then compare that summary against what actually reached your EMR, so a capture error and a sync error can be told apart
Check the transactional side on its own: the appointment, the new patient record, and the demographics that write directly
Confirm the clinical side stayed on the review path and nothing appeared in the chart without a person putting it there
Take any discrepancy to both your EMR vendor and your AI vendor with the timestamp and the call attached, so it can be traced rather than argued about

How it pays back

A weekly sample catches drift; an acceptance test does not

Everything works on the day you check it, which is why checking once proves very little. A small sample every week is what surfaces the change nobody announced — a new field, a renamed appointment type, a workflow someone adjusted.

Separating capture from sync tells you who to call

"The allergy is wrong in the chart" has two completely different causes. Comparing the recording to the summary, and then the summary to the EMR, splits the problem in two before anyone opens a support ticket.

The audit trail makes a discrepancy traceable

Every sync and every manual entry is timestamped and attributed, so a question about a specific call has a specific answer. Without that, an audit produces suspicion rather than a finding.

Direct EMR write-back

Appointment bookings, new patient records, and patient demographics (name, DOB, phone) sync in real time

Structured summaries for review

Intake, medical history, allergies, and chief complaint captured and returned as staff-verified summaries

Named EMR integrations

Native connectors to athenahealth, eClinicalWorks, Epic, Elation, Cerbo, Hint, Tebra, AdvancedMD, and ModMed

Staff approval required

Every non-appointment data point verified before entry into the permanent medical record

Frequently asked questions

How many calls should we spot-check, and how often?

Five to ten calls a week is a practical baseline. It is small enough that someone will actually do it, and large enough that a systematic problem shows up as more than one occurrence. Sample across different call types rather than only the ones that looked interesting.

What exactly am I comparing against what?

Two comparisons, in order. First, the call recording against the structured summary: did the AI capture what the patient said? Second, the summary against your EMR record: did what was captured reach the right place? Doing them separately is what makes the result diagnostic.

How do I tell whether the AI misheard or the sync failed?

If the recording and the summary disagree, it is a capture problem. If the summary and the EMR disagree, it is a sync or a filing problem. If both agree and the chart is still wrong, look at what your staff filed and when — the audit trail records that step too.

What do we do if the AI captured an allergy incorrectly?

This is the case human review exists for. Your staff listens to the recording, spots the error, corrects it, and enters the accurate allergy in the chart. The summary is the AI's understanding; the recording is what the patient actually said, and the chart follows the second one.

Who do I take a discrepancy to?

Both vendors, with evidence. Bring the call timestamp, the summary, and the EMR record so the two sides can trace the path rather than each assuming the other end failed. A discrepancy reported without the call attached tends to go nowhere.