What explains the fall in missed visits?
Compare the overall rate with the rates for each booking channel.
The question
You support a fictional outpatient reminder pilot. The improvement lead asks for an update on two weeks of scheduled visits.
| Scheduled | Missed | |
|---|---|---|
| Week A | 200 | 26 |
| Week B | 200 | 21 |
Both weeks exclude cancellations and use the same definition of a missed visit.
- Pilot recordThe summary does not show how the pilot was assigned or whether there was a comparison group.
“What changed, and what can you say about the pilot?”
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A strong answer, step by step
Open each step in turn. This is one strong answer, not the only one.
Step 1: What is the observed change?
What would you do here? Open the move you’d make.
Report a fall of five percentage points
Tempting, but it misses something. Five is the difference in missed visits, not in rates.
Report 13% to 10.5%, down 2.5 percentage points
The strong move. Divide each missed count by its own 200 scheduled visits.
Report that 89.5% missed their visits
Tempting, but it misses something. 89.5% is the share not recorded as missed in week B.
See how a strong candidate says it
“Missed visits fell from 26 of 200 to 21 of 200. The rate went from 13% to 10.5%, a fall of 2.5 percentage points.”
Why it works: Both totals count the same number of scheduled visits with the same cancellation exclusion.
Step 2: What can you say about the pilot?
What would you do here? Open the move you’d make.
Describe the fall and ask about the groups and pilot assignment
The strong move. You need those facts before making an impact claim.
Credit reminders with five prevented missed visits
Tempting, but it misses something. There is no comparison showing which visits would have been missed without a reminder.
Use a survey at check-in to explain every missed visit
Tempting, but it misses something. People who did not check in are missing from that sample.
See how a strong candidate says it
“The measured rate fell. I’ll check who was in each group, which visits received reminders and how the comparison was set up.”
Why it works: The first two totals show an observed change. They do not show how the pilot was assigned.
Step 3: What does the channel split show?
The groups are app-confirmed and assisted booking. Week A missed visits were 8 of 100 app-confirmed and 18 of 100 assisted. Week B had 12 of 150 app-confirmed and 9 of 50 assisted.
What would you do here? Open the move you’d make.
Say app-confirmed attendance got better
Tempting, but it misses something. 8 of 100 and 12 of 150 are both 8% missed.
Move all bookings to the app to repeat the result
Tempting, but it misses something. The split does not establish why the rates differ or whether everyone can use the app.
Report both flat rates and the shift in mix
The strong move. The new proportions reproduce the overall change.
See how a strong candidate says it
“The app-confirmed group stayed at 8% and assisted booking stayed at 18%. App-confirmed rose from 50% to 75% of the schedule. That shift in mix accounts for the fall in the overall rate.”
Why it works: Both group rates are flat. The week B total gives more weight to the group with the lower observed rate.
Step 4: What do you tell the lead?
What would you do here? Open the move you’d make.
Recommend withdrawing assisted booking
Tempting, but it misses something. The comparison gives no evidence that people using assisted booking can switch.
Report the rates and ask the lead to check comparable groups and access barriers
The strong move. The lead can check why the groups differ without assuming a pilot effect.
Give only the overall fall as the success measure
Tempting, but it misses something. That leaves out the channel split that explains the observed change.
See how a strong candidate says it
“The overall missed rate fell 2.5 points. Both channel rates stayed flat. I’d compare like groups and check who could not use each channel before proposing a change.”
Why it works: This reports the pattern, identifies the gap in evidence and gives the improvement lead a next check.
The answer, in short
- Decision
- Report the lower overall rate and the two flat channel rates.
- Because
- The share of app-confirmed visits grew from 50% to 75%. Its observed missed rate was lower in both weeks.
- What would change it
- With a suitable comparison and allocation record, the improvement lead can check whether the pilot changed outcomes.
Common traps
- Treating counts as percentage points
- Using an after-check-in survey to explain everyone who missed a visit
Check your answer
- What is the denominator each week?
- What changed within each channel?
- What evidence could support an impact claim?
- Whom would an after-check-in survey miss?
Last reviewed Nov 11, 2026