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12.9% of appointments are missed, worst for Eastside, Mon, 15+.

3,888 appointments, 501 missed: a 12.9% rate. About $42.6K of slot value is lost.

Flagged segments: Eastside, Mon, 15+, each missed at least 1.3 times as often as overall.

The worst combination is Eastside on Mon for 15+ days ahead: 46.2% missed of 52 booked.

Why patients missed is not in the data and no patient-level information is used; this shows where non-attendance concentrates.

12.9%
501/3888
Non-attendance
12.9%
Missed
501
3,888
Slot value lost
$42.6K
Worst cell
46.2%
Eastside · Mon

Missed by lead time (%)

5
0-3
9
4-7
14
8-14
24
15+

Missed by clinic (%)

  • Eastside17.5%
  • Central11.1%
  • Northgate9.5%

Missed by weekday (%)

  • Mon16.9%
  • Tue12%
  • Wed10%
  • Thu12%
  • Fri13.3%

Segments, highest rate first

SegmentBookedMissedRateFlagged
Lead time: 15+97223223.9%*
Clinic: Eastside1,37624117.5%*
Day: Mon84014216.9%*
Lead time: 8-1497213513.9%
Day: Fri79210513.3%
Day: Tue7088512%
Day: Thu7088512%
Clinic: Central1,31614611.1%
Day: Wed8408410%
Clinic: Northgate1,1961149.5%

What to do this week

  • Send a reminder and a confirm-or-cancel step to bookings made 15+ days ahead: they are missed 23.9% of the time.
  • Overbook carefully or add a short-notice list for Eastside, Mon: missed slots are concentrated there.
  • Bringing the biggest flagged segment to the overall rate would recover about 107 appointments in this period.

Do

  • Test one change at a time (reminder, shorter lead time, confirmation) and measure the rate after.

Don't

  • Don't overbook on the overall rate: the average hides the segments that are missed far more.

How to improve

  • Add reminder status and appointment type so the next report can see what already works.
  • Add cancellations with notice so the next report can separate late cancellations from true no-shows.

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Overview

Every clinic loses a significant share of its capacity to appointments nobody attends, and the standard responses — charging, overbooking, more reminders — all treat it as patient behaviour, and all make access worse for the people least able to attend.

The patterns say something different. Non-attendance concentrates: in appointments booked far ahead, in the first slot of the day, in slots that are hard to reach, in cohorts whose letters arrive after the appointment. Those are scheduling and communication facts, and every one is inside the clinic's control.

The second thing in the same data is capacity that was never lost to a patient at all: slots cancelled with enough notice to refill and never refilled, clinics finishing early while a waiting list exists, rooms booked for sessions that did not happen. Nobody records that as lost, because nobody failed to arrive.

This agent reports both, with the scheduling change that would recover each and what it is worth in appointments. It works on operational patterns only. It makes no clinical judgement, never profiles or scores an individual patient, and never recommends anything that would deprioritise a person.

Benefits

Capacity recovered without extra clinics

Most of it is already there and unused.

Access improved rather than rationed

The fixes are scheduling, not penalties.

Waiting lists that move

Because refillable slots actually get refilled.

What it does

  • Tests lead time first

    Non-attendance rises steeply with how far ahead an appointment was booked. It is the strongest lever and it is entirely yours.

  • Finds the slots people cannot reach

    First slot of the day, or one that needs two buses. A scheduling fact, not a patient failing.

  • Counts capacity nobody recorded as lost

    Cancellations with notice that were never refilled, and clinics finishing early against a waiting list.

  • Checks whether the letter arrived in time

    A cohort whose communication lands after the date. Invisible, and completely fixable.

  • Values each change in appointments

    What the fix recovers, so it competes against the alternatives properly.

  • Never profiles a patient

    It produces no individual risk score and recommends nothing that would deprioritise anyone. Patterns only.

What this changes

What this changes about your week

It takes over
Finding why appointments are missed and where capacity actually goes.
Done today by
Answered with charges, overbooking and more reminders.
How often
Reviewed as a rate, never as a pattern.
Typical manual effort
Lead time, slot, cohort and communication timing all analysed separately.

Why Healthcare in particular. Non-attendance concentrates in things the clinic controls — booking lead time, the first slot of the day, letters arriving late. And capacity lost to unfilled cancellations is never recorded as lost, because no patient failed to arrive.

  • Runs unattended

    Started by you or by an event, and it finishes on its own. Nothing waits for someone to be at a desk.

  • The same standard every time

    The two-hundredth item is held to the bar the first one was. Consistency is the part people cannot sustain.

  • It cannot act on its own

    Clinic Access Analyst has no path to sending, spending or committing. That limit is why its output is safe to act on.

How it runs

This agent runs server-side through the PROMIVO runtime. Each run is logged step by step and every tool call is permission-checked before it executes.

  1. 1InputValidated against the agent input schema
  2. 2Modelclaude-opus-5 with automatic fallback
  3. 3Toolsextract_structured_data
  4. 4PermissionsRuns READ actions automatically; anything higher waits for approval
  5. 5OutputStructured result matching the output schema
  6. 6LoggingTokens, duration and every step recorded

Requirements

Integrations

No integrations required.

Before you start

  • Appointment records: booked date, appointment date, outcome, slot, clinic
  • Cancellation records with notice given, and whether the slot was refilled
  • Communication send dates, where you hold them
  • Waiting list volumes
  • Requires the All-Access plan or higher

Use cases

Access and capacity review

Where capacity actually goes.

A rising non-attendance rate

Which pattern is driving it.

Before adding clinic sessions

Whether existing capacity is being used first.

Equity of access review

Whether attendance differs by cohort, and what scheduling explains it.

Pricing

All-Access

Billed monthly through your PROMIVO subscription. Cancel at any time.

Runs consume your plan allowance for agent executions and tokens. See plan limits

FAQ

Does it score individual patients?

No, and it refuses to. Individual non-attendance prediction leads to deprioritising the people who most need care. It reports operational patterns only.

Does it make clinical decisions?

Never. It makes no clinical judgement of any kind, does not assess urgency, and never suggests changing anyone's clinical priority.

Will it recommend overbooking?

Only where the data supports refill rather than double-booking, and it says plainly that overbooking transfers the cost to patients who did attend. It leads with the changes that create no such cost.

Does it handle patient data?

It analyses appointment records and reports aggregate patterns. It reproduces no patient identifier and suppresses cohorts below the size you set.

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