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WasteGuard · fictional workflow, real oversupply problem

Prevent repeat oversupply before dispensing.

WasteGuard compares supply timing, expected use, stock and medication changes to identify repeat items that may not be needed this cycle. The system flags; an authorised human decides.

Demo patientDEMO-10423 repeat medicines0/3 reviewed
Check needLast supplied 24 days ago

Atorvastatin 20 mg

Last quantity28tablets
Expected remaining4estimated
Patient reports10remaining
Why flagged
  • Requested 4 days before the expected cycle end.
  • Patient-reported stock (10) is higher than the modelled remainder (4).
Recovery record

Only verified actions count.

The demo values are illustrative. In a pilot, only a confirmed dispensing outcome can become a recovery record.

0

Supplies deferred

£0.00

Illustrative cost avoidance

0

Professional reviews

0

Autonomous stops

Evidence → product → proof

Separate what is already established from what Sitora still has to prove.

The published evidence supports the problem or intervention mechanism. A Sitora pilot must establish the local effect.

Proven already

What existing evidence establishes

NHSBSA already operates an Oversupply Dashboard because prescribers often see prescriptions one at a time rather than cumulative quantities actually dispensed. Repeat prescriptions make up around three-quarters of prescription items. A UK community-pharmacy study also found that 66% of study patients did not require their full quota of prescribed repeat medicines; the associated cost figure is historic and should not be treated as a current NHS estimate.

What Sitora adds

The product layer

Move from retrospective population-level oversupply signals to an individual, governed intervention before the next unnecessary supply is dispensed, using timing, cumulative supply, medication changes and optional stock confirmation.

Pilot must prove

No assumption allowed

How many flagged items are truly unnecessary; how many supplies are actually prevented; the workload created for patients, pharmacies and prescribers; missed-essential-medicine risk; net financial effect; and whether targeting is precise enough for routine use.

Evidence used in this demo