Clear cases keep moving.
Identify, assign a destination and record the route: the software handles repetitive tasks. In the planned first phase, physical placement remains manual.
Reverse logistics for hospital pharmacy
Assisted identification, guided sorting and traceability for returned medicines.
PharmacOps aims to delegate repetitive tasks to software and focus attention on exceptions. From evidence to destination, with every step recorded.
See the workflow in action Interactive prototypeGroup by destination and prepare for restocking.
Watch it fill automaticallyFROM CHALLENGE TO PROCESS
Handling a return means identifying the item, deciding where it belongs and recording what happened. PharmacOps brings these tasks into one workflow that moves clear cases forward and flags those needing attention.
Gather the sample evidence.
Suggest a reference and flag uncertainty.
Assign a compatible position.
Keep the decision and every movement on record.
CLEAR SORTING
Continue. Review. Separate.
Identify, assign a destination and record the route: the software handles repetitive tasks. In the planned first phase, physical placement remains manual.
Insufficient evidence or a discrepancy triggers a review. The exception stays located while subsequent samples continue. Resolving it leaves a decision and a reason that can inform future improvements.
Expired, opened, damaged or unapproved items leave the restocking workflow. The cause is recorded and the issue remains traceable.
STATUS AND DEVELOPMENT
A single model connects decisions, positions and movements. Physical automation will be introduced in modules once the process and its rules have been validated.
PHASE 1 · SOFTWARE
The demo already explores sorting, destinations, trays and traceability. Physical movement in the planned MVP will be manual.
PHASE 2 · PLANNED DEVELOPMENT
Input, sorting and physical execution can be added as modules using the same position model. Each module will require its own testing.
Phase 2 concept diagramCollects the image and preserves the information observable in it.
Product outline. The status shows what has been tested in the demonstration and what requires integration.
The final reference, reason and any correction help identify where the process fails and guide improvements. Professional judgement comes in when a case needs review.
The demo records these signals. Model evaluation and training remain pending; there is no real-time machine learning.
ORIGINS AND VALIDATION
Born from LabORA Challenge 6 on medicines returned to hospital pharmacy.
We aim to assess the workflow with professionals and prepare future tests using authorised samples.
Contact
PharmacOps welcomes collaboration to validate the process and develop its next phases.
[email protected]We are looking for hospitals, pharmacy services and healthcare professionals to help validate PharmacOps against real medicine return, identification and sorting processes.
We want to understand today's workflows first-hand, identify real needs and explore future pilot studies.
We are looking for engineers, robotics companies, integrators, technology providers and entrepreneurs who want to help develop the physical side of PharmacOps.
From computer vision and sensors to sorting and automation systems, we want to build an architecture ready to scale into real environments.
PharmacOps is at an early stage and open to discussions with investors interested in healthtech, medtech, robotics, deeptech and automation.
We are looking for partners who share our vision of transforming a largely manual pharmaceutical process into scalable technological infrastructure.
Do you work in pharmaceuticals, healthcare, technology or research and think we could build something together?
We welcome universities, technology centres, researchers, suppliers and others who can contribute knowledge, technology or new collaboration opportunities.
FREQUENTLY ASKED QUESTIONS
No. The planned first phase combines a mobile camera, software and physical trays with human placement. Connected capture and mechanical automation are future integrations.
A sample's journey to its destination: identification, grouping by zone and traceability in T01. Follow it step by step or use automatic mode with speed and pause controls.
The demo records the responsible person, decision, reason, conflicting evidence and corrections. This information could support future evaluations; today there is no automatic training, learning pipeline or measured accuracy improvement.
The case remains located and awaiting review while subsequent samples can continue. Intervention focuses on that exception; insufficient evidence does not become a confirmed identity.
No. Green indicates suitability to continue in the logistics workflow; dispensing requires its own professional procedure.
It is not presented as a deployment or an endorsed solution. Hospital Clínic promotes the challenge that inspired the project.