From recording events to anticipating needs
Medication management has to balance two opposing risks
A hospital pharmacy must protect medicine availability while avoiding excessive inventory that ties up funds and increases expiry risk. Changing ward demand, seasonality, uncertain deliveries, budget constraints and numerous contracts make forward-looking decisions difficult.
Too little inventory
A shortage may hinder or delay therapy and trigger time-consuming intervention purchasing.
Too much inventory
Excess stock ties up financial resources and increases the risk of medicine expiry.
Planned decision-support layer
Pharma Flow will complement the pharmacy system
Pharma Flow will use artificial intelligence, data analysis and federated learning to support demand forecasting, inventory optimisation, contract-performance monitoring and supplier communication. It will not replace a pharmacist or the software already used by the pharmacy.
- Which medicine may run out before the next delivery?
- Where may excess or expiry risk arise?
- Is a supplier performing the contract as agreed?
- Which order is delayed and requires action?
- Is a proposed substitute consistent with the contract?
- Where can a medicine be sourced in an emergency?
Planned operating model
How will Pharma Flow support the pharmacy?
The solution is intended to move medication management from reacting to recorded events towards earlier risk identification, an explained recommendation and an action approved by an authorised user.
- 01
Use data from existing systems
Historical consumption, inventory movements, stock levels, deliveries, orders, expiry dates, contract performance and selected hospitalisation or availability data may be analysed. The scope will be agreed with each hospital.
- 02
Forecast demand
Predictive models may consider seasonality, hospital characteristics, ward workloads, hospitalisation volumes, local epidemiological trends and unusual consumption changes.
- 03
Recommend inventory levels
The system may indicate shortage risk, excessive stock, expiry risk, a recommended order date and a suggested quantity for staff review.
- 04
Monitor contract performance
A planned natural-language-processing module will compare contract terms with actual deliveries and may flag delays, quantity or price differences and non-compliant substitutes.
- 05
Organise supplier communication
Standard correspondence and supplier replies may be connected with the relevant order, medicine, contract or case, while remaining under staff supervision.
- 06
Support intervention orders
If the regular supplier fails to perform, the system may help identify alternatives, send availability enquiries and compare responses. A user will approve the offer and order.
Planned dashboard
One view of forecasts, risks, orders and recommended actions
The main screen is intended to provide a concise view of the pharmacy situation and the items that may require attention. Each recommendation should explain what it concerns, the data behind it and the expected consequence.
Consumption · stock · time to depletion
- Projected shortage
- Unusual consumption increase
- Low inventory rotation
- Confirmation status
- Expected date
- User action required
- Adjust the planned order
- Contact a supplier
- Review a substitute
Four pillars
A connected process rather than another isolated report
Demand forecasting
The system will forecast future medicine demand using historical data, hospital characteristics and time-dependent factors.
Inventory optimisation
Pharma Flow will help limit both shortage risk and inventory levels that exceed probable needs.
Contract monitoring
Planned NLP mechanisms will compare contract terms with delivery performance and flag potential discrepancies.
Communication and orders
The system will organise supplier contact and support sourcing a medicine when the standard supplier cannot deliver.
Federated learning
Hospitals retain source data; model updates support shared learning
Each partner hospital may train a local model using data that remains in its environment. The shared process will receive the parameters required to update a global model rather than patient source data or complete operational datasets.
Federated learning does not automatically eliminate every privacy risk. Architecture, security and the scope of exchanged information will be analysed and verified during the project.
Complementing existing software
Pharma Flow will not replace the pharmacy system
AMMS by Asseco, KS-ASW by Kamsoft, CGM Clininet and other HIS or pharmacy systems will remain responsible for source records, warehouse documents, settlements and operational data. Pharma Flow will create an additional analytical and recommendation layer.
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Benefits to be verified during implementation
One system, different perspectives within the hospital
Hospital pharmacist
- Earlier warnings about possible shortages
- Order-planning support and fewer manual analyses
- Organised supplier communication
- Explained recommendations with approval controls
Pharmacy manager
- Concise view of inventory and contract performance
- Control of shortage, excess and expiry risk
- Supplier-performance information
- Analysis of how recommendations are used
Management and finance
- Better visibility of inventory value
- Support for reducing funds tied up in stock
- Analysis of delivery problems and their cost
- Data supporting future contract negotiations
Procurement, administration and IT
- Ordered contract and communication history
- Support for monitoring dates and terms
- Integration without replacing the pharmacy system
- Control over the target deployment and data-flow model
Human oversight
The authorised user will remain at the centre of the process
Pharma Flow will support logistical, administrative and purchasing processes. It will not make clinical decisions, recommend patient treatment or autonomously submit an intervention order.
- Change the planned order
- Select an alternative supplier
- Accept offer terms
- Approve a substitute
- Submit an intervention order
- Act on a detected contract discrepancy
Planned deployment
From data analysis to a controlled production launch
After R&D, Pharma Flow is intended to be offered as SaaS or installed on premises. The final model will depend on security requirements, IT architecture, data-processing rules and hospital policy.
SaaS
A cloud environment maintained by the supplier for hospitals that do not want to manage the application infrastructure themselves.
On-premises
An installation in the hospital environment, with infrastructure and technical administration remaining under hospital control.
- 01
Environment analysis
Systems, pharmacy processes, available data and data quality.
- 02
Data integration
Mechanisms for obtaining data from the pharmacy system, HIS and other sources.
- 03
Process configuration
Rules for inventory, alerts, contracts, suppliers and action approval.
- 04
Local model preparation
Models adapted to the characteristics and data of the hospital.
- 05
Pilot
A limited observational and recommendation scope.
- 06
Outcome assessment
Comparison of forecasts, recommendations and detected issues with actual events.
- 07
Production launch
Gradual extension to further medicine groups and processes after results are accepted.
Project in progress
Planned project period through 29 February 2028
Pharma Flow is being developed under project FENG.01.01-IP.02-0638/25: “Development of the Pharma Flow intelligent agent using artificial intelligence and federated machine learning to optimise medication management and take over routine, time-consuming tasks performed by hospital pharmacy staff”.
The planned project period is 1 March 2026 – 29 February 2028. Implementation of the results is planned for the following six months: 1 March – 31 August 2028. At this stage, Pharma Flow is not a commercially ready product. The described capabilities are project assumptions that will undergo research, testing and validation.
Collaboration with hospitals
Partner hospitals may support process analysis, data-quality assessment, use-case consultation, model validation, prototype testing, usability assessment and pilots. Organisations interested in the project can already discuss future deployment scenarios during the R&D stage.
The future of medication management
Which pharmacy process still requires manual analysis of multiple reports, emails and telephone calls?
Let us discuss the processes that Pharma Flow is intended to improve and the scope of a possible pilot or research collaboration.