A smart hospital model is not simply a diagram showing HIS, EMR, LIS, PACS, and a few applications placed next to one another. For these systems to truly function as a unified hospital ecosystem, the facility needs a multilayer architecture, beginning with technical infrastructure, followed by connected medical devices, clinical applications, integration and data platforms, and then analytics, operations management, and AI. A reference model can be visualized in six layers:
1. Technical infrastructure and connectivity.
2. Medical devices, sensors, and IoMT.
3. Clinical and operational applications.
4. Integration, identity, and data.
5. Analytics, operations management, and AI.
6. Governance, security, and business continuity.
These layers do not operate independently. A hospital may have advanced software but still experience disruption if its network is unstable. Connected devices without an appropriate integration layer may still require staff to enter data manually. A large data repository without proper data governance may also struggle to provide reliable information for management. WHO emphasizes that digital health initiatives should be supported by strong governance while relying on standards and architectures that enable different components to integrate with one another.
How Is a Smart Hospital Model Different from a List of Software Systems?
A software list may show that a hospital is using:
- HIS.
- EMR.
- LIS.
- RIS/PACS.
- Pharmacy software.
- Equipment management software.
- ERP.
- Patient applications.
However, that list does not explain:
- How the systems share a common patient identifier.
- Where medical devices connect to the architecture.
- Which interfaces are used to transmit data.
- Which system is the authoritative source for each information domain.
- How workflows continue when an application becomes unavailable.
- Where dashboards obtain their data.
- Which vendor is responsible when an integration workflow fails.
A smart hospital architecture should answer these questions by defining the roles, relationships, and responsibility boundaries between each layer. WHO also notes that a robust digital health strategy needs to integrate financial, organizational, human, and technological resources. The six-layer model below is a reference architecture intended to support planning and technical discussions; it is not an official set of legal criteria for hospitals in Vietnam.
Overview of the Six Layers of a Smart Hospital Model
| Layer | Primary Role | Typical Components |
|---|---|---|
| 1. Technical infrastructure | Provides the foundation for the entire system to operate | Power, UPS, network, servers, storage, data center |
| 2. Devices and IoMT | Captures data and performs functions at the point of care | Monitors, laboratory analyzers, imaging equipment, sensors, gateways |
| 3. Applications | Supports clinical work, administration, and interaction | HIS, EMR, LIS, RIS/PACS, pharmacy, ERP, patient portal |
| 4. Integration and data | Connects systems, manages identity, standardizes and organizes data | Integration engine, API gateway, identity management, shared master data, data platform/repository |
| 5. Analytics and operations management | Uses data to support decisions and appropriate automation | BI, dashboards, alerts, operational analytics, AI |
| 6. Governance and operations | Controls the lifecycle and safety of the entire architecture | Security, access control, backup, downtime procedures, change management |
An important point is that higher layers generally depend on the maturity of the layers below them. If source data are inaccurate, AI may struggle to generate reliable recommendations. If patient identity is inconsistent, even an attractive dashboard may produce incorrect summaries. If networks and servers experience frequent interruptions, digital workflows may be difficult to maintain reliably. The governance and operations layer, however, should not be understood as the technically “top” layer. In practice, this layer should span the entire architecture, from infrastructure to data and applications.
Layer 1: Technical Infrastructure and Connectivity
A smart hospital begins with components that patients rarely see. Infrastructure may include:
- Power supply.
- UPS.
- Backup generators.
- LAN.
- Wi-Fi.
- Fiber-optic connectivity.
- Switches.
- Firewall systems.
- Servers.
- Virtualization platforms where applicable.
- Storage systems.
- Backup systems.
- Data centers.
- Cooling systems.
- Server rooms.
- Cloud or hybrid infrastructure where appropriate.
At the hospital level, the network does not serve office computers alone. The same infrastructure may need to support:
- Physician workstations.
- Nursing devices.
- Laboratory analyzers.
- Diagnostic imaging equipment.
- Patient monitors.
- Kiosks.
- Mobile devices.
- Cameras.
- IP phones.
- Location-tracking devices.
- Environmental sensors.
Network design should therefore consider traffic volumes, priority levels, security, and the operational requirements of each group.
Infrastructure Should Account for Data Growth
Hospital data do not grow at the same rate across all systems. An administrative record is significantly smaller than:
- CT images.
- MRI images.
- Endoscopy video.
- Continuous monitor data.
- Specialty image files.
- Molecular biology data where applicable.
Hospitals should therefore forecast:
- Current storage capacity.
- Data growth rate.
- Required retention period.
- Which data require rapid access.
- Which data can be archived for long-term storage.
- Redundancy requirements.
- Recovery time after an incident.
Storage capacity should not be determined solely by utilization at the time of system acceptance.
Smart Infrastructure Does Not Mean the Most Complex Infrastructure
An appropriate system should meet actual needs and remain maintainable over the long term. Facilities should ask:
- Can the internal team operate it effectively?
- Which areas require vendor support?
- Is there an alternative if a component fails?
- Does the architecture depend on proprietary technology?
- Can it be expanded incrementally?
- Is infrastructure centrally monitored?
An architecture that is excessively complex relative to the hospital's resources may increase costs and extend incident resolution times.
Layer 2: Medical Devices, Sensors, and IoMT
The second layer brings the hospital's physical environment into the digital ecosystem. Devices and technologies capable of generating data may include:
- Laboratory analyzers.
- CT.
- MRI.
- X-ray.
- Ultrasound.
- Patient monitors.
- Ventilators.
- Infusion pumps.
- Syringe pumps.
- Operating room equipment.
- Certain rehabilitation devices.
- Temperature sensors.
- Environmental sensors.
- Asset location systems.
When medical devices and related technologies are connected to exchange data with other systems, they may become part of an Internet of Medical Things (IoMT) ecosystem. However, not every sensor or IoT device used in a hospital is automatically a medical device. For any connected device, the hospital should clarify:
- What data does the device send?
- What data does the device receive?
- Which protocols are supported?
- Is a gateway required?
- Is a separate connectivity license required?
- How is the device authenticated?
- Who manages technical accounts?
- How are software versions updated?
- Can the device continue performing its primary function when the network is unavailable?
Not All Device Data Need to Be Sent to the EMR
A patient monitor may generate numerous values over time, while a maintenance system may be more concerned with:
- Operating hours.
- Battery level.
- Error codes.
- Firmware version.
- Connectivity status.
These data groups serve different purposes. Hospitals should distinguish between: Clinical data → used for patient care or patient records. Technical data → used for equipment management, maintenance, and operations. Environmental data → used to monitor facility conditions. Data should not all be sent to the same system merely because it is technically possible.
Layer 3: Clinical and Operational Applications
This is the layer users interact with most frequently. It can be divided into three major groups.
Clinical Applications
Examples include:
- HIS.
- Electronic medical records.
- LIS.
- RIS/PACS.
- Pharmacy software.
- Electronic nursing systems.
- Operating room management.
- ICU systems.
- Specialty-specific software.
The role of this layer is to support specific workflows within each department. The architectural objective is not to require a single system to perform every function. More importantly, the hospital should determine:
- Which system is primarily responsible for each type of data?
- How do other systems access that data?
- Does information need to be re-entered?
- When information changes, which system has authority to update it?
Administrative Applications
These may include:
- Financial management.
- Human resources.
- Inventory.
- Procurement.
- Asset management.
- Equipment management.
- Bed management.
- Contract management.
- Quality management.
These applications do not directly produce diagnoses or treatments, but they significantly affect hospital operations. For example, equipment management software may provide data on:
- Equipment currently in operation.
- Equipment undergoing maintenance.
- Location.
- Costs.
- Spare part status.
This information can support procurement or asset allocation decisions.
Patient Engagement Applications
These may include:
- Websites.
- Mobile applications.
- Patient portals.
- Appointment booking.
- Kiosks.
- Digital payment.
- Result lookup.
- Telehealth.
- Follow-up reminders.
The key question is whether these channels are genuinely connected with the core workflow. An appointment application creates value only when:
- Available schedules are accurate.
- The central system receives the appointment.
- Changes are synchronized.
- Patients do not need to complete the entire registration process again when they arrive at the hospital.
Layer 4: Integration, Identity, and Data
This layer determines whether software systems and devices can operate as one ecosystem or remain isolated systems. WHO promotes the use of standards for interoperability and data sharing; SMART Guidelines also support standards-based systems capable of exchanging data accurately and reducing fragmentation. This layer may include:
- Integration engine.
- API gateway.
- Message broker where appropriate.
- Identity management systems.
- Shared master data.
- Master data management.
- Data platforms.
- Data repositories.
- Device integration platforms.
- Log and transaction management tools.
Identity Is the Foundation of Integration
Systems need to standardize or map:
- Patient IDs.
- Encounter IDs.
- Specimen IDs.
- Service codes.
- Medication codes.
- Device IDs.
- Department codes.
- Bed IDs.
- Staff IDs.
If the same patient is assigned multiple identifiers without an effective linkage mechanism, data may remain fragmented even when the systems are technically connected.
Shared Master Data Are as Important as Communication Protocols
Two software systems may exchange data but still interpret it differently if:
- Service names differ.
- Units of measurement differ.
- Medication codes differ.
- Status definitions differ.
- Department master data are inconsistent.
Hospitals should therefore determine:
- Who manages master data?
- Who is allowed to create codes?
- Which system is the authoritative source?
- When a code is retired, how are other systems updated?
“Having an API” Does Not Mean an Integration Architecture Is Already in Place
When evaluating a solution, hospitals should ask more detailed questions:
- What functions does the API support?
- Which data can be read?
- Which data can be written?
- Are there access limitations?
- Is a test environment available?
- How is authentication handled?
- Are transaction logs available?
- How are API versions managed?
- When the API changes, how are dependent systems notified?
Integration is a capability that must be maintained throughout the lifecycle, not a one-time technical task completed on acceptance day.
The Data Warehouse Belongs to the Data Layer, Not the Analytics Layer
A data warehouse or data platform may be responsible for:
- Consolidating data from multiple sources.
- Organizing data.
- Storage.
- Standardization.
- Supporting retrieval.
- Providing a foundation for analytics layers above it.
For this reason, in this model, the data warehouse is placed in Layer 4. Layer 5 uses the organized data from this layer to perform analytics, dashboards, alerts, or AI.
Layer 5: Analytics, Operations Management, and AI
On top of data that have been integrated and governed in the layers below, hospitals can deploy capabilities such as:
- Business Intelligence.
- Dashboards.
- Management reports.
- Alert systems.
- Operational analytics.
- Forecasting tools.
- AI applications where suitable use cases and data are available.
Dashboards Should Answer Management Questions
For example:
- Which department is experiencing increasing waiting times?
- Which beds are expected to become available?
- Which devices are currently unavailable?
- Which equipment has low utilization?
- Which laboratory tests are experiencing longer turnaround times?
- Which inventory area is at risk of running short?
- Which operating rooms are behind schedule?
The starting question should not be: “How many charts should the dashboard have?” Instead, it should be: “What decisions does management need to make?”
AI Is an Analytical Capability, Not the Foundation
AI may support:
- Demand forecasting.
- Image analysis.
- Work prioritization.
- Anomaly detection.
- Predictive maintenance.
- Operational support.
- Text analysis.
- Decision support.
However, AI depends on:
- Data.
- Identity.
- Integration.
- Infrastructure.
- Processes.
- Governance.
WHO in 2026 also emphasizes three shared foundations of digital health and AI: trusted data, interoperable systems, and strong governance. In this six-layer model, AI is placed in the analytics layer to reflect its dependence on data, integration, and underlying infrastructure. In practice, however, AI functionality does not necessarily exist as an independent platform. AI may also be embedded directly within:
- Clinical software.
- Medical devices.
- PACS.
- Equipment management systems.
- Decision-support workflows.
Its position in the model describes its architectural role, not a fixed physical location for a product.
Layer 6: Governance, Security, and Business Continuity
This layer should span the entire architecture. It may include:
- Policies.
- Access control.
- Account management.
- Information security.
- Change management.
- Version management.
- Testing.
- Backups.
- Disaster recovery.
- Downtime procedures.
- Vendor management.
- Contract management.
- Training.
- Data governance.
- Remote access control.
WHO considers governance an important foundation for digital health initiatives to integrate, scale, and be implemented safely.
The Architecture Needs Clear Ownership
Problems may arise when:
- HIS is managed by the IT department.
- LIS is managed through collaboration between the laboratory and its vendor.
- PACS is managed by the diagnostic imaging department.
- Devices are managed by the technical department.
- ERP is implemented by finance.
Without a common architecture governance layer, each project may make decisions independently. Hospitals should define:
- Who approves the architecture?
- Who manages integration standards?
- Who governs master data?
- Who approves new connections?
- Who is accountable for each data domain?
- Who resolves conflicts between vendors?
The Architecture Must Account for the Day Systems Fail
A good design does not describe only normal operation. Hospitals should prepare for situations where:
- The network becomes unavailable.
- The primary server fails.
- PACS becomes unavailable.
- LIS loses connectivity with analyzers.
- Devices send duplicate data.
- An API becomes unavailable.
- A cloud system cannot be accessed.
- A vendor updates software.
- Data need to be recovered.
Each situation should define:
- Temporary workflow.
- Responsible personnel.
- How data are recorded.
- How data are synchronized after recovery.
- Criteria for returning to normal operations.
How Does Data Move Through the Six Layers?
A diagnostic imaging order can be used as an example.
Step 1: The User Creates an Order
A physician creates an order in a clinical system. Participating layer: applications.
Step 2: The Data Are Integrated
The order is sent to RIS through an integration mechanism. The information needs to be associated with the correct:
- Patient.
- Encounter.
- Service.
- Time.
- Department.
- Ordering physician.
Participating layer: integration and data.
Step 3: The Device Receives the Worklist
The CT or MRI system receives the appropriate information so that the technologist can select the correct patient and procedure. Participating layer: devices and IoMT.
Step 4: Images Are Generated and Stored
The device generates images and sends the data to PACS. This process depends on:
- Network.
- Servers.
- Storage systems.
Participating layers: devices + infrastructure + applications.
Step 5: The Physician Reviews and Reports the Result
The report is validated and returned to the patient record. Participating layers: applications + integration.
Step 6: Data Are Used for Operations Management
The hospital may analyze:
- Number of imaging procedures.
- Waiting time.
- Report turnaround time.
- Equipment capacity.
Participating layers: data + analytics and operations management.
Step 7: The Entire Process Is Governed
The hospital controls:
- Accounts.
- Access rights.
- Logs.
- Backups.
- Security.
- Recovery.
Participating layer: governance and operations. A process that appears relatively simple from the patient's perspective may simultaneously pass through nearly the entire architecture.
Should a Hospital Use One Main Platform or Multiple Specialized Systems?
This is a decision about the strategy for selecting and organizing application systems and is not completely equivalent to centralized or distributed architecture in the technical sense. Three common approaches may be considered.
Single Main Platform or Suite Model
One platform or a suite from a single vendor handles most core functions. Potential advantages:
- Fewer integration interfaces.
- Simpler administration.
- Potentially more consistent user experience.
Points to assess:
- Ability to change vendors.
- Rights to export and use data.
- Capability of specialized modules.
- Risk of dependence on a proprietary ecosystem.
Multiple Specialized Systems Model
The hospital selects specialized solutions for individual areas and connects them together. Potential advantages:
- Greater ability to select solutions that fit specific specialties.
- More flexibility when replacing one component.
Points to assess:
- Integration complexity.
- Master data and identity governance.
- Number of accounts and interfaces.
- Responsibility when cross-system failures occur.
Hybrid Model
Certain core platforms are standardized, while specialized systems connect through an integration layer. This model works effectively only when the hospital can control:
- Architecture.
- Interfaces.
- Data.
- Ownership.
- Responsibilities of each vendor.
The model should not be selected solely based on the number of vendors. The more important question is whether the hospital has sufficient capability to govern that architecture over the long term.
Five Principles for Building a Smart Hospital Model
1. Workflow Before Product
Do not begin with: “The hospital needs to buy AI, IoT, and a data lake.” Begin instead with: “Which workflows have problems, and how should data move through those workflows?”
2. Each Data Domain Needs an Authoritative Source
For each data domain, the hospital should identify which system is the authoritative source responsible for creating and updating information, while also defining how other systems receive or synchronize that data. For example:
- Which system manages staff data?
- Where does the medication master list come from?
- Which system maintains device identifiers?
- Who updates the department master list?
- Where is patient information created and consolidated?
This does not mean that data can exist in only one database. The important point is to define the authoritative source so that multiple systems do not modify the same information according to different rules.
3. Integration Requirements Should Be Included in Procurement
The following should be clarified from the beginning:
- Protocols.
- APIs.
- Licensing.
- Gateways.
- Integration responsibilities.
- Documentation.
- Test environment.
- Acceptance criteria.
Hospitals should not purchase equipment or software first and determine integration capability afterward.
4. Do Not Build Higher Layers Before Lower Layers Are Sufficiently Ready
For example:
- No standardized device IDs → difficult to analyze utilization.
- Master data not standardized → dashboards may be unreliable.
- No historical data → AI has limited ability to forecast.
- Unstable network → IoMT may be difficult to operate reliably.
This does not mean hospitals must fully complete one layer before beginning the next. However, important dependencies should be clearly identified to avoid building additional systems on top of unstable data foundations.
5. Architecture Must Be Adaptable
Hospitals typically have much longer lifecycles than individual software systems. During operation, a facility may:
- Replace HIS.
- Upgrade PACS.
- Replace laboratory analyzers.
- Add vendors.
- Change storage models.
- Deploy new AI applications.
The architecture should allow individual components to change without requiring the entire system to be rebuilt.
Roadmap for Building a Smart Hospital Model
Stage 1: Map the Current Architecture
List:
- Infrastructure.
- Software.
- Connected devices.
- Data sources.
- Interfaces.
- Vendors.
- System owners.
Then map major data flows and workflows.
Stage 2: Identify Breakpoints
Look for areas where:
- Data must be re-entered.
- Excel is used as an intermediary.
- Files are transferred manually.
- Codes are inconsistent.
- Logs are unavailable.
- No fallback mechanism exists.
- Vendor responsibilities are unclear.
Stage 3: Develop the Target Architecture
Determine:
- Which platforms will be retained.
- Which systems need to be replaced.
- Which integration layer is required.
- Authoritative data sources.
- Storage architecture.
- Device connectivity approach.
- Security requirements.
Stage 4: Prioritize by Value
Not every layer needs to be implemented simultaneously. Priorities may include:
- Patient identity.
- Laboratory workflows.
- Diagnostic imaging.
- Electronic medical records.
- Medication management.
- Bedside devices.
- Data platforms.
The sequence depends on the actual needs of each hospital.
Stage 5: Test Integration
Do not test each software system in isolation. End-to-end testing should include scenarios such as:
- Patients with identical names.
- Canceled orders.
- Information corrections.
- Department transfers.
- Network outages.
- Devices sending duplicate data.
- Server failure.
- Data recovery.
Stage 6: Measure Effectiveness
Possible indicators include:
- Data re-entry rate.
- Waiting time.
- Integration error rate.
- Downtime.
- Turnaround time.
- System utilization.
- Rate of manual work.
- User feedback.
Common Mistakes When Building a Smart Hospital Model
Designing Architecture Around Vendor Names
The architecture should describe roles and functions first and products second. If the diagram consists mainly of vendor logos, the hospital may find it difficult to evaluate its ability to change components in the future.
Each Project Creates Its Own Database
This may create multiple inconsistently governed copies of:
- Patients.
- Staff.
- Services.
- Devices.
- Departments.
Too Many Point-to-Point Connections
When each system connects directly to many others, the number of interfaces can grow rapidly and become difficult to manage. An intermediate integration layer may be useful when appropriate for the facility's scale and needs.
Building Only the Technology Architecture
A diagram showing all servers and software systems is still incomplete if it lacks:
- Data owners.
- Governance processes.
- Policies.
- Operational procedures.
- Vendor responsibilities.
Starting with AI
AI is a capability for using data. If data and integration are not stable, implementing AI first may simply create another isolated system that needs to be maintained.
Failing to Account for Integration Costs
The price of software or equipment may not include:
- APIs.
- Licensing.
- Gateways.
- Middleware.
- Customization.
- Testing.
- Interface maintenance.
These costs should be clarified during the procurement stage.
Smart Hospital Architecture Review Checklist
Infrastructure
- An up-to-date network diagram is available.
- Backup power is available.
- A backup system is in place.
- Recovery has been tested.
- Storage capacity has an expansion plan.
- Devices and systems are appropriately monitored.
Devices
- The list of connected devices is defined.
- Interface documentation is available.
- Data scope is clearly defined.
- A procedure exists for connectivity loss.
- Software versions are managed.
Applications
- The role of each system is clear.
- Unnecessary functional duplication is minimized.
- The authoritative source for each data domain is defined.
- Data can be exported.
Integration
- An interface strategy is available.
- A test environment is available.
- Error logs are available.
- Version management is in place.
- Responsible personnel are defined.
Data
- Identity is managed consistently.
- Shared master data are available.
- Data ownership and responsibilities are clear.
- Data quality is monitored.
- A retention policy is available.
Analytics
- Dashboards are linked to specific decisions.
- Indicators have defined meanings.
- Data sources can be traced.
- AI has a clearly defined use case.
Governance
- Access rights are clearly defined.
- Change management is in place.
- A downtime plan is available.
- A recovery plan is available.
- Vendor responsibilities are specific.
- A mechanism exists for periodic architecture review.
Frequently Asked Questions About Smart Hospital Models
What Layers Make Up a Smart Hospital?
There are different ways to model the architecture. One reference framework may include technical infrastructure, devices and IoMT, applications, integration and data, analytics–operations management–AI, together with a governance and operations layer spanning the entire system.
Is HIS the Center of a Smart Hospital?
HIS can be an important platform, but it does not necessarily need to contain every function. Its specific role depends on each hospital's architecture.
Must a Hospital Use One Vendor for the Entire System?
No. Both a main-suite model and a multi-vendor specialized systems model have advantages and disadvantages. What matters is interoperability, data governance, system control, and long-term operational capability.
How Is IoMT Different from General IoT?
IoMT generally refers to the ecosystem of medical devices and connected technologies used in healthcare environments. Not every IoT device used in a hospital is a medical device.
Should a Hospital Build a Data Warehouse Before Completing Integration?
Implementation can occur in phases, but the value and quality of a data warehouse depend heavily on the ability to integrate, identify, and standardize data from source systems.
Where Does AI Sit Within a Smart Hospital Architecture?
In this framework, AI is placed in the analytics and operations management layer to reflect its dependence on data and integration. In practice, AI may also be embedded directly within specific software or medical devices.
Is There a Standard Smart Hospital Architecture That Applies to Every Hospital?
This article is provided for informational purposes only. The six-layer model is intended to support architectural thinking and planning and does not replace the criteria, standards, or legal requirements applicable to each facility.
Compare Each Smart Hospital Layer with Real-World Solutions at PHARMEDI & HEALTHCARE VIETNAM 2026
An architecture diagram becomes truly useful when hospitals can compare each layer with technologies, devices, and solutions that can be implemented in practice. From connected medical devices to data platforms, digital health, and smart healthcare, every component should be considered in relation to the overall system.
At PHARMEDI & HEALTHCARE VIETNAM 2026, the Product Showcase Area brings together multiple product and solution categories across the healthcare sector. Official event information covers areas ranging from medical devices, diagnostic imaging, and laboratory technologies to digital health and smart healthcare technologies.
For hospitals developing or reviewing their target architecture, visiting PHARMEDI VIETNAM 2026 can provide an opportunity to evaluate solutions by architectural layer rather than reviewing individual products in isolation:
- How can the device connect to the hospital's current architecture?
- What additional gateways, middleware, or integration licenses are required?
- Does the system allow data to be exported and exchanged with other platforms?
- Which components need to be implemented together to form a complete workflow?
- If one system loses connectivity, which functions can continue operating?
- Does the solution depend on proprietary data formats or a vendor-specific ecosystem?
- How can additional devices, departments, or modules be added in the future?
- How far does the vendor's responsibility extend for integration, testing, and post-implementation support?
Official PHARMEDI information also shows that the event spans an ecosystem ranging from medical devices, diagnostic imaging, and laboratory technologies to digital health and smart healthcare technologies. This diversity gives healthcare facilities an opportunity to observe the relationships between different parts of the architecture instead of searching only for an individual software platform or device.
From Technology Architecture to Investment and Operational Strategy
The hospital architecture perspective is further expanded through the International Conference Series on Investment and Innovation in Healthcare, taking place over two days, September 22–23, 2026.
On Day 1 – September 22, the program focuses on Vietnam's Healthcare Sector – The Next Decade of Growth, with discussions covering healthcare demand, investment trends, hospital supply procurement and investment decisions, and applications of AI in healthcare.
On Day 2 – September 23, the focus shifts to Hospital Management & Operations, including the future hospital model built around smart, sustainable, and patient-centered care; hospital operations and accreditation; clinical innovation; growth strategies; and the challenge of balancing quality and access with financial efficiency.
For a smart hospital project, these two groups of topics connect directly with technology architecture: Day 1 addresses where hospitals should invest and which technologies can create value, while Day 2 expands the discussion to how those systems should support long-term hospital operating models.
B2B Matchmaking – Exploring Integration Requirements with Individual Partners
Once a hospital has defined its target architecture and identified the gaps that need to be addressed, B2B Matchmaking at PHARMEDI provides additional opportunities for one-to-one connections between professional visitors and companies based on needs and potential fit.
For hospitals, discussions can go deeper into architectural questions such as: which layer the system belongs to, which platforms it exchanges data with, which gateways or middleware are required, how data access rights are managed, which party is responsible for integration, and whether individual components can be expanded or replaced in the future.
In this way, PHARMEDI & HEALTHCARE VIETNAM 2026 can help hospitals move from asking:
“Which system should we buy?”
to the more important question:
“Where will this solution sit within the architecture, what will it connect to, and how will it be operated over the long term?”
Dates: September 22–24, 2026
Venue: Saigon Exhibition and Convention Center (SECC), Ho Chi Minh City
Exhibitor Registration:https://pharmed.vn/exhibitor/application-form
Visitor Registration:https://ers-vn.informa-info.com/pha2026?cid=FB
Contact:
Booth Booking: [email protected] (Thanh Thuý)
Media Support: [email protected] (Anita Pham)
Visitor Support: [email protected] (Ciel Le)
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