SCNET · Enterprise IT · Ankara, Türkiye

Sanal Çekirdek

When a clinical system stops, a patient waits.

Health and life sciences technology services cover clinical and operational systems that must provide security and availability at the same time. Sanal Çekirdek treats clinical applications, devices, laboratories, imaging, scheduling, and support as interdependent flows, positioning AI outputs as verifiable support rather than a replacement for professional judgment.

Critical application and service continuity

Clinical and operational systems are classified by patient and service impact, with identity, network, data, device and third-party dependencies mapped. Recovery sequence is based on the minimum safe service level.

  • HIS/EMR, PACS, LIS and scheduling flows
  • Backup, DR and cyber recovery
  • Alternative communications and manual process
  • Technical and business acceptance testing

Sensitive data and secure integration

Health and personal data is governed by class, purpose and permission. Inter-system flows include minimization, identity, encryption, logging and failure handling.

  • Data inventory and ownership
  • API, messaging and file integration
  • Role- and purpose-based access
  • Logging, retention and secure disposal

Governed clinical and operational AI

RAG and analytics systems are designed around verified sources, professional approval, performance evaluation and defined use boundaries. Clinical decision support requires specific risk assessment according to task and error impact.

  • Source-grounded clinical and operational retrieval
  • Professional approval and alert design
  • Model validation, drift and versioning
  • Private deployment options for patient data

User experience and capacity

Application, sign-in, device and network experience for clinical and operational users is measured end to end. Infrastructure and applications are planned together for peaks, imaging volume and remote services.

  • Identity and sign-in experience
  • Clinical device and network segmentation
  • Application performance
  • Capacity and demand visibility

How we work

  1. Map dependencies across clinical and administrative systems
  2. Establish where patient data originates and where it flows
  3. Design continuity, authorization and recovery order
  4. Pilot in one department without disturbing clinical flow
  5. Extend to other departments as patient safety is evidenced

How success is measured

  • Critical health-service availability
  • Access, data and integration errors
  • AI validation, professional correction and alert burden
  • Recovery testing and minimum-service success

Frequently asked questions

Can AI make clinical decisions?

That role should not be assigned without assessing the use case, regulation, performance, explainability and professional oversight. Sanal Çekirdek builds the technical system; clinical validity and medical decisions remain with qualified professionals.

Can health data be held in a private cloud?

Yes. Data class, location, access, keys, backup and operating responsibility must be designed together. Private cloud alone does not establish legal compliance.

How should clinical devices connect to the network?

Network zones, permitted flows and passive visibility should reflect device class, vendor support, communication needs and patient or service impact.

Design critical clinical flows, sensitive data and professional oversight together.

Assess your health-data use case