Storing healthcare data securely: PACS archives, patient data, and local AI

Healthcare is digitizing at a high pace. Hospitals, clinics, and healthcare institutions are working with an increasing number of digital records, medical images, measurement data, research data, and AI applications. This offers opportunities for better diagnostics, more efficient processes, and improved collaboration between healthcare providers.

But healthcare also faces a unique challenge. Virtually all data is sensitive. A medical record, scan, treatment history, or research file directly concerns people. It concerns their health, their identity, and often the most vulnerable moments in their lives.

That is why digital innovation in healthcare is not just about speed or cost savings. It is primarily about trust. Patients must be able to count on their data being processed securely. Healthcare providers must have access to the right information at the right time. And organizations must be able to demonstrate that data is stored carefully, securely, and compliantly.

The growing pressure on healthcare data

Healthcare institutions produce enormous amounts of data daily. Think of electronic patient records, laboratory results, surgical reports, monitoring data, and research files. A large part of this must remain available for years, sometimes even for a lifetime.

One of the biggest drivers of data growth is medical imaging. PACS archives contain X-rays, MRI scans, CT scans, ultrasounds, and other imaging studies. These files are often large, must remain quickly accessible, and have high medical and legal value.

PACS archives in particular clearly show where the challenge lies. Once stored, this data usually no longer changes, but it must remain reliable, intact, and available. A doctor must be able to retrieve a scan years later and trust that the image has not been altered. At the same time, the number of examinations is rising, images are becoming more detailed, and retention periods are increasing.

This makes healthcare data not just a medical issue, but also a storage issue.

AI in healthcare is growing fast

AI is playing an increasingly large role in healthcare. Think of support in diagnostics, image analysis, triage, capacity planning, administrative processes, and medical research. Especially with large datasets, such as medical imaging, AI can recognize patterns that are difficult or time-consuming for humans to analyze manually.

Sensitive patient data requires control

The use of AI in healthcare starts with data. Without good data, there is no reliable AI. But in healthcare specifically, this data is extremely sensitive. Patient data, medical images, and research data cannot simply be processed in external systems or public cloud models.

That is why the location and control over data are so important. Where is the data located? Who has access? Is the data used to train external models? How is logging managed? And can the healthcare institution demonstrate that all processing complies with privacy legislation and internal guidelines?

This is particularly relevant for medical imaging. PACS archives contain large amounts of patient-related image data. These images are interesting for AI analysis but should not be moved or processed without clear safeguards. A scan is not an arbitrary data file. It is medical information with a direct impact on diagnosis, treatment, and liability.

Collaboration without giving up control of data

Medical research benefits from large datasets. The more data available, the better AI models can recognize patterns. However, international and inter-organizational collaboration often stalls due to privacy, data sharing, and compliance.

Federated models can play an important role here. In this approach, data remains local at the healthcare institution, while algorithms can learn from patterns across multiple sources. The data is therefore not collected centrally, but the knowledge from that data can be shared.

This is especially relevant for hospitals, research institutions, and collaborations surrounding rare diseases. One institution often has too little data to train strong models. Collaboration is necessary, but it must not mean that sensitive patient data is moved uncontrollably.

From storing to maintaining availability

In healthcare, storage is only valuable if data remains available when needed. A backup that cannot be restored quickly is of little help during an incident. An archive that is not easily searchable slows down healthcare processes. And a PACS environment that is not scalable will sooner or later become a brake on innovation.

Therefore, healthcare institutions must take a critical look at their data foundation. Which data must be immediately available? Which data can be moved to an archive layer? How are medical images protected against ransomware? How do you prevent growing PACS archives from unnecessarily burdening the primary infrastructure? And how do you ensure that data remains securely available for future AI applications?

These questions are becoming more important now that healthcare institutions are dealing with rising costs, staff shortages, stricter privacy requirements, and increasing cyber threats.

How Comex can help

Comex helps healthcare institutions store critical healthcare data securely, sustainably, and controllably. We have specific experience with large archives and medical imaging, including PACS archives. For example, the market share of FAST LTA systems, especially Silent Cubes, in German hospitals is higher than 50%.

Our solutions are suitable for data that must remain reliably available for years, such as medical images, patient-related files, research data, and archives. We help healthcare institutions store this data securely, protect it against data loss, and keep it accessible without unnecessary dependence on external cloud platforms.

Comex can also provide expertise regarding local AI. Think of applications where sensitive patient data remains within the organization’s own environment, while healthcare institutions can still benefit from analysis and better accessibility. This creates a foundation on which innovation becomes possible without losing control over patient data.

For healthcare, that is the core: digitization and AI can deliver a lot, but only when the data remains secure, reliable, and under one’s own control.