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Which Certifications and Standards Are Relevant When Purchasing AI Patient Monitoring?

Stéphanie van Rosmalen ·
Medisch certificaat met officieel reliëfzegel op ziekenhuisnachtkastje, wand camera zichtbaar op de achtergrond, klinische sfeer.

When purchasing AI patient monitoring, ISO 27001, NEN 7510, and a CE marking are the most relevant certifications and standards. For healthcare organizations in the Netherlands, ISO 27001 and NEN 7510 are indispensable in practice, while a CE marking is mandatory as soon as the software is classified as a medical device. Which certifications apply exactly depends on the type of software, the setting, and the risk classification. In this article, we answer the most frequently asked questions about certifications, privacy, and clinical validation for AI monitoring systems.

Which certifications are legally required for AI healthcare software?

Which certifications are legally required for AI healthcare software depends on the risk class of the system. Software that makes diagnoses or supports clinical decisions falls under the Medical Device Regulation (MDR) and requires a CE marking. AI systems classified as high risk under the EU AI Act must also comply with additional transparency and documentation requirements.

For healthcare software that processes patient data, organizations in the Netherlands are additionally required to comply with the GDPR (General Data Protection Regulation). Although ISO 27001 and NEN 7510 are not formally statutory obligations, they are widely recognized by healthcare organizations and regulators as the standard for information security in healthcare. Many tenders and procurement processes set these certifications as a hard requirement.

In concrete terms, this means that an AI patient monitoring system must at minimum be able to demonstrate:

  • Compliance with the GDPR for the processing of personal data
  • A CE marking if the system qualifies as a medical device
  • Demonstrable information security via ISO 27001 and/or NEN 7510
  • Conformity with the EU AI Act for high-risk applications

What are ISO 27001 and NEN 7510 and why are they relevant?

ISO 27001 is an international standard for information security that describes how organizations set up and maintain an Information Security Management System (ISMS). NEN 7510 is the Dutch elaboration of this standard, specifically tailored to healthcare. Together, they form the standard for the secure handling of sensitive healthcare data in the Netherlands.

Both standards are particularly relevant for AI patient monitoring because such systems process large volumes of sensitive patient information. ISO 27001 ensures that the vendor has demonstrable processes for risk management, access control, and incident response. NEN 7510 adds healthcare-specific requirements, such as rules around the management of medical records and the continuity of care processes.

A vendor that holds both certifications demonstrates that information security is structurally embedded in the organization — not merely on paper. For healthcare organizations, this provides a concrete basis for comparing vendors and limiting liability risks.

How does an AI monitoring system protect patient privacy?

A well-designed AI monitoring system protects patient privacy by processing images in a fully automated way, without any person ever seeing them. The AI analyzes the situation and only sends an alert when an unsafe situation is detected. Care staff are therefore only called upon when it is truly necessary.

This principle, also known as privacy by design, is a core requirement under the GDPR. In practice, this means that raw camera images are not stored or forwarded to external servers. Processing takes place locally or in a secure environment, and only the relevant alert is shared with care staff.

Additional privacy safeguards that a reliable system should offer:

  • Encryption of all data during storage and transmission
  • Strict access control based on roles and authorizations
  • Automatic deletion of data after a defined retention period
  • Transparent data processing agreements in accordance with the GDPR

What is the difference between a CE marking and clinical validation?

A CE marking is a legally required certification mark that demonstrates that a medical device meets European safety and performance standards under the MDR. Clinical validation is an independent study that demonstrates that the system does in practice what it promises, with measurable and reliable results. Both are important, but they measure different things.

The CE marking says something about the development process and regulatory conformity. Clinical validation says something about actual performance in a care setting. A system can have a CE marking without its clinical effectiveness having been thoroughly investigated, and conversely, a system can perform well clinically but not yet be fully certified.

For healthcare organizations, it is advisable to request both. The CE marking provides legal certainty; clinical validation provides substantive certainty. Ask for publications, pilot results, or independent evaluations that demonstrate the system’s performance in comparable care settings.

What questions should a healthcare organization ask an AI vendor?

A healthcare organization must ask targeted questions about certifications, performance, and privacy when evaluating an AI monitoring system. The right questions help distinguish between vendors who demonstrably meet the requirements and vendors who merely claim to do so.

Be sure to ask the following questions:

  1. Which certifications do you hold? Ask specifically about ISO 27001, NEN 7510, and any CE marking, and request the most recent audit reports.
  2. How are patient images processed? Are images processed locally or sent to external servers? Who has access to them?
  3. What are the real-world performance results? Ask about the number of false alarms per period and about clinical validation studies or pilot results.
  4. How is the software updated and secured? Who is responsible for updates, and how are security issues reported and resolved?
  5. What data processing agreement is offered? Is it in line with the GDPR and who is the data controller?
  6. How does the implementation process work? Is the system plug-and-play or does it require extensive technical modifications?

The answers to these questions provide a reliable picture of how seriously a vendor takes security, privacy, and clinical reliability.

How we help with safe and certified AI patient monitoring

We understand that healthcare organizations are not only looking for a working system, but also the assurance that it meets all relevant standards and legislation. That is why we have built our AI monitoring software with certification and privacy as its foundation — not as an afterthought.

What we offer:

  • ISO 27001 and NEN 7510 certified, so that information security is demonstrably in order
  • Privacy by design: images are never viewed by people and care staff are only called upon when the software deems it necessary
  • Exceptionally low error rate: only one false alarm every 92 days, significantly reducing the workload on care staff
  • Robust patent portfolio of 21 patents that underscores the technological reliability
  • Simple implementation via a plug-and-play concept, without extensive technical modifications

Want to know how our solutions align with the specific requirements of your healthcare organization? Take a look at our website and discover how we put safe AI patient monitoring into practice.

Frequently Asked Questions

How long does it take for an AI monitoring system to become fully operational after implementation?

Implementation time varies by system and healthcare organization, but a plug-and-play solution can typically be up and running within days to weeks without extensive technical modifications. It is important to assess in advance which existing infrastructure — such as network, hardware, and integrations with EHR systems — is already in place. Ask the vendor for a concrete implementation plan including a timeline, testing phase, and training for care staff.

What happens if the AI generates a false alert — who is liable?

Liability for false alarms or missed detections is an important consideration that must be contractually established in advance. In most cases, ultimate responsibility for patient safety remains with the healthcare organization, but the vendor can be held liable if the system demonstrably fails to meet the agreed performance requirements or certification standards. Ensure that the data processing agreement and the service agreement contain clear provisions regarding performance guarantees, incident reporting, and liability limits.

Does patient consent need to be obtained for the use of AI monitoring?

This depends on how the system is configured and on which legal basis the data processing takes place. When images are processed in a fully automated manner and are not stored or viewed by people, the processing may in some cases be based on a legitimate interest or legal obligation within the framework of the care agreement. Nevertheless, transparency towards patients is always required under the GDPR: inform them via a privacy statement or admission conversation about which technology is being used and for what purpose.

How often must certifications such as ISO 27001 and NEN 7510 be renewed or audited?

ISO 27001 and NEN 7510 certifications typically have a validity period of three years, but require annual interim surveillance audits to maintain the certification. This means that a vendor must continue to actively invest in information security and not merely obtain a certificate once. Always ask a vendor for the date of the most recent audit and the accompanying report to confirm that the certification is current and valid.

What is the difference between a high-risk and low-risk AI system under the EU AI Act, and how do I know which category a monitoring system falls into?

Under the EU AI Act, an AI system is classified as high risk when it is deployed in critical sectors such as healthcare én has a significant impact on the safety or fundamental rights of people — such as systems that support clinical decisions or assess patient risks. AI monitoring systems that detect falls or unsafe situations typically fall into this category. The vendor is required to carry out a conformity assessment and provide transparent documentation; ask about this explicitly during the procurement process.

Can an AI monitoring system be integrated with existing care records or nurse call systems?

Many modern AI monitoring systems offer integration capabilities with existing nurse call systems, EHRs, or communication platforms via standard API connections or HL7/FHIR protocols. However, the degree of integration varies considerably by vendor and care environment. Ask in advance which systems are already supported, whether customization is possible, and who is responsible for the management and security of data exchange between systems.

How can a healthcare organization objectively evaluate the performance of an AI monitoring system before purchase?

The most reliable approach is to request independent clinical validation studies, pilot results from comparable care settings, and concrete performance figures such as the number of false alarms per unit of time and detection accuracy. Additionally, consider requesting a trial period or pilot within your own organization so that performance can be tested in the specific context of your care environment. Always compare results against the stated clinical goals and actively involve healthcare professionals in the evaluation.

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This content was generated with the help of AI and it may contain mistakes