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Security

Security & Data Ethics

Carelog is GDPR-compliant and D-Mark certified. Learn about our security measures for protecting patient data in Denmark.

D-mærket - digital tryghed

D-mærket

The Danish label for digital responsibility

D-mærket (the D-seal) is Denmark's labelling scheme for IT security and responsible data use. It is awarded to companies that, based on an annual audit, meet D-mærket's requirements for IT security and responsible data use.

See our case with D-mærket

ISAE 3000

ISAE 3000

Independent auditor's assurance

Our data protection controls and GDPR compliance are verified through an independent ISAE 3000 assurance report - third-party documentation that our processes deliver what we promise.

How we protect our users

Our system integrates a range of security measures and protocols to ensure that personal information and health data are handled with the highest level of security.

01

Local storage on your device

All your notes and client information are stored exclusively locally on your own device and encrypted with strong, industry-standard encryption (OS-native encryption on desktop and AES-256-GCM in the browser). Carelog has no access to your data, and no content is stored permanently on our servers. You retain full control of your data at all times.

02

Real-time transcription

Audio from the consultation is transcribed in real-time using a GDPR-compliant speech-to-text API. For real-time speech-to-text processing, chunks of audio are continuously processed in short-term memory, and no audio data is stored. Therefore, there is never, at any point, a complete audio recording of the consultation. Only individual chunks of audio are processed and then immediately discarded. All data processed by Carelog is encrypted for protection.

Based on guidelines from the Danish Council on Ethics

9 Key Findings from Project

1

Finding 1: Data ethics is already part of practice

Ethical considerations are a cornerstone of healthcare professionals' work and influence many decisions - including when it comes to the use of AI.

2

Finding 2: AI is implemented based on many different motives

Efficiency is far from the only driving force. Motivations range from professional curiosity and innovation through organizational modernization to the desire to improve health for patients and citizens.

3

Finding 3: AI provides the most value when linked to a clear need

Complaints procedure

Do you want to complain or object to our processing of personal data or our IT security? See our complaints procedure

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03

Automatic data deletion

Only you have access to your transcriptions and anonymized notes through your two-factor protected user interface. This allows you to review and finalize the notes before transferring them to your medical records system. All transcriptions and notes are automatically and permanently deleted after 5 days. Before this, you also have the option to delete them manually.

04

Your data

Neither Carelog nor third parties use your data to train models or for any other purposes. All information processed is only accessible to you. In other words, no employees at Carelog or other involved data processors have access. To maintain the highest level of security, all data processing takes place within the EU in accordance with the European Union's General Data Protection Regulation (GDPR). Temporary storage of data occurs on dedicated and private Microsoft Azure servers in Sweden.

05

Transparency about security

We prioritize transparency in our security practices. Should a security incident occur that affects your data, we will inform you promptly in accordance with GDPR guidelines. Additionally, we regularly share updates here about new security measures we implement to ensure that our services meet the highest standards.

AI has great potential when used to solve a specific problem. However, in some cases, implementation occurs before the need is clearly defined.

4

Finding 4: Data and model selection is crucial

The development of meaningful AI solutions heavily depends on the choice of data and models.

5

Finding 5: It is complex and resource-intensive to create responsible AI

It is often a major task to navigate the requirements for development, approval, labeling, and documentation.

6

Finding 6: Consent can be difficult in practice

When AI is integrated into healthcare workflows, it can be difficult to obtain individually informed consent for the use of the technology.

7

Finding 7: Uncertainty and errors are central themes in working with AI

There is significant attention to how errors and uncertainty are handled in AI systems.

8

Finding 8: The desire for scaling

Several actors want their AI solutions to be scalable and create value broadly in the health and welfare sector.

9

Finding 9: There is a need for interdisciplinary collaboration

Interdisciplinary collaboration is necessary to succeed in developing successful and robust AI solutions.