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Medical Cloud Company

the guardian angel for my health



The main elements of MyCareAvatar

Icon Real-time data acquisition

Real-time data acquisition


Effective and validated hardware will be used to obtain realtime biometric, environmental and patient reported outcome data that will be processed and transformed in actionable information both for the patient and his doctor. To do so MedC2 will work together with partners in hardware development.

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Icon Individualised Patient Decision Aids

Individualised Patient Decision Aids


MyCareAvatar includes individualised Patient Decision Aids in the form of validated modules that patients follow at their own pace to get informed about their disease and treatment options in an individualised way. After collecting their preferences, the tool will summarise this valuable information into a report that can serve as a base to start a Shared Decision Making discussion between the patients and their care team regarding treatment choice.

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Icon Privacy preserving data mining

Privacy preserving data mining


Privacy preserving data mining The predictive models used in MyCareAvatar will be developed and trained in a privacy preserving way, using Distributed Learning. In this method, patient data never leaves the firewalls of its source (e.g. hospital, virtual locker in the cloud).

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Medical Cloud Company Health

MedC2 follows the path of modern healthcare by enabling a medicine that is more personalised, preventive, predictive and participatory.

MyCareAvatar, as a preventive and monitoring tool could decrease disease occurrence and help to release patients faster from the hospital by providing a constant follow-up of their health status evolution. Doing so, MyCareAvatar has the potential to help decreasing the ever-escalating costs of healthcare while increasing overall quality of life, morbidity (time spent free of chronic disease) and mortality.


Lifecycle: My data improves future health

Real-time monitoring of patients

Relevant biomarkers and other types of information (e.g. pain level, feeding regime, treatment compliance).

Predictive models

To generate more individualised statistics on disease control and side effects.

  • - Prevention
  • - Patient Involvement
  • - Treatment Decision Making
  • - Treatment Support & Monitoring
  • - Cost Efficiency
  • - Siteless Clinical Trials

In depth educational information on the disease and treatment options in the form of Patients Decision Aids.

Distributed learning methods and Blockchain solution

Enabling real-time learning and updates in a privacy-preserving way.