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The French National Authority for Health proposes the first classification of digital solutions used in healthcare

22 February 2021

Given the growing number of digital tools that can be used in healthcare, the French National Authority for Health (HAS) has recently developed a system for classifying digital healthcare solutions according to their purpose of use, their capacity to provide a personalised response and their autonomy, i.e. their ability to act with or without human intervention. The aim is to help players find their way around and contribute to better integration of these tools in the health and medico-social sector.

However, the diversity of digital solutions goes hand in hand with their great heterogeneity, linked to their technological nature, their functionalities and the public for whom they are intended (patients, carers, healthcare professionals etc). These digital solutions are also distinguished by other criteria, such as their status (medical device or not), their evaluation process and/or the repositories required of developers, whether they are covered by health insurance etc.

Therefore, the HAS has drawn up a classification that is simple to use with a total of 11 types of digital solution classified into four levels (A, B, C and D). Level B is divided into eight sub-categories. This classification is based on their purpose of use, ability to offer a personalised response and autonomy in decision-making (distinguishing those requiring human intervention to implement a therapeutic, screening or diagnostic action from those generating the same type of action on their own, i.e. without prior human intervention).

The classification grid (see below) has been designed as a basic reference tool intended for the various potential actors and uses. The classification grid will be updated over time and according to other parameters of a regulatory, technical or economic nature.

At national and European level, the digital regulatory framework is developing, particularly on the issues of autonomy and artificial intelligence. The grid proposed by the HAS could help to structure exchanges and, ultimately, contribute to the efficient integration of digital solutions in the health and medico-social areas of the healthcare system.

Level A: Support services for patients, carers or professionals in the context of care or care pathway optimisation or medical/socio-administrative management without direct action on patient health, shared medical records, online appointment scheduling software, geolocation application for public health purposes etc.

Level B: Non-personalised general information for the user on living conditions, hygiene and dietary rules, pathologies/disabilities or any health condition (in the broadest sense of the term), life paths etc. Also provides training materials and tools for health professionals.

Level C: Assistance with living, prevention, screening, diagnosis, observance, monitoring or treatment of a pathology, a state of health or in the context of a disability situation, without the autonomy of the digital solution in the management of the therapeutic decision. This level alone comprises eight sub-categories according to the various functionalities of the solutions at this level. Concrete examples include: audio description application for the blind; application enabling people with disabilities to request assistance to solve a specific problem from connected volunteer carers; remote monitoring system enabling a healthcare professional to interpret and manage patient data remotely; connected emergency alert bracelet for the elderly; ovulation period prediction tool; gamification solution applied to the treatment of psychiatric pathologies; wrist tensiometer connected to the patient’s mobile phone; and software associated with a chest band to detect respiratory pauses in order to diagnose sleep apnoea.

Level D: Autonomous decision management after data analysis and diagnosis to automatically adjust the treatment to be administered, without human intervention. For example: a system that analyses data from a continuous glucose monitor used by a diabetic patient that will automatically adjust the basal rate or administer a bolus dose without the patient intervening (artificial pancreas); and a cardiac defibrillator implanted with a telemonitoring solution that analyses data from a cardiac monitor, delivers a shock in the event of cardiac arrest and can transmit alerts to the professional monitoring the patient.