Tessan Revolutionizes Healthcare with AI: Towards Continuity of Care

A New Vision for Health Through AI

In the field of health, the integration of artificial intelligence (AI) is not merely a technological innovation. For Jordan Cohen, co-founder and CEO of Tessan, the major challenge lies in using AI to reorganize the healthcare system. In France, this system still largely relies on a model centered around isolated medical acts, where patients only consult when symptoms appear, leaving a gap between consultations. This gap, often synonymous with a lack of information and follow-up, represents a high cost for public health.

Tessan, a company specializing in teleconsultation, strives to fill this gap by placing data and AI at the heart of its approach. By transforming isolated moments into a structured health journey, Tessan aims to improve the organization of care. AI is not just an additional technological layer; it becomes an essential tool for structuring and activating data at the right moment.

Bridging the Gap Between Consultations

Traditionally, the healthcare system is limited to the act of consultation, leaving a void between medical visits. This gap is characterized by a lack of information, guidance, and follow-up for patients, who enter and exit the system without a clear framework. However, AI and data can bridge this gap by connecting isolated moments and transforming medical practices.

At Tessan, which conducts over 3,500 teleconsultations per day, AI intervenes at several levels of the care pathway. Even before the consultation, it organizes symptoms and pre-fills necessary information. During the consultation, it ensures automatic transcription and assists with prescriptions. After the consultation, it offers operational assistants for medical partners. This approach allows for a shift from a model of isolated acts to one of continuity, where health value comes from the ability to organize continuity around the patient.

From Reactivity to Proactivity in Medicine

Historically, the healthcare system has been designed on a reactive logic, where patients consult when they feel symptoms, often too late. This approach is now showing its limits. The integration of AI in health paves the way for a paradigm shift, moving from reactive medicine to anticipatory medicine, driven by data.

AI enables continuous signal capture through pharmacy check-up kiosks or everyday connected devices that measure essential data such as blood pressure, heart rate, and oxygen saturation. Once analyzed by AI algorithms, this data allows for the identification of weak signals, detection of anomalies, and prioritization of actions. An unusual variation can thus trigger an alert and quickly initiate care, whether through teleconsultation, tele-expertise, or enhanced follow-up.

In this model, the patient is constantly integrated into the care pathway, monitored and re-evaluated as necessary. The goal is not to replace medical diagnosis with AI, but to improve the quality of the signal upstream. By structuring symptoms, medical history, and vital signs, AI enables earlier detection of at-risk profiles, particularly in dermatology, where internal models qualify images and prioritize relevant cases.

Pharmacies: New Centers for Detection and Support

Access to care is no longer sufficient to ensure effective management. The current challenge lies in directing patients to the right professional at the right time. AI plays a crucial role in this process by analyzing the context, reason for consultation, and patient history to guide them to the most suitable professional.

This orientation capability ensures continuity of care, with targeted follow-ups, monitoring of progress, and, if necessary, reactivation of the patient in their journey. To achieve this level of precision, AI in health relies on a variety of data, whether declarative, from teleconsultations, pharmacy check-ups, or medical devices measuring vital signs.

Pharmacies, as accessible and everyday points of contact, are becoming key players thanks to AI. They are transforming into true health hubs, reinforcing their role in the health journey by structuring and synthesizing data for healthcare professionals.

Challenges, Ethics, and Scaling Up

The integration of AI in health poses significant challenges, particularly regarding data management and adoption by professionals. Trust is an essential element in this field, as Jordan Cohen emphasizes. In health, the question is not just what can be done, but what is permissible and how.

To address these challenges, Tessan makes structural choices, such as hosting data in France with a certified HDS operator, in strict compliance with GDPR. Additionally, aiming for accreditation as a Teleconsultation Company with the Ministry of Health represents an extra level of rigor.

Beyond technical and regulatory frameworks, the use of data must be disciplined. It should only be exploited if it adds real value to the care pathway. Similarly, AI should not be a black box, and decisions must always involve a healthcare professional. The issue extends far beyond AI, touching on the very organization of the healthcare system, with a central role for pharmacists and healthcare professionals, and necessary support from public authorities to reconnect care pathways.