Data and Artificial Intelligence (AI) have revolutionized the healthcare industry, enabling personalized medicine. Personalized medicine is a medical model that tailors treatment and prevention plans to individual patients based on their genetic, lifestyle and environmental information. The use of data and AI in personalized medicine provides numerous benefits, including improved patient outcomes, reduced healthcare costs, and better disease management.
ONCORELIEF focuses on the management of symptoms of cancer survivors, exploiting patient data to monitor the progression of post-cancer symptoms, detect changes, and suggest appropriate treatments in real-time. More specifically, the following data sources are used:
- Historical patient data kept by the hospital
- Real-world data collected through a smart device worn by the patient
- Patient responses to questionnaires
Clinicians can view this data for each patient they care, through the ONCORELIEF web application. However, simple access to available data can hardly provide any benefit. Indeed, as each doctor may attend multiple patients, and a large amount of data is collected per patient, the task of assessing the condition of patients can be overwhelming. Artificial intelligence comes to the rescue. ONCORELIEF leverages AI to quickly provide the clinician with a quick overview of their patients’ status and of their main symptoms.
As regards the patient status, the Quality of Life index has been devised by the ONCORELIEF project to capture the condition of a patient into a single easy-to-read metric. AI is used to go through captured data and responses and come up with this metric. The Quality of Life index is complemented by a notification system, which informs clinicians when the former drops more than 20% within a week. As regards the patient symptoms, the clinician is presented with the phenotypic group a patient belongs to, based on the patient’s responses to questionnaires and captured data. In order for these groups to be created, AI is parsing all data collected to identify categories of patients based on their symptoms and lifestyle. As more data pertaining to a patient are collected, the ONCORELIEF web-app is able to more accurately match this patient to a certain category. Since each category is characterized by a specific set of symptoms, the clinician can easily see how well each patient matches this category, and subsequently identify which recommendation can be suggested to the patient in order to improve his/her condition.
Summing up, ONCORELIEF, through the Quality of Life index and the clustering engine, allows healthcare providers to adjust treatment plans quickly and effectively, leading to better disease management and improved patient outcomes.