CATEGORY: Healthcare insurance, Explainable AI, Personalized healthcare, Real-world data, IoT, Big data

SOURCE: Big Data and Artificial Intelligence in Digital Finance. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-94590-9_16

Risk Assessment for Personalized Health Insurance Products

Kyriazakos S., Pnevmatikakis A., Perikleous A., Kanavos S. (2022)

Abstract

The way people lead their lives is considered an important factor in health. In this chapter, we describe a system to provide risk assessment based on behavior for the health insurance sector. The system processes real-world data (RWD) of individuals from their daily life that enumerate different aspects of behavior collection. The data have been captured using the Healthentia platform and a simulator that augments the actual dataset with synthetic data. Classifiers are built to predict variations of peoples’ well-being short-term outlook. Risk assessment services are provided to health insurance professionals by processing the classifier predictions in the long term while explaining the classifiers themselves provide insights on the coaching of the users of the service.

Keywords: Healthcare insurance, Explainable AI, Personalized healthcare, Real-world data, IoT, Big data

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CATEGORY: eHealth, Health monitoring, e-Health applications, Cardiovascular diseases, Virtual Assistant, Remote patient monitoring
SOURCE: Int. J. Environ. Res. Public Health 2023, FEB. 22, 20(5), 3937; https://doi.org/10.3390/ijerph20053937;

Assessing the Efficacy of a Virtual Assistant in the Remote Cardiac Rehabilitation of Heart Failure and Ischemic Heart Disease Patients: Case-Control Study of Romanian Adult Patients

Andreea Lăcraru, Ștefan-Sebastian Busnatu, Maria-Alexandra Pană, Gabriel Olteanu, Liviu Șerbănoiu, Kai Gand, Hannes Schlieter, Sofoklis Kyriazakos, Octavian Ceban, Cătălina Liliana Andrei & Crina-Julieta Sinescu

Abstract

Cardiovascular diseases (CVDs) are the leading cause of mortality in Europe, with potentially more than 60 million deaths per year, with an age-standardized rate of morbidity-mortality higher in men than women, exceeding deaths from cancer. Heart attacks and strokes account for more than four out of every five CVD fatalities globally. After a patient overcomes an acute cardiovascular event, they are referred for rehabilitation to help them to restore most of their normal cardiac functions. One effective way to provide this activity regimen is via virtual models or telerehabilitation, where the patient can avail themselves of the rehabilitation services from the comfort of their homes at designated timings. Under the funding of the European Union’s Horizon 2020 Research and Innovation program, grant no 769807, a virtual rehabilitation assistant has been designed for elderly patients (vCare), with the overall objective of supporting recovery and an active life at home, enhancing patients’ quality of life, lowering disease-specific risk factors, and ensuring better adherence to a home rehabilitation program. In the vCare project, the Carol Davila University of Bucharest (UMFCD) was in charge of the heart failure (HF) and ischemic heart disease (IHD) groups of patients. By creating a digital environment at patients’ homes, the vCare system’s effectiveness, use, and feasibility was evaluated. A total of 30 heart failure patients and 20 ischemic heart disease patients were included in the study. Despite the COVID-19 restrictions and a few technical difficulties, HF and IHD patients who performed cardiac rehabilitation using the vCare system had similar results compared to the ambulatory group, and better results compared to the control group.

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