Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies
PERSIST aims at developing an open and interoperable ecosystem to improve the care of cancer survivors. The key results to be achieved by partners are: increased self-efficacy and satisfaction with care as well as reduced psychological stress for a better management of the consequences of the cancer treatment and the disease, resulting in an improvement in health and wellbeing and a faster integration into the labour market, where applicable, compared to usual care (KR1); increased effectiveness in cancer treatment and follow-up by providing prediction models from Big Data that will support decision-making and contribute to optimal treatment decisions with positive consequences in the QoL and the health status of survivors (KR2); and improved information and evidence to advance the efficacy of management, intervention and prevention policies/strategies in order to timely treat side effects and, if possible, avoid secondary diseases and fatal events. The long-term result will be to reduce the socio-economic burden related to cancer survivors’ care (KR3). The ecosystem proposed consists of a Big Data platform to be built on top of an open infrastructure from one of the partners and a mHealth application for patients. The main building blocks to be developed are a multimodal sensing network running on a smart phone that will collect relevant data regarding the wellbeing of the patient; predictive models from anonymised health data from thousands of breast and colorectal patients; and modules essential for the development of a decision support system, which will employ the predictive models mentioned. Furthermore, PERSIST will contribute to establish evidence on the use of liquid biopsy techniques to the follow-up of cancer patients treated with curative purposes. A pilot study involving 160 patients and 32 health care professionals will be decisive to establish a co-creation methodology ranging from the earlies phases of the project throughout its conclusion.
PERSIST aims at developing an open and interoperable ecosystem to improve the care of cancer survivors. The key results to be achieved by partners are: increased self-efficacy and satisfaction with care as well as reduced psychological stress for a better management of the consequences of the cancer treatment and the disease, resulting in an improvement in health and wellbeing and a faster integration into the labour market, where applicable, compared to usual care (KR1); increased effectiveness in cancer treatment and follow-up by providing prediction models from Big Data that will support decision-making and contribute to optimal treatment decisions with positive consequences in the QoL and the health status of survivors (KR2); and improved information and evidence to advance the efficacy of management, intervention and prevention policies/strategies in order to timely treat side effects and, if possible, avoid secondary diseases and fatal events. The long-term result will be to reduce the socio-economic burden related to cancer survivors’ care (KR3). The ecosystem proposed consists of a Big Data platform to be built on top of an open infrastructure from one of the partners and a mHealth application for patients. The main building blocks to be developed are a multimodal sensing network running on a smart phone that will collect relevant data regarding the wellbeing of the patient; predictive models from anonymised health data from thousands of breast and colorectal patients; and modules essential for the development of a decision support system, which will employ the predictive models mentioned. Furthermore, PERSIST will contribute to establish evidence on the use of liquid biopsy techniques to the follow-up of cancer patients treated with curative purposes. A pilot study involving 160 patients and 32 health care professionals will be decisive to establish a co-creation methodology ranging from the earlies phases of the project throughout its conclusion.

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