The P-medicine project : a new personalized approach to the medical decision process

Therapeutic processes are characterized by the continuous necessity to take decisions. Due to the complexity of the medical discipline, decisions are often subject to biases and errors that can endanger the patient’s life. Aim of the project The p-medicine project proposes a personalized approach for the treatment of cancer diseases that includes the development of automated decision-making tools for clinical routine that, starting from data obtained from single patients, run simulation workflows in support of efficient and unbiased clinical decision making processes. Methods Three multiscale simulation models of clinical tumor response to treatment will be developed: one for nephroblastoma, one for breast cancer and one for acute lymphoblastic leukaemia, based on the principles that have been shown to be most appropriate for the clinical trial context. These three models will constitute the simulation core of the p-medicine oncosimulator which will provide an integrated platform for simulating, investigating and exploring the natural phenomenon of cancer and ‐ after successful validation ‐ an integrated decision support tool for treatment. Regarding patients, they are typically seen just as the recipients of care. Another important ideal of p-medicine is to better enable them to be participants and guides in their own health care. Patients will be educated and informed about the therapeutic processes in which they are involved. Only by using this information, shared decision support is possible. Transparency in data handling, augmentation of the patient’s knowledge and participation as an active partner in a shared decision process increases trust in the Health Care System including data handling and demands for more research by patients allowing the use of his/her individual data to solve his/her personal medical problems. Conclusions We argue that sophisticated tools such as the oncosimulator and the automated decision support tools can efficiently assist and improve the medical decision processes both from the side of therapists and patients.