Background. Patients with Neck and/or Low Back Pain (NLBP) constitute a heterogeneous group with the prognosis and precise mix of factors involved varying substantially between individuals. This means that a one-size-fits-all approach is not recommended, but methods to tailor treatment to the individual needs are still relatively under-developed. Moreover, the fragmentation of disciplines involved in its study hampers achieving sound answers to clinical questions. Data mining techniques open new horizons by combining data from existing datasets, in order to select the best treatment at each moment in time to a patient based on the individual characteristics. Method. Within the Back-UP project (H2020 #777090) a multidisciplinary consortium is creating a prognostic model to support more effective and efficient management of NLBP, based on the digital representation of multidimensional clinical information. Patient-specific models provide a personalized evaluation of the patient case, using multidimensional health data from the following sources: (1) psychological, behavioral, and socioeconomic factors, (2) biological patient characteristics, including musculoskeletal structures and function, and molecular data, (3) workplace and lifestyle risk factors. Conclusion. The Back-UP system leverages shared-decision making, not only by enabling interoperability between all professionals involved in the care trajectory, but also empowering the patient in the decisions related to his/her care path. Furthermore, dynamic intervention models ensure that the patient receives the most beneficial treatment at each moment in time, considering the current position of the patient in the care path (i.e. within clinical rehabilitation, in return-to-work process or through motivational strategies supporting self-management in daily life). No conflicts of interest. Sources of funding: This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 777090