Optimizing quality of life for patients during and after primary cancer treatment requires evidence-based rehabilitation and supportive care. Since every patient with cancer is unique, care should be personalized, i.e. tailored to the individual needs, preferences, capabilities and characteristics of each patient. It should integrate soma and psyche, address the patients’ autonomy, and should be provided in an efficient way.
Numerous rehabilitation and supportive care programs aiming at improving quality of life are available. These programs focus on exercise and psychosocial function, and generally use a one-size fits all approach. Clinical practice shows that a program may be effective in some patients but not in others, which may be indicative of the heterogeneity among cancer patients. Furthermore, for clinicians and patients, rehabilitation and supportive care is still a maze: the number of programs is increasing without evidence-based direction which program is best for whom.
An essential step towards tailored rehabilitation and supportive care is to determine what program works best for whom, under what circumstances, and through which mechanisms. We need to build and implement a clinical decision rule that provides input for shared – i.e. active participation of clinicians and patients – decision making regarding the most optimal choice for rehabilitation and supportive care programs. The development of this clinical decision rule requires a large data set.