###### Abstract  
Patients are the most important actors in clinical research. Therefore, patient preference information (PPI) could support the decision-making process, being indisputable for research value, quality, and integrity. However, there is a lack of clear guidance or consensus on the search for preference studies. In this blueprint, an openly available and regularly updated patient preference management system for an integrated database (PPMSDB) that contains the minimal set of data sufficient to provide detailed information for each study (the so-called evidence tables in systematic reviews) and a high-level overview of the findings of a review (summary tables) is described. These tables could help determine which studies, if any, are eligible for quantitative synthesis. Finally, a web platform would provide a graphical and user-friendly interface. On the other hand, a set of APIs (application programming interfaces) would also be developed and provided. The PPMSDB, aims to collect preference measures, characteristics, and meta-data, and allow researchers to obtain a quick overview of a research field, use the latest evidence, and identify research gaps. In conjunction with proper statistical analysis of quantitative preference measures, these aspects can facilitate formal evidence-based decisions and adequate consideration when conducting a structured decision-making process. Our objective is to outline the conceptual infrastructure necessary to build and maintain a successful network that can monitor the currentness and validity of evidence. PPI; PPMSDB; systematic reviews; analytic infrastructure +
Footnote †: journal: MedicalAligning health care policy with patient preferences could improve the effectiveness of health care interventions by implementing the adoption of, satisfaction with, and adherence to clinical treatments or public health programs [14; 15]. Therefore, it is fundamental to know the preferences of a treatment expressed by the patients [16]. This type of data is referred to as patient preference information (PPI). PPI is defined by the Food and Drug Administration (FDA) as \"qualitative or quantitative assessments of the relative desirability or acceptability to patients of specified alternatives or choices among outcomes or other attributes that differ among the alternative health interventions\" [7]. PPI aims to capture patients' needs and provide a significant opportunity for patients to express their preferences [17]. PPI can be assessed through patient preference studies (PPSs) using preference exploration or elicitation methods [16]. The former are qualitative studies, such as patient interviews or focus groups, that examine the patient's subjective experiences and decisions [18]. The latter are quantitative methods, mostly adopting approaches developed in health economics, collecting quantifiable data for statistical analysis. Therefore, PPI aims to capture patients' needs and provide a significant opportunity for patients to express their preferences; in summary, PPI enhances the possibility of integrating patient preferences into the decision-making process [17]. There is an emerging consensus that the patient perspective should be incorporated into decisions in the medical product lifecycle (MPLC) [19; 20; 21; 22; 23; 24]. Furthermore, PPI can be used in every phase of MPLC to identify unmet medical needs, to notify the selections of endpoints [25], and to inform about benefit-risk assessments [13]; in fact, PPI can give insights into the trade-offs that patients make between benefits and risks and show the relative importance of outcomes for patients [12; 26; 27]. The evidence also demonstrates that integrating PPI into clinical practice can optimise symptoms management, supportive therapy, and patient-centered care and ultimately benefit survival during oncological treatment [28; 29; 30; 31; 32]. Furthermore, patient-centric decision-making results in better transparency and accountability in the development of medical products.