INTRODUCTION: In Brazil, the National Health System (SUS) provides healthcare to the public. The system has multiple administrative databases; the major databases record hospital (SIH) and outpatient (SIA) procedures. Epidemiological information is collected for all populations in subsystems, such as mortality (SIM), live births (SINASC) and diseases of compulsory declaration (SINAN). Each subsystem has its own information system, which is able to provide information about consultations, clinical information and medicines dispensed. However, these systems are not linked, thereby preventing individual-centred analysis. OBJECTIVE: To describe the methods and results of parameter setting that are needed to execute the probabilistic deduplication of large administrative and epidemiological databases in Brazil and to create a National Health Database Centred on the individual. METHODS: This paper shows the results of a record linkage model to integrate data from SIH, SIA, SIM, and SINAN, which have different formats and attributes between them and over time. These data consist of 1.3 billion records from 2000-2015. Probabilistic and deterministic record linkages were used to deduplicate these data. The Kappa statistic and clerical review were used to ensure the quality of the linkage. The graph algorithm and depth-first search were used to generate the identifiers. RESULTS: The deterministic deduplication process resulted in a database with 403,113,527 possible unique individuals. After the probabilistic deduplication process of the former database was performed, 159,703,805 unique individuals were identified. This result had an estimated a false positive error rate of 3.3%, and the false negative error was estimated at 12.3%. CONCLUSIONS: The National Health Database centred on the individual was generated and will allow researchers to use real-world evidence to conduct clinical, epidemiological, economic and other studies. This database represents a significant cohort, spanning 15 years of historical data and preserving patient privacy. The success of the process described will allow repeating and appending the data for future years and enable important studies to promote SUS efficiency and provide better treatments for patients.