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This study aimed to evaluate the use of hidden Markov models (HMM) for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient's and mother's names in the databases of the Mortality Information System (SIM), Information Subsystem for High Complexity Procedures (APAC), and Hospital Information System (AIH). A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.

This study focuses on the development of a system to integrate monthly outpatient cancer care data in the Unified National Health System (SUS). The system was modeled to retrace the treatment evolution in each cancer case and services output, with the following advantage: the system focuses on the cancer case, does not require knowledge by the user in order to handle the database, and allows updating the base as new data emerge. The results of direct queries in the system were identical to those obtained from direct inspection of the original database and those from another integration approach. The use of a tool with these characteristics by public administrators can help improve the quality of outpatient cancer care provided by the National Health System.