ABSTRACT:
Data quality is essential for the functioning of Health Information Systems, directly influencing public policy formulation and continuity of care. However, challenges such as quality variability, lack of standardization, and interoperability issues still compromise information reliability. With the advancement of Big Data and Artificial Intelligence, data quality has become even more critical, as analytical models depend on completeness and consistency. This study conducts an integrative literature review (2015-2024) to analyze global interventions in HIS and personnel management, supported by quality indicators, that contribute to improving data quality. This study followed the integrative review principles described by Whittemore and Knafl. The Population, Variable, and Outcome strategy guided the research question. We included articles, dissertations, and theses published between 2015 and 2024, retrieved from PubMed, Scopus, Web of Science, IEEE, LILACS, and CAPES. Screening and analysis were performed using Rayyan and Zotero software. Interventions were classified as technological, personnel management-focused, or hybrid (combining both). The PRISMA flowchart was used to organize the study selection process.

RESULTS:
Most rnaearch was conducted in Africa (41%), followed by Asia (22%), North America (13%), Europe (9%), South America (9%), and Oceania (6%). Technological interventions (30.3%) included electronic audits, automation, and applications. Personnel management interventions (15.3%) focused on training, supervision, and process standardization. Hybrid approaches (54.4%) combined both. Quality indicators evaluated intervention impact, error reduction, protocol adherence, data updates, duplicate elimination, completeness, and reliability. Results indicate that well-structured interventions not only improve data quality but also strengthen trust in health information for decision-making.

CONCLUSIONS:
This study reinforces the need for an integrated approach combining technology and permanent education to ensure HIS data quality. Furthermore, it highlights that new technologies, such as Artificial Intelligence ana Data, can play a strategic role in continuous monitoring and improving data quality, ensuring greater reliability, integrity, and precision in health records.