Data quality and Big Data in the health industry: a scoping review protocol

IntroductionBig Data is characterized by the large volume of data, the variety of types and formats, the speed with which they are generated, and the veracity and value that can be extracted from the data. However, the result obtained with this technology will depend on the quality of the information obtained from the data. Big Data has great potential in healthcare and can be used to advance diagnosis, treatment, and healthcare management. Health data is highly vulnerable due to its sensitive nature, as it contains personal and confidential information. If exposed or compromised, it could lead to privacy violations, inaccuracies, misuse, incorrect diagnoses, or misguided decision-making in patient care. It is important to prioritize confidentiality, adhere to regulatory compliance, and maintain data integrity; for that, it is essential to use efficient methods to obtain quality data and make them able to reach the proposed objective. ObjectiveIn this context, the scoping review protocol aims to identify and map existing strategies, methods, or models that improve the quality of medical and health data in Big Data environments. This review explores the methods to support the effective use of Big Data in healthcare while addressing the challenges to maintain data integrity and ensure safe decision-making. Methods and analysisThis scoping review will be conducted based on the six-step process outlined in the framework proposed by Levac et al. in "Scoping Studies: Advancing the methodology" and will be reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. The research team will use Data Quality, Big Data, and Health terms to search for primary studies in the Scopus Document Search, IEEE Xplore Digital Library, and ACM Digital Library databases.