Data cleansing is primarily concerned with which of the following?

Data cleansing is fundamentally focused on checking consistency and identifying errors within a dataset. This process involves identifying inaccuracies, removing duplicates, correcting misspellings, and resolving any inconsistencies that may exist among different records. The goal is to ensure that the data is accurate, reliable, and usable for analysis, reporting, and decision-making. By validating and correcting data, organizations can enhance the quality of the information they rely on, which ultimately leads to better insights and outcomes.

This focus on validity and integrity is critical in healthcare information systems, where decisions based on erroneous data can lead to serious ramifications. Data cleansing lays the groundwork for effective data management and analysis by ensuring that all entries in a database reflect true and precise information.