Which of the following is NOT a correct step for analyzing patient data to improve treatment outcomes?

In analyzing patient data to improve treatment outcomes, adhering to data quality rules is paramount. This includes ensuring the accuracy, completeness, consistency, and relevance of the data. Ignoring data quality rules would have detrimental effects on the analysis, leading to potentially flawed insights and unreliable outcomes in treatment.

Utilizing predictive analytics involves applying statistical techniques and machine learning algorithms to anticipate potential health outcomes based on historical data, effectively improving patient care. Performing data profiling helps assess the data's quality and structure, enabling analysts to understand its characteristics and address any issues that may impact their analysis. Integrating data using secure connections ensures that sensitive patient information is protected while allowing for comprehensive analysis across various data sources.

Each of these steps contributes positively to the effectiveness and reliability of the analysis, setting them apart from the importance of upholding data quality standards.