Which step involves integrating AI algorithms that analyze medical documents and make code recommendations?

Integrating AI algorithms that analyze medical documents and provide code recommendations falls under the implementation of AI-driven decision support systems. This step is crucial as it involves using machine learning and natural language processing techniques to interpret the vast amounts of unstructured data found in medical documents, such as clinical notes and discharge summaries.

In this context, AI-driven decision support systems harness advanced algorithms to sift through the data, identify relevant diagnoses and procedures, and ultimately suggest appropriate coding. This functionality is essential for enhancing the accuracy and efficiency of medical billing and coding processes, as it assists coders and billers in making informed decisions based on a wealth of data that would be cumbersome to analyze manually.

When considering the other options, model evaluation pertains to assessing the performance and accuracy of an already trained AI model, while model selection is the process of determining which algorithms or models would be best suited for the specific task at hand. Data cleansing refers to preparing data by correcting errors or inconsistencies, but it does not involve direct integration of AI for code recommendation. Thus, implementing AI-driven decision support systems is the pivotal step that directly contributes to the analysis of medical documents and the generation of coding recommendations.