To make the wealth of information in unstructured documentation more accessible and usable, several strategies can be employed. Firstly, Natural Language Processing (NLP) can be used to analyze unstructured text and extract relevant information. This can help in identifying codeable items more accurately. Secondly, machine learning algorithms can be trained to predict the most likely codes based on the context of the documentation. This can improve the accuracy of CAC. Lastly, healthcare organizations can invest in training for their staff to improve their documentation practices. This can help in creating more structured and standardized documentation, which can be more easily analyzed and used for decision-making