Electronic Health Records (EHRs) often contain unstructured data, making it challenging to extract insights. Standardizing medical data, particularly medical descriptions, enhances healthcare, reduces ambiguity, and improves data sharing. Authors employed a narrative review to examine the challenges and limitations of medical data standardization and proposed future directions. Focusing on Clinical Entity Recognition (CER) as a key Natural Language Processing (NLP) task, the research analyzed standardization processes in healthcare workflows, highlighting issues like inconsistent terminology and non-standard abbreviations. CER plays a vital role in organizing patient data, enabling semantic interoperability, and extracting insights from unstructured texts. Standardization was found to improve CER performance, data quality, and integration while reducing errors, development costs, and supporting machine learning (ML). Although standardization enhances CER and facilitates smart healthcare solutions, addressing its challenges requires stakeholder collaboration and recognition of limitations.