Reference no: EM133876245

Question

Dirty data, relating to electronic health records, is data or information that is inaccurate, inconsistent, or not fully complete in a patient's health record. Dirty data can lead to mistreatment or misrepresentation of a patient. Dirty data is more prevalent now because our world is more data driven and the increase for human error is high, especially when using a keyboard to input information instead of handwritten notes. "Human error is one of the most significant contributors to dirty data. When data is entered manually, there is a high likelihood of errors occurring due to typos, misinterpretation, or lack of attention to detail" (Williams, 2024). Other examples of dirty data are system failures and lack of standardization which can lead to misinterpretation. Data Cleaning is essential to quality, decision-making, and efficiency. One thing that can be done to ensure clean data is data screening. Going through the information and ensuring the information that is there is correct. In medical, we do this for every patient, as the patient checks in for their appointment, we verify their demographics and health insurance are up to date. "Data cleaning is crucial step in the data management and data science pipeline ensures that the data is accurate, consistent, and reliable, which is essential for effective analysis and decision-making".