{'fraction_non_alphanumeric': 0.015952143569292122, 'fraction_numerical': 0.0, 'mean_word_length': 5.121951219512195, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An error detecting and tagging framework for reducing data entry errors in electronic medical records (EMR) system

We develop an error detecting and tagging framework for reducing data entry errors in Electronic Medical Records (EMR) systems. We propose a taxonomy of data errors with three levels: Incorrect Format and Missing error, Out of Range error, and Inconsistent error. We aim to address the challenging problem of detecting erroneous input values that look statistically normal but are abnormal in medical sense. Detecting such an error needs to take patient medical history and population data into consideration. In particular, we propose a probabilistic method based on the assumption that the input value for a field depends on the historical records of this field, and is affected by other fields through dependency relationships. We evaluate our methods using the data collected from an EMR System. The results show that the method is promising for automatic data entry error detection.