{'fraction_non_alphanumeric': 0.009401709401709401, 'fraction_numerical': 0.0, 'mean_word_length': 5.295698924731183, '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}}
Comparing speech recognition and text writing in recording patient health records

Duration and accuracy rate are important elements in recording clinical finding during doctor-patient encounter. This is to ensure high quality and accurate healthcare services could be delivered to the patients. Speech recognition is one of the approaches that would speed up the process of recording patient health records compared to text writing that is a typical way to capture medical data into computer system. The aims of this research is to develop speech recognition for capturing clinical finding using digital clinical terminology namely SNOMED-CT. The research also compares the time taken and accuracy rate between speech recognition and text writing. Twenty respondents had been selected to test the system and the outcome from the experiment showed that speech recognition application has faster time compared to text writing. However, data captured through speech recognition once translated to health records is always inaccurate. It was noted that the human factors such as accent and tone in speaking affect the translation of speech recognition into medical records.