{'fraction_non_alphanumeric': 0.011284722222222222, 'fraction_numerical': 0.0, 'mean_word_length': 5.626436781609195, '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}}
What matters most to patients? On the Core Determinants of Patient Experience from Free Text Feedback

Free-text feedback from patients is increasingly used for improving the quality of healthcare services and systems. A major reason for the growing interest in harnessing free-text feedback is the belief that it provides richer information about what patients want and care about. The use of computational approaches such as structural topic modelling for analysing large unstructured textual data such as free-text feedback from patients has also been gain traction lately. However, its use for generating insights is constrained by the apparent lack of statistical rigour and explanatory capability required for credible evidence in decision making. From the theoretical perspective, theorybuilding from unstructured textual data is also currently problematic in IS and health service research. This study presents an approach to address this challenge by integrating text analytics, predictive and quantitative models as part of a computational grounded theory approach to determine factors that significantly determine overall patient experience.