Abstract
Online physician reviews (OPRs), also known as electronic word of mouth, have become the primary source of information for patients while making health consultation decisions. However, different techniques to analyze these reviews by machines have not been frequently applied yet in this domain. In this study, a novel method for opinion mining is being proposed to fill the existing research gap, that is, a hybrid approach to sentic computing. This approach integrates artificial intelligence and semantic web techniques to implicitly analyze OPRs in order to evaluate patient perceptions of healthcare service quality. We develop our methodology by using the following three main tasks: (1) sentence-level topic spotting (a topic-analysis procedure) to extract major topics, (2) a sentic computing framework to perform concept-level sentiment analysis (polarity detection on the categorized sentences), and (3) root cause and strengths, weaknesses, opportunities, and threats (SWOT) analyses to identify SWOT for healthcare organizations. Analyses results of 47,499 OPRs from the UK-based website (Iwantgreatcare.org) show that the proposed hybrid approach has accurately classified concept words to their corresponding topics, and it has also outperformed the similar other methods of topic extraction in the healthcare domain. The results also indicate that the proposed approach can better contribute to the overall performance analyses for healthcare organizations, which could help practitioners in improving service-quality measures based on the real voices of patients. The proposed model also provides a theoretical basis for formulating quality measures for the healthcare sector.
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This research is supported by the National Natural Science Foundation, People’s Republic of China (No. 71531013, 71729001).
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Shah, A.M., Yan, X., Tariq, S. et al. Listening to the patient voice: using a sentic computing model to evaluate physicians’ healthcare service quality for strategic planning in hospitals. Qual Quant 55, 173–201 (2021). https://doi.org/10.1007/s11135-020-00999-3
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DOI: https://doi.org/10.1007/s11135-020-00999-3