Evaluation of Maternal Patient Experience Through Natural Language Processing Techniques: The Case of Twitter Data in The United States During COVID-19

Abstract

Abstract Purpose The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback, such as HCAHPS, to measure patients' experiences. The power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. Method We used the "COVID-19 Tweets" Dataset, which has over 28 million tweets, to evaluate patient experience using Natural Language Processing (NLP) and extract tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. Results We created word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” regarding the maternal patient experience, which are expressed through social media. Conclusion This model will help process improvement experts without domain expertise understand various domain challenges efficiently. Such insights can help decision-makers improve the patient care system. We also conducted a preliminary study to discover if a particular group faces racial health inequity.