Undiagnosed Long COVID-19 in China Among Non-vaccinated Individuals: Identifying Persistent Symptoms and Impacts on Patients' Health-Related Quality of Life.

Is Long COVID-19 under-diagnosed? The definition of this new condition has received many contributions, and it is still under development as a great variety of symptoms have been associated to it. This study explores the possibility that there are non-diagnosed cases among individuals who have been infected by SARS-CoV-2 and have not been vaccinated. The long-term symptoms identified among a sample 255 individuals have been associated to Long COVID-19 by recent literature. The study relates these symptoms to risk factors and health-related quality of life (HRQoL) negative impacts. The individuals were screened 1 year after discharge to explore its potential relation to Long COVID-19. Patients diagnosed with COVID-19 and discharged from designated hospitals in a Chinese province between January and April 2020 were included in this study. They received computed tomography (CT) scans one month after discharge. One year after discharge, patients were invited to physical examination and interviewed with questionnaire on health-related quality of life (HRQoL) and post-COVID-19 symptoms. Tobit regression and Logistic regression were applied to evaluate the risk factors for health utility value and pain/discomfort and anxiety/depression. One year after discharge, 39.61% patients complained of several of the symptoms associated to Long COVID-19. More than half had abnormal chest CT. Previous studies focused on the post-COVID-19 symptoms and chest CT findings of patients, but few studies have assessed the COVID-19-associated risk factors for health-related quality of life.

Evaluating Patients' Experiences with Healthcare Services: Extracting Domain and Language-Specific Information from Free-Text Narratives.

Evaluating patients' experience and satisfaction often calls for analyses of free-text data. Language and domain-specific information extraction can reduce costly manual preprocessing and enable the analysis of extensive collections of experience-based narratives. The research aims were to (1) elicit free-text narratives about experiences with health services of international students in Poland, (2) develop domain- and language-specific algorithms for the extraction of information relevant for the evaluation of quality and safety of health services, and (3) test the performance of information extraction algorithms' on questions about the patients' experiences with health services. The materials were free-text narratives about health clinic encounters produced by English-speaking foreigners recalling their experiences (n = 104) in healthcare facilities in Poland. A linguistic analysis of the text collection led to constructing a semantic−syntactic lexicon and a set of lexical-syntactic frames. These were further used to develop rule-based information extraction algorithms in the form of Python scripts. The extraction algorithms generated text classifications according to predefined queries. In addition, the narratives were classified by human readers. The algorithm-based and the human readers' classifications were highly correlated and significant (p < 0.01), indicating an excellent performance of the automatic query algorithms. The study results demonstrate that domain-specific and language-specific information extraction from free-text narratives can be used as an efficient and low-cost method for evaluating patient experiences and satisfaction with health services and built into software solutions for the quality evaluation in health care.