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

• Published in: International journal of environmental research and public health Vol. 19; no. 16; p. 10182
• Main Authors: Jacennik, Barbara, Zawadzka-Gosk, Emilia, Moreira, Joaquim Paulo, Glinkowski, Wojciech Michał
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.08.2022; MDPI
• Subjects: Algorithms; Clinical outcomes; Delivery of Health Care; Health care; Health facilities; Health Services; Humans; Information Storage and Retrieval; Language; Narratives; Natural Language Processing; Patients; Semantics; Poland; free-text narratives; information extraction; text mining; patient experience; linguistic analysis; technological innovation
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph191610182

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.