An Evaluation Of Hospital Accreditation From The Survey With Text Vector Analysis Techniques

• Main Authors: Prapunwattana, Dittaphong, Imsombut, Aurawan, Suwannahitatorn, Picha, Saethang, Thammakorn
• Format: Conference Proceeding
• Language: English
• Subjects: Accreditation; Computational modeling; Hospitals; LEALLA; Predictive models; Sentence Encoder; Support vector machines; Surveys; Text analysis; Topic Modelling; USE; Zero-shot classification
• ISSN: 2642-6579
• DOI: 10.1109/JCSSE58229.2023.10202013

The Healthcare Accreditation Institute has an assessment and certification process for hospitals applying for Healthcare Accreditation. The assessment process requires a large number of text-based reports. The purpose of this research was to study the text analysis of the self-assessment reports of healthcare facilities and surveyor reports on issues related to the pharmaceutical system to evaluate and rate the accreditation of medical facilities. The natural language text vector analysis technique, together with the Universal Sentence Encoder (USE) was compared to Learning Lightweight Language-agnostic Sentence Embeddings (LEALLA) for encoding data into a high-dimensional format. Next the sentence encoding feature was fed through a machine learning procedure, including artificial neural networks, logistic regression, and support vector machines to classify nursing facility accreditation ratings. The experimental results showed that the USE embedding yielded better performance than the LEALLA embedding across all models with a precision of 0.70 but took slightly longer to encode feature sentences. This research could improve the performance of the analysis and scoring.