Research on the identification of medical service quality factors: based on a data-driven method

Huiying Gao*, Shan Lu, Xiaojin Kou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Purpose: The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing online reviews of medical and healthcare service platforms in combination with a questionnaire survey. Design/methodology/approach: This study adopts a combination of review mining and questionnaire surveys. The latent Dirichlet allocation (LDA) model was used to mine hospital reviews on the medical and healthcare service platform to obtain the medical service quality factors that patients pay attention to, and then the questionnaire was administered to obtain the relative importance of these factors to patients' perception of service quality. Finally, the index system was established. Findings: The medical service quality factors patients care about include medical skills and ethics, registration service, operation effect, consulting communication, drug therapy, diagnosis process and medical equipment. Research limitations/implications: The identification of medical service quality factors provides a reference for medical institutions to improve their medical service quality. Originality/value: This study uses online review mining to obtain medical service quality factors from the perspective of patients, which is different from previous methods of obtaining factors from relevant literature or expert judgments; then, based on the mining results, a medical service quality evaluation index system is established by using questionnaire data.

Original languageEnglish
Pages (from-to)1617-1645
Number of pages29
JournalInternet Research
Volume32
Issue number5
DOIs
Publication statusPublished - 7 Sept 2022

Keywords

  • Influencing factors
  • Medical service quality
  • Review mining
  • Topic extraction

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