• Developing a Service Quality I...

Treffer: Developing a Service Quality Index System for AI Health Care Chatbots: Mixed Methods Study.

Title:
Developing a Service Quality Index System for AI Health Care Chatbots: Mixed Methods Study.
Authors:
Gu Y; School of Medical Technology, Capital Medical University, Beijing, China., Wang X; School of Medical Technology, Capital Medical University, Beijing, China.
Source:
Journal of medical Internet research [J Med Internet Res] 2026 Feb 18; Vol. 28, pp. e83051. Date of Electronic Publication: 2026 Feb 18.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
Imprint Name(s):
Publication: <2011- > : Toronto : JMIR Publications
Original Publication: [Pittsburgh, PA? : s.n., 1999-
MeSH Terms:
Artificial Intelligence* , Quality of Health Care* , Quality Indicators, Health Care*, Delphi Technique ; Humans ; Generative Artificial Intelligence
Contributed Indexing:
Keywords: AI; Delphi method; SERVQUAL; analytic hierarchy process; artificial intelligence; artificial intelligence health care chatbot; index system development; service quality
Entry Date(s):
Date Created: 20260218 Date Completed: 20260218 Latest Revision: 20260307
Update Code:
20260307
PubMed Central ID:
PMC12961388
DOI:
10.2196/83051
PMID:
41707180
Database:
MEDLINE

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Background: Artificial intelligence (AI) health care chatbots are gaining widespread adoption worldwide. It is imperative to understand the service quality of AI health care chatbots. However, there is limited guidance on how to comprehensively evaluate their service quality.
Objective: This study aimed to develop an index system based on the SERVQUAL framework for evaluating the service quality of AI health care chatbots.
Methods: An initial indicator pool was compiled through a comprehensive literature review and consultations with 4 experts. These indicators were mapped and categorized into 5 domains adapted from the SERVQUAL framework. The experts were recruited from hospital, university, and health commission settings by purposive sampling. The service quality index system was identified using a 2-round Delphi process, which included a virtual meeting between the 2 rounds. In the third round, indicator weights within each quality domain and subdomain were determined using the analytic hierarchy process.
Results: There were 26 indicators identified in the literature, based on which the 2-round Delphi process was conducted. A total of 20 experts were invited. The response rates in both rounds of Delphi and the analytic hierarchy process were 100%, and the authoritative coefficients were both >0.7. The final service quality index system for AI health care chatbots comprises 5 primary indicators and 17 secondary indicators. There were 3 (18%) indicators on assurance, 4 (24%) on reliability, 3 (18%) on human-likeness, 4 (24%) on tangibility, and 3 (18%) on responsiveness. The primary indicators, ranked from highest to lowest weight, were assurance (0.239), reliability (0.237), human-likeness (0.187), tangibility (0.170), and responsiveness (0.167).
Conclusions: This study pioneers the development of a service quality index system for AI health care chatbots adapted from the SERVQUAL framework. The results provide a validated tool for evaluating the performance of chatbots and offer valuable insights for health service managers and developers to enhance AI-driven medical consultation services.
(©Yu Gu, Xinyi Wang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.02.2026.)