Perioperative application of chatbots: a systematic review and meta-analysis.

Shih-Jung Lin, Chin-Yu Sun, Dan-Ni Chen, Yi-No Kang, Nai Ming Lai, Kee-Hsin Chen, Chiehfeng Chen
Author Information
  1. Shih-Jung Lin: School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  2. Chin-Yu Sun: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan.
  3. Dan-Ni Chen: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan.
  4. Yi-No Kang: Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan.
  5. Nai Ming Lai: Digital Health and Innovation Impact Lab, Taylor's University, Subang Jaya, Malaysia.
  6. Kee-Hsin Chen: Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan clifchen@tmu.edu.tw keehsin@tmu.edu.tw.
  7. Chiehfeng Chen: Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan clifchen@tmu.edu.tw keehsin@tmu.edu.tw. ORCID

Abstract

BACKGROUND AND OBJECTIVES: patient-clinician communication and shared decision-making face challenges in the perioperative period. Chatbots have emerged as valuable support tools in perioperative care. A simultaneous and complete comparison of overall benefits and harm of chatbot application is conducted.
MATERIALS: MEDLINE, EMBASE and the Cochrane Library were systematically searched for studies published before May 2023 on the benefits and harm of Chatbots used in the perioperative period. The major outcomes assessed were patient satisfaction and knowledge acquisition. Untransformed proportion (PR) with a 95%���CI was used for the analysis of continuous data. Risk of bias was assessed using the Cochrane Risk of Bias assessment tool version 2 and the Methodological Index for Non-Randomised Studies.
RESULTS: Eight trials comprising 1073 adults from four countries were included. Most interventions (n���=���5, 62.5%) targeted perioperative care in orthopaedics. Most interventions use rule-based Chatbots (n���=���7, 87.5%). This meta-analysis found that the majority of the participants were satisfied with the use of Chatbots (mean proportion=0.73; 95%���CI: 0.62 to 0.85), and agreed that they gained knowledge in their perioperative period (mean proportion=0.80; 95%���CI: 0.74 to 0.87).
CONCLUSION: This review demonstrates that perioperative Chatbots are well received by the majority of patients with no reports of harm to-date. Chatbots may be considered as an aid in perioperative communication between patients and clinicians and shared decision-making. These findings may be used to guide the healthcare providers, policymakers and researchers for enhancing perioperative care.

Keywords

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MeSH Term

Humans
Perioperative Care
Patient Satisfaction
Communication
Decision Making, Shared
Physician-Patient Relations

Word Cloud

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