Evidence map›Paper›PMID 39032946›Full record

SynthesisBMJ health & care informatics2024

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

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in BMJ health & care informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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  5. Observational
  6. Article
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  8. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shih-Jung Lin *School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Chin-Yu Sun *Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan.
Dan-Ni ChenDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan.
Yi-No KangCochrane Taiwan, Taipei Medical University, Taipei, Taiwan.
Nai Ming LaiDigital Health and Innovation Impact Lab, Taylor's University, Subang Jaya, Malaysia.
Kee-Hsin Chen *Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan clifchen@tmu.edu.tw keehsin@tmu.edu.tw.
Chiehfeng Chen *Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan clifchen@tmu.edu.tw keehsin@tmu.edu.tw.ORCID http://orcid.org/0000-0002-1595-6553

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesPatient-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.

resultsEight 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).

conclusionThis 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.

Indexed as

Perioperative CareCommunicationDecision Making, SharedHumansPatient SatisfactionPhysician-Patient RelationsArtificial intelligenceDecision Making, Computer-AssistedHealth CommunicationInternet-Based InterventionMedical Informatics Applications

Identifiers

PMID39032946
PMCPMC11261686

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.