Evidence map›Paper›PMID 34323916›Full record

SynthesisBJS open2021

Automated conversational agents for post-intervention follow-up: a systematic review.

L Geoghegan, A Scarborough, J C R Wormald, C J Harrison, D Collins, M Gardiner, J Bruce, J N Rodrigues

Open access · goldAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BJS open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
3.8field-weighted citation impact, top 6% of its field
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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.

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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

8 authors at 7 institutions in 1 country.

L GeogheganSection of Vascular Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.ORCID 0000-0002-2045-2923
A ScarboroughDepartment of Cardiothoracic Surgery, King's College Hospital, London, UK.
J C R WormaldNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
C J HarrisonNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
D CollinsDepartment of Plastic, Reconstructive and Burns Surgery, Chelsea and Westminster Hospital, London, UK.
M GardinerDepartment of Plastic and Reconstructive Surgery, Frimley Park Hospital, Guildford, UK.ORCID 0000-0002-8058-4186
J BruceWarwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK.ORCID 0000-0002-8462-7999
J N RodriguesWarwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK.
Nuffield Orthopaedic Centre · GBChelsea and Westminster Hospital · GBFrimley Park Hospital · GBImperial College London · GBKing's College Hospital · GBStoke Mandeville Hospital · GBUniversity of Warwick · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdvances in natural language processing and other machine learning techniques have led to the development of automated agents (chatbots) that mimic human conversation. These systems have mainly been used in commercial settings, and within medicine, for symptom checking and psychotherapy. The aim of this systematic review was to determine the acceptability and implementation success of chatbots in the follow-up of patients who have undergone a physical healthcare intervention.

methodsA systematic review of MEDLINE, MEDLINE In-process, EMBASE, PsychINFO, CINAHL, CENTRAL and the grey literature using a PRISMA-compliant methodology up to September 2020 was conducted. Abstract screening and data extraction were performed in duplicate. Risk of bias and quality assessments were performed for each study.

resultsThe search identified 904 studies of which 10 met full inclusion criteria: three randomised control trials, one non-randomised clinical trial and six cohort studies. Chatbots were used for monitoring after the management of cancer, hypertension and asthma, orthopaedic intervention, ureteroscopy and intervention for varicose veins. All chatbots were deployed on mobile devices. A number of metrics were identified and ranged from a 31 per cent chatbot engagement rate to a 97 per cent response rate for system-generated questions. No study examined patient safety.

conclusionA range of chatbot builds and uses was identified. Further investigation of acceptability, efficacy and mechanistic evaluation in outpatient care pathways may lend support to implementation in routine clinical care.

Indexed as

AftercareCommunicationClinical Trials as TopicHumansMachine LearningNatural Language Processing

Identifiers

PMID34323916
PMCPMC8320342
OpenAlexW3186085110

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.