Automated conversational agents for post-intervention follow-up: a systematic review. Issue 4 (29th July 2021) - Geoghegan, L; Scarborough, A; Wormald, J C R; Harrison, C J; Collins, D; Gardiner, M; Bruce, J; Rodrigues, J N
Abstract: Background: Advances 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. Methods: A 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. Results: The 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. Conclusion: A 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. Abstract : The COVID-19 pandemic has necessitated a shift away from in-person consultation and follow-up. The authors performed a systematic review to determine the acceptability of using chatbots to engage with patients following intervention. They identified ten studies which report the use of chatbots following intervention and outline pragmatic steps which must be taken before clinical implementation.