Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review

Abstract

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings. We conducted a systematic literature review to identify relevant randomised controlled trials conducted between 2013 and 2018. We searched the following databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, Cochrane Library, ASSIA, Emerald, Health Business Fulltext Elite, ProQuest Public Health, Social Care Online, and grey literature sources. Search terms were conceptualised into three groups: artificial intelligence-related terms, computerised decision support -related terms, and terms relating to health and social care. Terms within groups were combined using the Boolean operator OR, and groups were combined using the Boolean operator AND. Two reviewers independently screened studies against the eligibility criteria and two independent reviewers extracted data on eligible studies onto a customised sheet. We assessed the quality of studies through the Critical Appraisal Skills Programme checklist for randomised controlled trials. We then conducted a narrative synthesis. We identified 68 hits of which five studies satisfied the inclusion criteria. These studies varied substantially in relation to quality, settings, outcomes, and technologies. None of the studies was conducted in social care settings, and three randomised controlled trials showed no difference in patient outcomes. Of these, one investigated the use of Bayesian triage algorithms on forced expiratory volume in 1 second (FEV1) and health-related quality of life in lung transplant patients. Another investigated the effect of image pattern recognition on neonatal development outcomes in pregnant women, and another investigated the effect of the Kalman filter technique for warfarin dosing suggestions on time in therapeutic range. The remaining two randomised controlled trials, investigating computer vision and neural networks on medication adherence and the impact of learning algorithms on assessment time of patients with gestational diabetes, showed statistically significant and clinically important differences to the control groups receiving standard care. However, these studies tended to be of low quality lacking detailed descriptions of methods and only one study used a double-blind design. Although the evidence of effectiveness of data-driven artificial intelligence to support decision-making in health and social care settings is limited, this work provides important insights on how a meaningful evidence base in this emerging field needs to be developed going forward. It is unlikely that any single overall message surrounding effectiveness will emerge - rather effectiveness of interventions is likely to be context-specific and calls for inclusion of a range of study designs to investigate mechanisms of action.