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

Publication date: Jan 22, 2020

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.

Open Access PDF

Cresswell, K., Callaghan, M., Khan, S., Sheikh, Z., Mozaffar, H., and Sheikh, A. Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review. 06553. 2020 Health Informatics J.

Concepts Keywords
Artificial Intelligence Critical appraisal
Bayesian Randomized controlled trial
CINAHL Systematic review
Cochrane Library Nursing research
Computer Vision Clinical research
Double Blind Healthcare quality
EMBASE Knowledge
FEV1 Health
Forced Expiratory Volume Articles
Gestational Diabetes Evidence-based practices
Kalman Filter Lung transplant
Lung Transplant Conceptualised groups
MEDLINE Artificial intelligence systems
Neonatal Triage algorithms
Neural Networks Social care groups
ProQuest Evidence-based medicine
PsycINFO Cochrane
Randomised Controlled Trials Artificial intelligence
Social Care Bayesian triage algorithms
Systematic Review
Therapeutic Range


Type Source Name
disease MESH development
drug DRUGBANK Warfarin
disease MESH medication adherence
disease MESH gestational diabetes
drug DRUGBANK Coenzyme M
pathway REACTOME Reproduction
disease MESH emergencies
disease MESH thrombosis
disease MESH satisfaction
disease MESH stroke
drug DRUGBANK Aspartame
disease MESH uncertainty
drug DRUGBANK Indoleacetic acid
disease MESH fetal heart rate
drug DRUGBANK Hexachlorophene
drug DRUGBANK Gold
disease MESH cancer


Original Article

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