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. 2024 Sep 5:76:102822.
doi: 10.1016/j.eclinm.2024.102822. eCollection 2024 Oct.

Clinical decision support systems for maternity care: a systematic review and meta-analysis

Neil Cockburn  1 , Cristina Osborne  1 , Supun Withana  1 , Amy Elsmore  2 , Ramya Nanjappa  2 , Matthew South  1 , William Parry-Smith  2   3 , Beck Taylor  4 , Joht Singh Chandan  1   5 , Krishnarajah Nirantharakumar  1
Affiliations

Clinical decision support systems for maternity care: a systematic review and meta-analysis

Neil Cockburn et al. EClinicalMedicine. .

Abstract

Background: The use of Clinical Decision Support Systems (CDSS) is increasing throughout healthcare and may be able to improve safety and outcomes in maternity care, but maternity care has key differences to other disciplines that complicate the use of CDSS. We aimed to identify evaluated CDSS and synthesise evidence of their impact on maternity care.

Methods: We conducted a systematic review for articles published before 24th May 2024 that described i) CDSS that ii) investigated the impact of their use iii) in maternity settings. Medline, CINAHL, CENTRAL and HMIC were searched for articles relating to evaluations of CDSS in maternity settings, with forward- and backward-citation tracing conducted for included articles. Risk of bias was assessed using the Mixed Methods Assessment Tool, and CDSS were described according to the clinical problem, purpose, design, and technical environment. Quantitative results from articles reporting appropriate data were meta-analysed to estimate odds of a CDSS achieving its desired outcome using a multi-level random effects model, first by individual CDSS and then across all CDSS. PROSPERO ID: CRD42022348157.

Findings: We screened 12,039 papers and included 87 articles describing 47 unique CDSS. 24 articles (28%) described randomised controlled trials, 30 (34%) described non-randomised interventional studies, 10 (11%) described mixed methods studies, 10 (11%) described qualitative studies, 7 (8%) described quantitative descriptive studies, and 7 (8%) described economic evaluations. 49 (56%) were in High-Income Countries and 38 (44%) in Low- and Middle-Income countries, with no CDSS trialled in both income categories. Meta-analysis of 35 included studies found an odds ratio for improved outcomes of 1.69 (95% confidence interval 1.24-2.30). There was substantial variation in effects, aims, CDSS types, context, study designs, and outcomes.

Interpretation: Most CDSS evaluations showed improvements in outcomes, but there was heterogeneity in all aspects of design and evaluation of systems. CDSS are increasingly important in delivering healthcare, and Electronic Health Records and mHealth will increase their availability, but traditional epidemiological methods may be limited in guiding design and demonstrating effectiveness due to rapid CDSS development lifecycles and the complex systems in which they are embedded. Development methods that are attentive to context, such as Human Centred Design, will help to meet this need.

Funding: None.

Keywords: Clinical decision support; Maternity; Obstetrics; Systematic review; mHealth.

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Conflict of interest statement

WPS is a council member Royal College of Obstetricians and Gynecologists. MS and KN are directors of OpenClinical CIC, a not-for-profit organisation that seeks to promote the use of Clinical Decision Support technologies. MS owns stock and received royalties from Deontics Ltd., a Clinical Decision Support company whose products are not included in the reviewed papers. JSC received grant and contract funding from National Institute for Health and Care Research, Youth Endowment Fund, College of Policing, University of Birmingham, Birmingham City Council, Home Office (UK). BT received grant and contract funding from NIHR and UKRI/MRC. KN received grant and contract funding from NIHR, UKRI/MRC, Kennedy Trust for Rheumatology Research, Health Data Research UK, Wellcome Trust, European Regional Development Fund, Institute for Global Innovation, Boehringer Ingelheim, Action Against Macular Degeneration Charity, Midlands Neuroscience Teaching and Development Funds, South Asian Health Foundation, Vifor Pharma, College of Police, and CSL Behring, and consulting fees from BI, Sanofi, CEGEDIM and MSD.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram of article selection for this study. Period from September-2022 to May 2024 updated in medline only. CINAHL: Cumulative Index to Nursing and Allied Health Literature; CENTRAL: Cochrane Central Register of Controlled Trials; HMIC: The Health Management Information Consortium database.
Fig. 2
Fig. 2
Publication of included articles on maternal Clinical Decision Support Systems by country, with colour representing the frequency of publication. Grey countries had no articles identified.
Fig. 3
Fig. 3
Identified tools organised by clinical problem (rows) and phase of pregnancy care (columns).
Fig. 4
Fig. 4
Forest plot of outcomes of interventional studies with control groups and binary outcomes. All included outcomes are displayed with odds ratio and 95% confidence interval plotted. Outcomes for each Clinical Decision Support Systems (CDSS) are combined, and then all CDSS are combined together. Dotted lines seperate CDSS, while solid lines seperate Randomised Controlled Trials (RCTs) with clinical outcomes, RCTs with process outcomes, Non-randomised interventions with clinical outcomes, and non-randomised interventions wth process outcomes. Details of outcomes analysed are provided in Supplementary 6.
Fig. 5
Fig. 5
Funnel plot showing ORs against standard errors for included outcomes in the meta-analysis. The white area represents the range inside the 95% pseudo-confidence intervals.
See this image and copyright information in PMC

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