Evidence map›Paper›PMID 39296586›Full record

ArticleEClinicalMedicine2024

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

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

Abstract read
In one paragraph

Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Article
  2. Review
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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

10 authors.

Neil CockburnDepartment of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom.
Cristina OsborneDepartment of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom.
Supun WithanaDepartment of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom.
Amy ElsmoreDepartment of Obstetrics and Gynaecology, Shrewsbury and Telford Hospitals NHS Trust, Telford, United Kingdom.
Ramya NanjappaDepartment of Obstetrics and Gynaecology, Shrewsbury and Telford Hospitals NHS Trust, Telford, United Kingdom.
Matthew SouthDepartment of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom.
William Parry-SmithDepartment of Obstetrics and Gynaecology, Shrewsbury and Telford Hospitals NHS Trust, Telford, United Kingdom.
Beck TaylorWarwick Medical School, Warwick University, Coventry, United Kingdom.
Joht Singh ChandanDepartment of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom.
Krishnarajah NirantharakumarDepartment of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Clinical decision supportMaternitymHealthObstetricsSystematic review

Identifiers

PMID39296586
PMCPMC11408819

What Socratic 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 Socratic graph.