The practicalities of adapting UK maternity clinical information systems for observational research: Experiences of the POOL study.

clinical information systems feasibility observational research routine data waterbirth

Journal

International journal of population data science
ISSN: 2399-4908
Titre abrégé: Int J Popul Data Sci
Pays: Wales
ID NLM: 101737740

Informations de publication

Date de publication:
2023
Historique:
medline: 28 2 2024
pubmed: 28 2 2024
entrez: 28 2 2024
Statut: epublish

Résumé

Using routinely collected clinical data for observational research is an increasingly important method for data collection, especially when rare outcomes are being explored. The POOL study was commissioned to evaluate the safety of waterbirth in the UK using routine maternity and neonatal clinical data. This paper describes the design, rationale, set-up and pilot for this data linkage study using bespoke methods. Clinical maternity information systems hold many data items of value for research purposes, but often lack specific data items required for individual studies. This study used the novel method of amending an existing clinical maternity database for the purpose of collecting additional research data fields. In combination with the extraction of existing data fields, this maximised the potential use of existing routinely collected clinical data for research purposes, whilst reducing NHS staff data collection burden.Wellbeing Software Twenty-six NHS sites were set-up over 27 months (January 2019 - April 2021). Twenty-four thousand maternity records were extracted from the one NHS site, pertaining to the period January 2015 to March 2019. Data field completeness for maternal and neonatal primary outcomes were mostly acceptable. Neonatal identifiers flowed to the National Neonatal Research Database for successful matching and linkage between maternity and neonatal unit records. Piloting the data extraction and linkage highlighted the need for additional governance arrangements, training at NHS sites and new processes for the study team to ensure data quality and confidentiality are upheld during the study. Amending existing NHS electronic information systems and accessing clinical data at scale, is possible, but continues to be a time consuming and a technically challenging exercise.

Sections du résumé

Background UNASSIGNED
Using routinely collected clinical data for observational research is an increasingly important method for data collection, especially when rare outcomes are being explored. The POOL study was commissioned to evaluate the safety of waterbirth in the UK using routine maternity and neonatal clinical data. This paper describes the design, rationale, set-up and pilot for this data linkage study using bespoke methods.
Methods UNASSIGNED
Clinical maternity information systems hold many data items of value for research purposes, but often lack specific data items required for individual studies. This study used the novel method of amending an existing clinical maternity database for the purpose of collecting additional research data fields. In combination with the extraction of existing data fields, this maximised the potential use of existing routinely collected clinical data for research purposes, whilst reducing NHS staff data collection burden.Wellbeing Software
Results UNASSIGNED
Twenty-six NHS sites were set-up over 27 months (January 2019 - April 2021). Twenty-four thousand maternity records were extracted from the one NHS site, pertaining to the period January 2015 to March 2019. Data field completeness for maternal and neonatal primary outcomes were mostly acceptable. Neonatal identifiers flowed to the National Neonatal Research Database for successful matching and linkage between maternity and neonatal unit records.
Discussion UNASSIGNED
Piloting the data extraction and linkage highlighted the need for additional governance arrangements, training at NHS sites and new processes for the study team to ensure data quality and confidentiality are upheld during the study. Amending existing NHS electronic information systems and accessing clinical data at scale, is possible, but continues to be a time consuming and a technically challenging exercise.

Identifiants

pubmed: 38414546
doi: 10.23889/ijpds.v8i1.2072
pii: 8:1:18
pmc: PMC10897763
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2072

Déclaration de conflit d'intérêts

Statement of conflict of interests: The authors declare that they have no competing interests.

Auteurs

Fiona Lugg-Widger (F)

Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, CF14 4YS, UK.

Christian Barlow (C)

Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, CF14 4YS, UK.

Rebecca Cannings-John (R)

Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, CF14 4YS, UK.

Chris Gale (C)

Neonatal Medicine, School of Public Health, Faculty of Medicine, Imperial College London, Chelsea and Westminster Hospital campus, London, SW10 9NH, UK.

Nicola Houlding (N)

Wellbeing Software Group, i2 Mansfield, Hamilton Court, Oakham Business Park, Mansfield, NG18 5FB.

Rebecca Milton (R)

Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, CF14 4YS, UK.

Rachel Plachcinski (R)

Parent, patient and public representative, National Childbirth Trust [NCT], Brunel House, Clifton, Bristol BS8 3NG.

Michael Robling (M)

Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, CF14 4YS, UK.
DECIPHer, School of Social Sciences, Cardiff University, Cardiff, CF10 3WT, UK.

Julia Sanders (J)

School of Healthcare Sciences, Cardiff University, Ty Dewi Sant, Heath Park, Cardiff. CF14 4YS, UK.

Classifications MeSH