Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design.


Journal

BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723

Informations de publication

Date de publication:
02 Jul 2024
Historique:
received: 06 02 2024
accepted: 17 06 2024
medline: 3 7 2024
pubmed: 3 7 2024
entrez: 3 7 2024
Statut: epublish

Résumé

Pregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions. Employing a qualitative descriptive study design, focus groups and/or interviews were conducted, separately engaging public contributors and clinical professionals. Diverse participants were recruited through social media convenience sampling. Semi-structured, facilitator-led discussions explored perspectives of current postnatal assessment and attitudes towards linking patient electronic healthcare data to develop digital tools for identifying postpartum women at risk of CVD. Participant perspectives were gathered using post-it notes or a facilitator scribe and analysed thematically. From 27 public and seven clinical contributors, five themes regarding postnatal check expectations versus reality were developed, including 'limited resources', 'low maternal health priority', 'lack of knowledge', 'ineffective systems' and 'new mum syndrome'. Despite some concerns, all supported data linkage to identify women postnatally, targeting intervention to those at greater risk of CVD. Participants outlined potential benefits of digitalisation and risk prediction, highlighting design and communication needs for diverse communities. Current health system constraints in England contribute to suboptimal postnatal care. Integrating data linkage and improving education on data and digital tools for maternal healthcare shows promise for enhanced monitoring and improved future health. Recognised for streamlining processes and risk prediction, digital tools may enable more person-centred care plans, addressing the gaps in current postnatal care practice.

Sections du résumé

BACKGROUND BACKGROUND
Pregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions.
METHODS METHODS
Employing a qualitative descriptive study design, focus groups and/or interviews were conducted, separately engaging public contributors and clinical professionals. Diverse participants were recruited through social media convenience sampling. Semi-structured, facilitator-led discussions explored perspectives of current postnatal assessment and attitudes towards linking patient electronic healthcare data to develop digital tools for identifying postpartum women at risk of CVD. Participant perspectives were gathered using post-it notes or a facilitator scribe and analysed thematically.
RESULTS RESULTS
From 27 public and seven clinical contributors, five themes regarding postnatal check expectations versus reality were developed, including 'limited resources', 'low maternal health priority', 'lack of knowledge', 'ineffective systems' and 'new mum syndrome'. Despite some concerns, all supported data linkage to identify women postnatally, targeting intervention to those at greater risk of CVD. Participants outlined potential benefits of digitalisation and risk prediction, highlighting design and communication needs for diverse communities.
CONCLUSIONS CONCLUSIONS
Current health system constraints in England contribute to suboptimal postnatal care. Integrating data linkage and improving education on data and digital tools for maternal healthcare shows promise for enhanced monitoring and improved future health. Recognised for streamlining processes and risk prediction, digital tools may enable more person-centred care plans, addressing the gaps in current postnatal care practice.

Identifiants

pubmed: 38956666
doi: 10.1186/s12916-024-03489-7
pii: 10.1186/s12916-024-03489-7
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

276

Informations de copyright

© 2024. The Author(s).

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Auteurs

Siobhán O'Connor (S)

Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, M13 9PL, UK.

George Tilston (G)

Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, M13 9PL, UK.

Olivia Jones (O)

Maternal and Fetal Health Research Centre, Division of Developmental Biology and Medicine, The University of Manchester, Manchester, M13 9WL, UK.

Anita Sharma (A)

NHS England, Manchester, UK.

Laura Ormesher (L)

Maternal and Fetal Health Research Centre, Division of Developmental Biology and Medicine, The University of Manchester, Manchester, M13 9WL, UK.

Bradley Quinn (B)

Health Innovation Manchester, Manchester, M13 9NQ, UK.

Anthony Wilson (A)

Clinical Data Science Unit, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK.

Jenny Myers (J)

Maternal and Fetal Health Research Centre, Division of Developmental Biology and Medicine, The University of Manchester, Manchester, M13 9WL, UK.

Niels Peek (N)

Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, M13 9PL, UK.
The Healthcare Improvement Studies Institute (THIS Institute), Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

Victoria Palin (V)

Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, M13 9PL, UK. victoria.palin@manchester.ac.uk.
Maternal and Fetal Health Research Centre, Division of Developmental Biology and Medicine, The University of Manchester, Manchester, M13 9WL, UK. victoria.palin@manchester.ac.uk.

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