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

Siobhán O’Connor, George Tilston, Olivia Jones, Anita Sharma, Laura Ormesher, Bradley Quinn, Anthony Wilson, Jenny Myers, Niels Peek, Victoria Palin

King's College London

Research output: Contribution to journal › Article › peer-review

Original language	English
Journal	BMC Medicine
DOIs	https://doi.org/10.1186/s12916-024-03489-7
Publication status	Published - 2 Jul 2024

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s12916-024-03489-7

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O’Connor, S., Tilston, G., Jones, O., Sharma, A., Ormesher, L., Quinn, B., Wilson, A., Myers, J., Peek, N., & Palin, V. (2024). 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. BMC Medicine. https://doi.org/10.1186/s12916-024-03489-7

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title = "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",

author = "Siobh{\'a}n O{\textquoteright}Connor and George Tilston and Olivia Jones and Anita Sharma and Laura Ormesher and Bradley Quinn and Anthony Wilson and Jenny Myers and Niels Peek and Victoria Palin",

year = "2024",

month = jul,

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O’Connor, S, Tilston, G, Jones, O, Sharma, A, Ormesher, L, Quinn, B, Wilson, A, Myers, J , Peek, N & Palin, V 2024, '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', BMC Medicine. https://doi.org/10.1186/s12916-024-03489-7

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. / O’Connor, Siobhán; Tilston, George; Jones, Olivia et al.
In: BMC Medicine, 02.07.2024.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - 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

AU - O’Connor, Siobhán

AU - Tilston, George

AU - Jones, Olivia

AU - Sharma, Anita

AU - Ormesher, Laura

AU - Quinn, Bradley

AU - Wilson, Anthony

AU - Myers, Jenny

AU - Peek, Niels

AU - Palin, Victoria

PY - 2024/7/2

Y1 - 2024/7/2

UR - https://doi.org/10.1186/s12916-024-03489-7

U2 - 10.1186/s12916-024-03489-7

DO - 10.1186/s12916-024-03489-7

M3 - Article

SN - 1741-7015

JO - BMC Medicine

JF - BMC Medicine

ER -

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

UN SDGs

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