Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.


Journal

BMJ health & care informatics
ISSN: 2632-1009
Titre abrégé: BMJ Health Care Inform
Pays: England
ID NLM: 101745500

Informations de publication

Date de publication:
10 Oct 2024
Historique:
received: 01 03 2024
accepted: 03 09 2024
medline: 11 10 2024
pubmed: 11 10 2024
entrez: 10 10 2024
Statut: epublish

Résumé

Interactions between patients and healthcare professionals (HCP) during hospital admissions are complex and difficult to interrogate using traditional analysis of electronic patient record (EPR) data. The objective of this study was to determine the feasibility of applying temporal network analytics to EPR data, focusing on HCP-patient interactions over time. Network (graph) analysis was applied to routinely collected structured data from an EPR for HCP interactions with individual patients during admissions for patients undergoing renal transplantation between May 2019 and June 2023. Networks were constructed per day of admission within a session, defined by whether the patient was in the intensive care unit (ICU) or standard hospital ward. Connections between HCP were defined using a 60 min period. Reports were generated visualising daily interaction network structures, across individual admissions. 2300 individual networks were constructed from 127 hospital admissions for renal transplantation. The number of nodes or HCP per network varied from 2 to 45, and network metrics provided detail regarding variation in the density and transitivity, changes in structure with different diameters and radii, and variations in centralisation. Each network analysis metric has a contribution to play in describing the dynamics of a daily HCP network and the composite findings provide insights that cannot be determined with standard approaches. Network analysis provides a novel approach to investigate and visualise patterns of HCP-patient interactions which allow for a deeper understanding of the complex nature of hospital patient care and could have numerous practical operational applications.

Identifiants

pubmed: 39389618
pii: bmjhci-2024-101072
doi: 10.1136/bmjhci-2024-101072
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

Auteurs

John Booth (J)

University College London Great Ormond Street Institute of Child Health, London, UK John.Booth@gosh.nhs.uk.
Data Research Innovation and Virtual Environment (DRIVE), Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.

Maria H Eriksson (MH)

University College London Great Ormond Street Institute of Child Health, London, UK.

Stephen D Marks (SD)

University College London Great Ormond Street Institute of Child Health, London, UK.
NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK.

William A Bryant (WA)

2 Data Research Innovation and Virtual Environment (DRIVE), Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.

Spiros Denaxas (S)

Institute of Health Informatics, University College London, London, UK.
Biomedical Research Centre, UCLH, London, UK.

Rebecca Pope (R)

University College London Great Ormond Street Institute of Child Health, London, UK.

Neil J Sebire (NJ)

University College London Great Ormond Street Institute of Child Health, London, UK.
Drive, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.

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Classifications MeSH