Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort study
Aim 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 analy...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2024-09-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/31/1/e101072.full |
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| author | Neil J Sebire Stephen D Marks John Booth Spiros Denaxas Rebecca Pope William A Bryant Maria H Eriksson |
| author_facet | Neil J Sebire Stephen D Marks John Booth Spiros Denaxas Rebecca Pope William A Bryant Maria H Eriksson |
| author_sort | Neil J Sebire |
| collection | DOAJ |
| description | Aim 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.Method 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.Results 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.Conclusions 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. |
| format | Article |
| id | doaj-art-71b9c6ac98d649ff95c2eb9e99eb6b01 |
| institution | Kabale University |
| issn | 2632-1009 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-71b9c6ac98d649ff95c2eb9e99eb6b012025-08-20T03:52:43ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092024-09-0131110.1136/bmjhci-2024-101072Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort studyNeil J Sebire0Stephen D Marks1John Booth2Spiros Denaxas3Rebecca Pope4William A Bryant5Maria H Eriksson6Great Ormond Street Hospital for Children NHS Foundation Trust, UK, NIHR Great Ormond Street Hospital for Children NHS Foundation Trust Biomedical Research Centre, UKUniversity College London Great Ormond Street Institute of Child Health, London, UKUniversity College London Great Ormond Street Institute of Child Health, London, UKInstitute of Health Informatics, University College London, London, UKUniversity College London Great Ormond Street Institute of Child Health, London, UKDRIVE, Great Ormond Street Hospital for Children NHS Foundation Trust, UKUniversity College London Great Ormond Street Institute of Child Health, London, UKAim 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.Method 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.Results 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.Conclusions 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.https://informatics.bmj.com/content/31/1/e101072.full |
| spellingShingle | Neil J Sebire Stephen D Marks John Booth Spiros Denaxas Rebecca Pope William A Bryant Maria H Eriksson Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort study BMJ Health & Care Informatics |
| title | Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort study |
| title_full | Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort study |
| title_fullStr | Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort study |
| title_full_unstemmed | Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort study |
| title_short | Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional–patient interaction intensity: a cohort study |
| title_sort | method to apply temporal graph analysis on electronic patient record data to explore healthcare professional patient interaction intensity a cohort study |
| url | https://informatics.bmj.com/content/31/1/e101072.full |
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