Measuring electronic communication networks in virtual care teams using electronic health records access-log data

Xi Zhu, Shin-Ping Tu, Daniel Sewell, Nengliang (Aaron) Yao, Vimal Mishra, Alan Dow, Colin Banas

    Research output: Contribution to journalArticle

    Abstract

    Objective: To develop methods for measuring electronic communication networks in virtual care teams using electronic health records (EHR)access-log data. Methods: For a convenient sample of 100 surgical colorectal cancer patients, we used time-stamped EHR access-log data extracted from an academic medical center's EHR system to construct communication networks among healthcare professionals (HCPs)in each patient's virtual care team. We measured communication linkages between HCPs using the inverse of the average time between access events in which the source HCPs sent information to and the destination HCPs retrieved information from the EHR system. Social network analysis was used to examine and visualize communication network structures, identify principal care teams, and detect meaningful structural differences across networks. We conducted a non-parametric multivariate analysis of variance (MANOVA)to test the association between care teams’ communication network structures and patients’ cancer stage and site. Results: The 100 communication networks showed substantial variations in size and structures. Principal care teams, the subset of HCPs who formed the core of the communication networks, had higher proportions of nurses, physicians, and pharmacists and a lower proportion of laboratory medical technologists than the overall networks. The distributions of conditional uniform graph quantiles suggested that our network-construction technique captured meaningful underlying structures that were different from random unstructured networks. MANOVA results found that the networks’ topologies were associated with patients’ cancer stage and site. Conclusions: This study demonstrates that it is feasible to use EHR access-log data to measure and examine communication networks in virtual care teams. The proposed methods captured salient communication patterns in care teams that were associated with patients’ clinical differences.

    Original languageEnglish (US)
    Pages (from-to)46-52
    Number of pages7
    JournalInternational Journal of Medical Informatics
    Volume128
    DOIs
    StatePublished - Aug 1 2019

    Fingerprint

    Electronic Health Records
    Communication
    Delivery of Health Care
    Patient Care Team
    Analysis of Variance
    Multivariate Analysis
    Medical Electronics
    Medical Laboratory Personnel
    Pharmacists
    Social Support
    Colorectal Neoplasms
    Neoplasms
    Nurses
    Physicians

    Keywords

    • Communication networks
    • Electronic health records
    • Methods
    • Virtual care teams

    ASJC Scopus subject areas

    • Health Informatics

    Cite this

    Measuring electronic communication networks in virtual care teams using electronic health records access-log data. / Zhu, Xi; Tu, Shin-Ping; Sewell, Daniel; Yao, Nengliang (Aaron); Mishra, Vimal; Dow, Alan; Banas, Colin.

    In: International Journal of Medical Informatics, Vol. 128, 01.08.2019, p. 46-52.

    Research output: Contribution to journalArticle

    Zhu, Xi ; Tu, Shin-Ping ; Sewell, Daniel ; Yao, Nengliang (Aaron) ; Mishra, Vimal ; Dow, Alan ; Banas, Colin. / Measuring electronic communication networks in virtual care teams using electronic health records access-log data. In: International Journal of Medical Informatics. 2019 ; Vol. 128. pp. 46-52.
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    abstract = "Objective: To develop methods for measuring electronic communication networks in virtual care teams using electronic health records (EHR)access-log data. Methods: For a convenient sample of 100 surgical colorectal cancer patients, we used time-stamped EHR access-log data extracted from an academic medical center's EHR system to construct communication networks among healthcare professionals (HCPs)in each patient's virtual care team. We measured communication linkages between HCPs using the inverse of the average time between access events in which the source HCPs sent information to and the destination HCPs retrieved information from the EHR system. Social network analysis was used to examine and visualize communication network structures, identify principal care teams, and detect meaningful structural differences across networks. We conducted a non-parametric multivariate analysis of variance (MANOVA)to test the association between care teams’ communication network structures and patients’ cancer stage and site. Results: The 100 communication networks showed substantial variations in size and structures. Principal care teams, the subset of HCPs who formed the core of the communication networks, had higher proportions of nurses, physicians, and pharmacists and a lower proportion of laboratory medical technologists than the overall networks. The distributions of conditional uniform graph quantiles suggested that our network-construction technique captured meaningful underlying structures that were different from random unstructured networks. MANOVA results found that the networks’ topologies were associated with patients’ cancer stage and site. Conclusions: This study demonstrates that it is feasible to use EHR access-log data to measure and examine communication networks in virtual care teams. The proposed methods captured salient communication patterns in care teams that were associated with patients’ clinical differences.",
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    AB - Objective: To develop methods for measuring electronic communication networks in virtual care teams using electronic health records (EHR)access-log data. Methods: For a convenient sample of 100 surgical colorectal cancer patients, we used time-stamped EHR access-log data extracted from an academic medical center's EHR system to construct communication networks among healthcare professionals (HCPs)in each patient's virtual care team. We measured communication linkages between HCPs using the inverse of the average time between access events in which the source HCPs sent information to and the destination HCPs retrieved information from the EHR system. Social network analysis was used to examine and visualize communication network structures, identify principal care teams, and detect meaningful structural differences across networks. We conducted a non-parametric multivariate analysis of variance (MANOVA)to test the association between care teams’ communication network structures and patients’ cancer stage and site. Results: The 100 communication networks showed substantial variations in size and structures. Principal care teams, the subset of HCPs who formed the core of the communication networks, had higher proportions of nurses, physicians, and pharmacists and a lower proportion of laboratory medical technologists than the overall networks. The distributions of conditional uniform graph quantiles suggested that our network-construction technique captured meaningful underlying structures that were different from random unstructured networks. MANOVA results found that the networks’ topologies were associated with patients’ cancer stage and site. Conclusions: This study demonstrates that it is feasible to use EHR access-log data to measure and examine communication networks in virtual care teams. The proposed methods captured salient communication patterns in care teams that were associated with patients’ clinical differences.

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