Measuring electronic communication networks in virtual care teams using electronic health records access-log data
| Autor: | Xi Zhu, Daniel K. Sewell, Vimal Mishra, Shin Ping Tu, Alan W. Dow, Nengliang Aaron Yao, Colin Banas |
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| Rok vydání: | 2019 |
| Předmět: |
020205 medical informatics
Computer science Health Personnel education Health Informatics 02 engineering and technology Health records Network topology Computer Communication Networks 03 medical and health sciences 0302 clinical medicine Multivariate analysis of variance Log data 0202 electrical engineering electronic engineering information engineering Electronic Health Records Humans Electronic communication 030212 general & internal medicine Patient Care Team Primary Health Care Health professionals Communication Cancer stage Data science Telecommunications network Telemedicine |
| Zdroj: | International Journal of Medical Informatics. 128:46-52 |
| ISSN: | 1386-5056 |
| DOI: | 10.1016/j.ijmedinf.2019.05.012 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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