Methods Inf Med 2019; 58(04/05): 109-123
DOI: 10.1055/s-0040-1702237
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Modeling Care Team Structures in the Neonatal Intensive Care Unit through Network Analysis of EHR Audit Logs

You Chen
1  Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
2  Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, Nashville, Tennessee, United States
,
Christoph U. Lehmann
3  Departments of Pediatrics, Bioinformatics, and Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States
,
Leon D. Hatch
4  Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Emma Schremp
5  Department of Anesthesiology, Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Bradley A. Malin
1  Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
2  Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, Nashville, Tennessee, United States
6  Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Daniel J. France
5  Department of Anesthesiology, Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, Tennessee, United States
› Author Affiliations
Funding This research was supported, in part, by the National Library of Medicine of the National Institutes of Health under Award Number R01LM012854.
Further Information

Publication History

14 November 2018

22 December 2019

Publication Date:
13 March 2020 (online)

Abstract

Background In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS).

Methods The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model.

Results The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p = 0.03).

Conclusion Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.

Supplementary Material