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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
› Institutsangaben Funding This research was supported, in part, by the National Library of Medicine of the National Institutes of Health under Award Number R01LM012854.
Weitere Informationen

Publikationsverlauf

14. November 2018

22. Dezember 2019

Publikationsdatum:
13. März 2020 (online)

    Lizenzen und Reprints

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.

Keywords

provider interaction network - electronic health record - audit log - neonatal intensive care unit - care team - network analysis - network structure - postsurgical length of stay - proportional-odds model

Supplementary Material

 

  • References

  • 1 Sahni R, Polin RA. Physiologic underpinnings for clinical problems in moderately preterm and late preterm infants. Clin Perinatol 2013; 40 (04) 645-663
    CrossrefPubMedGoogle Scholar
  • 2 Mwaniki MK, Atieno M, Lawn JE, Newton CRJC. Long-term neurodevelopmental outcomes after intrauterine and neonatal insults: a systematic review. Lancet 2012; 379 (9814): 445-452
    CrossrefPubMedGoogle Scholar
  • 3 Chavez-Valdez R, McGowan J, Cannon E, Lehmann CU. Contribution of early glycemic status in the development of severe retinopathy of prematurity in a cohort of ELBW infants. J Perinatol 2011; 31 (12) 749-756
    CrossrefPubMedGoogle Scholar
  • 4 Kornhauser M, Schneiderman R. How plans can improve outcomes and cut costs for preterm infant care. Manag Care 2010; 19 (01) 28-30
    PubMedGoogle Scholar
  • 5 Petrou S, Eddama O, Mangham L. A structured review of the recent literature on the economic consequences of preterm birth. Arch Dis Child Fetal Neonatal Ed 2011; 96 (03) F225-F232
    CrossrefPubMedGoogle Scholar
  • 6 Mangham LJ, Petrou S, Doyle LW, Draper ES, Marlow N. The cost of preterm birth throughout childhood in England and Wales. Pediatrics 2009; 123 (02) e312-e327
    CrossrefPubMedGoogle Scholar
  • 7 National Perinatal Information System/Quality Analytic Services. Available at: www.npic.org . Prepared by March of Dimes Perinatal Data Center; 2011. Accessed February 18, 2020
    PubMed
  • 8 Jacob J, Kamitsuka M, Clark RH, Kelleher AS, Spitzer AR. Etiologies of NICU deaths. Pediatrics 2015; 135 (01) e59-e65
    CrossrefPubMedGoogle Scholar
  • 9 Brodsky D, Gupta M, Quinn M. , et al. Building collaborative teams in neonatal intensive care. BMJ Qual Saf 2013; 22 (05) 374-382
    CrossrefPubMedGoogle Scholar
  • 10 Vandenberg KA. Individualized developmental care for high risk newborns in the NICU: a practice guideline. Early Hum Dev 2007; 83 (07) 433-442
    CrossrefPubMedGoogle Scholar
  • 11 Sneve J, Kattelmann K, Ren C, Stevens DC. Implementation of a multidisciplinary team that includes a registered dietitian in a neonatal intensive care unit improved nutrition outcomes. Nutr Clin Pract 2008; 23 (06) 630-634
    CrossrefPubMedGoogle Scholar
  • 12 Salera-Vieira J, Tanner J. Color coding for multiples: a multidisciplinary initiative to improve the safety of infant multiples. Nurs Womens Health 2009; 13 (01) 83-84
    CrossrefPubMedGoogle Scholar
  • 13 White RD, Smith JA, Shepley MM. ; Committee to Establish Recommended Standards for Newborn ICU Design. Recommended standards for newborn ICU design, eighth edition. J Perinatol 2013; 33 (Suppl. 01) S2-S16
    PubMedGoogle Scholar
  • 14 Milette I, Martel MJ, da Silva MR, Coughlin McNeil M. Guidelines for the institutional implementation of developmental neuroprotective care in the NICU. Part B: recommendations and justification. A joint position statement from the CANN, CAPWHN, NANN, and COINN. Can J Nurs Res 2017; 49 (02) 63-74
    CrossrefPubMedGoogle Scholar
  • 15 Profit J, Sharek PJ, Kan P. , et al. Teamwork in the NICU setting and its association with health care-associated infections in very low-birth-weight infants. Am J Perinatol 2017; 34 (10) 1032-1040
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 16 Barbosa VM. Teamwork in the neonatal intensive care unit. Phys Occup Ther Pediatr 2013; 33 (01) 5-26
    CrossrefPubMedGoogle Scholar
  • 17 O'Brien K, Bracht M, Macdonell K. , et al. A pilot cohort analytic study of family integrated care in a Canadian neonatal intensive care unit. BMC Pregnancy Childbirth 2013; 13 (01) (Suppl. 01) S12
    CrossrefPubMedGoogle Scholar
  • 18 Bracht M, O'Leary L, Lee SK, O'Brien K. Implementing family-integrated care in the NICU: a parent education and support program. Adv Neonatal Care 2013; 13 (02) 115-126
    CrossrefPubMedGoogle Scholar
  • 19 Uddin S, Khan A, Piraveenan M. Administrative claim data to learn about effective healthcare collaboration and coordination through social network. Paper presented at: 48th Hawaii International Conference on System Sciences. Kauai, Hawaii: IEEE; 2015: 3105-3114
    PubMedGoogle Scholar
  • 20 Cunningham FC, Ranmuthugala G, Plumb J, Georgiou A, Westbrook JI, Braithwaite J. Health professional networks as a vector for improving healthcare quality and safety: a systematic review. BMJ Qual Saf 2012; 21 (03) 239-249
    CrossrefPubMedGoogle Scholar
  • 21 Uddin S, Hossain L, Hamra J, Alam A. A study of physician collaborations through social network and exponential random graph. BMC Health Serv Res 2013; 13: 234
    CrossrefPubMedGoogle Scholar
  • 22 Uddin S. Exploring the impact of different multi-level measures of physician communities in patient-centric care networks on healthcare outcomes: a multi-level regression approach. Sci Rep 2016; 6: 20222
    CrossrefPubMedGoogle Scholar
  • 23 Chen Y, Lorenzi N, Nyemba S, Schildcrout JS, Malin B. We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records. Int J Med Inform 2014; 83 (07) 495-506
    CrossrefPubMedGoogle Scholar
  • 24 Chen Y, Xie W, Gunter CA. , et al. Inferring clinical workflow efficiency via electronic medical record utilization. Paper presented at: AMIA Annual Symposium Proceedings. San Francisco, CA. 2015 ;2015: 416
    PubMedGoogle Scholar
  • 25 Chen Y, Patel MB, McNaughton CD, Malin BA. Interaction patterns of trauma providers are associated with length of stay. J Am Med Inform Assoc 2018; 25 (07) 790-799
    CrossrefPubMedGoogle Scholar
  • 26 Chen Y, Lorenzi NM, Sandberg WS, Wolgast K, Malin BA. Identifying collaborative care teams through electronic medical record utilization patterns. J Am Med Inform Assoc 2017; 24 (e1): e111-e120
    CrossrefPubMedGoogle Scholar
  • 27 Chen Y, Kho AN, Liebovitz D. , et al. Learning bundled care opportunities from electronic medical records. J Biomed Inform 2018; 77: 1-10
    CrossrefPubMedGoogle Scholar
  • 28 Gray JE, Davis DA, Pursley DM, Smallcomb JE, Geva A, Chawla NV. Network analysis of team structure in the neonatal intensive care unit. Pediatrics 2010; 125 (06) e1460-e1467
    CrossrefPubMedGoogle Scholar
  • 29 Aizawa A. An information-theoretic perspective of tf–idf measures. Inf Process Manage 2003; 39 (01) 45-65
    CrossrefPubMedGoogle Scholar
  • 30 Ye J. Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artif Intell Med 2015; 63 (03) 171-179
    CrossrefPubMedGoogle Scholar
  • 31 Roque FS, Jensen PB, Schmock H. , et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLOS Comput Biol 2011; 7 (08) e1002141
    CrossrefPubMedGoogle Scholar
  • 32 Wilcoxon F, Katti SK, Wilcox RA. Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Select Tables Mathematica Stat 1970; 1: 171-259
    PubMedGoogle Scholar
  • 33 Brandes U. A faster algorithm for betweenness centrality. J Math Sociol 2001; 25 (02) 163-177
    CrossrefPubMedGoogle Scholar
  • 34 Kempe D, Kleinberg J, Tardos É. Maximizing the spread of influence through a social network. Paper presented at: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Washington, DC. 2003: 137-146
    PubMedGoogle Scholar
  • 35 Okamoto K, Chen W, Li XY. Ranking of closeness centrality for large-scale social networks. In: Preparata FP, Wu X, Yin J. (eds). Frontiers in Algorithmics. FAW 2008. Lecture Notes in Computer Science, Vol 5059. Berlin, Heidelberg: Springer;
    Google Scholar
  • 36 Bonacich P. Some unique properties of eigenvector centrality. Soc Networks 2007; 29 (04) 555-564
    CrossrefPubMedGoogle Scholar
  • 37 Tichy NM, Tushman ML, Fombrun C. Social network analysis for organizations. Acad Manage Rev 1979; 4 (04) 507-519
    CrossrefPubMedGoogle Scholar
  • 38 Barabâsi AL, Jeong H, Néda Z. , et al. Evolution of the social network of scientific collaborations. Phys A 2002; 311 (3–4): 590-614
    CrossrefPubMedGoogle Scholar
  • 39 Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci U S A 2006; 103 (23) 8577-8582
    CrossrefPubMedGoogle Scholar
  • 40 Scott J. Social network analysis. Sociology 1988; 22 (01) 109-127
    PubMedGoogle Scholar
  • 41 Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Paper presented at: Third International AAAI Conference on Weblogs and Social Media; San Jose, CA. 2009
    PubMedGoogle Scholar
  • 42 R Foundation for Statistical Computing. R: A language and environment for statistical computing. 2016 . Available at: https://www.R-project.org/ . Accessed January 31, 2019
    PubMedGoogle Scholar
  • 43 Harrell Jr FE. Regression modeling strategies. 2019 . Available at: http://biostat.mc.vanderbilt.edu/rms . Accessed January 31, 2019
    PubMedGoogle Scholar
  • 44 Partnership for Health IT Patient Safety. Closing the loop: using health IT to mitigate delayed, missed, and incorrect diagnoses related to diagnostic testing and medication changes. 2018 . Available online at: https://www.ecri.org/Resources/HIT/Closing_Loop/Closing_the_Loop_Toolkit.pdf . Accessed October 30, 2018
    PubMedGoogle Scholar
  • 45 Rucker DW. Using telephony data to facilitate discovery of clinical workflows. Appl Clin Inform 2017; 8 (02) 381-395
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 46 Chandler AE, Mutharasan RK, Amelia L, Carson MB, Scholtens DM, Soulakis ND. Risk adjusting health care provider collaboration networks. Methods Inf Med 2019; 58 (2-03): 71-78
    Artikel in Thieme ConnectPubMedGoogle Scholar