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

You Chen; Christoph U. Lehmann; Leon D. Hatch; Emma Schremp; Bradley A. Malin; Daniel J. France

doi:10.1055/s-0040-1702237

Methods of Information in Medicine, Inhaltsverzeichnis

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

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

²Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, Nashville, Tennessee, United States

,

Christoph U. Lehmann

³Departments of Pediatrics, Bioinformatics, and Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States

,

Leon D. Hatch

⁴Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

,

Emma Schremp

⁵Department of Anesthesiology, Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, Tennessee, United States

,

Bradley A. Malin

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

²Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, Nashville, Tennessee, United States

⁶Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

,

Daniel J. France

⁵Department of Anesthesiology, Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, Tennessee, United States

› Institutsangaben

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

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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
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
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
4 Kornhauser M, Schneiderman R. How plans can improve outcomes and cut costs for preterm infant care. Manag Care 2010; 19 (01) 28-30
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
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
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
8 Jacob J, Kamitsuka M, Clark RH, Kelleher AS, Spitzer AR. Etiologies of NICU deaths. Pediatrics 2015; 135 (01) e59-e65
9 Brodsky D, Gupta M, Quinn M. , et al. Building collaborative teams in neonatal intensive care. BMJ Qual Saf 2013; 22 (05) 374-382
10 Vandenberg KA. Individualized developmental care for high risk newborns in the NICU: a practice guideline. Early Hum Dev 2007; 83 (07) 433-442
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
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
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
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
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
16 Barbosa VM. Teamwork in the neonatal intensive care unit. Phys Occup Ther Pediatr 2013; 33 (01) 5-26
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
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
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
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
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
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
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
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
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
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
27 Chen Y, Kho AN, Liebovitz D. , et al. Learning bundled care opportunities from electronic medical records. J Biomed Inform 2018; 77: 1-10
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
29 Aizawa A. An information-theoretic perspective of tf–idf measures. Inf Process Manage 2003; 39 (01) 45-65
30 Ye J. Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artif Intell Med 2015; 63 (03) 171-179
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
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
33 Brandes U. A faster algorithm for betweenness centrality. J Math Sociol 2001; 25 (02) 163-177
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
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;
36 Bonacich P. Some unique properties of eigenvector centrality. Soc Networks 2007; 29 (04) 555-564
37 Tichy NM, Tushman ML, Fombrun C. Social network analysis for organizations. Acad Manage Rev 1979; 4 (04) 507-519
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
39 Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci U S A 2006; 103 (23) 8577-8582
40 Scott J. Social network analysis. Sociology 1988; 22 (01) 109-127
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
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
43 Harrell Jr FE. Regression modeling strategies. 2019 . Available at: http://biostat.mc.vanderbilt.edu/rms . Accessed January 31, 2019
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
45 Rucker DW. Using telephony data to facilitate discovery of clinical workflows. Appl Clin Inform 2017; 8 (02) 381-395
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

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