Nursing processes are complex and include quite a few nonlinear interrelationships that are difficult to discover. Moreover, the formal description, visualization, and analysis of these processes are nontrivial challenges. The purpose of this paper is to establish the term network nursing as synonym for using quantitative graph theory in nursing science and to discuss how network nursing can be used for tackling such complex challenges in nursing science.
Methods
In particular, methods from quantitative graph theory, divided into two major classes, comparative network analysis and network characterization, are employed to solve challenging problems in nursing.
Findings
We demonstrate by way of example that this mathematical apparatus is feasible to tackle research questions when modeling and analyzing nursing processes.
Here we use a “NANDA-I” showcase to illustrate how nursing networks can be derived from nursing data and how these networks can be used to compare different patients regarding their nursing diagnoses.
Conclusions
Nursing networks can be used to characterize patients from a nursing perspective. Especially, they allow a process-based view and are able to map relationships or dependencies. One major advantage of a networking approach is that it can be applied independently from the underlying nursing classifications or terminologies.
Implications for Nursing Practice
Network nursing makes it possible to formally investigate the nursing process and thus opens up a so far little-known cosmos of possibilities and methods to expand nursing knowledge.
AlbertR.BarabásiA. L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–97.
2.
AndersonR. R.DanielR. R. M. (2008). Taking complexity science seriously: New research, new methods. In LindbergC.NashS.LindbergC. (Eds.), On the edge: Nursing in the age of complexity (pp. 73–96). Bordentown, NJ: PlexusPress.
3.
AntiqueiraC.TalsmaA. (2012). Characterizing the structure of operating room staffing using social network analysis. Nursing Research, 60, 378–385.
4.
BehrensJ.LangerG. (2010). Evidence-based nursing and caring: Methoden und Ethik der Pflegepraxis und Versorgungsforschung (3rd ed.). Bern: Huber.
5.
BockH. H. (1974). Automatische Klassifikation. Theoretische und praktische Methoden zur Gruppierung und Strukturierung von Daten. Göttingen: Vandenhoeck & Ruprecht.
6.
BonchevD. (1983). Information theoretic indices for characterization of chemical structures. Chichester: Research Studies Press.
7.
BotafogoR. A.RivlinE.ShneidermanB. (1992). Structural analysis of hypertexts: Identifying hierarchies and useful metrics. ACM Transactions on Information Systems, 10(2), 142–180.
8.
BunkeH. (2000). Recent developments in graph matching. In 15th international conference on pattern recognition (Vol. 2, pp. 117–124).
9.
ConteD.FoggiaF.SansoneC.VentoM. (2004). Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence, 18, 265–298.
10.
DavidsonA. W.RayM. A.TurkelM. C. (2001). Nursing, caring and complexity science: For human-enviroment well-being. New York: Springer.
11.
DehmerM. (2006). Strukturelle Analyse web-basierter Dokumente. Wiesbaden: Deutscher Universitäts Verlag.
12.
DehmerM. (2008). Information processing in complex networks: Graph entropy and information functionals. Applied Mathematics and Computation, 201, 82–94.
13.
DehmerM. (2011a). Information theory of networks. Symmetry, 3, 767–779.
14.
DehmerM. (Ed.) (2011b). Structural analysis of complex networks. Dordrecht, The Netherlands: Birkhäuser Publishing.
15.
DehmerM.Emmert-StreibF.GraberA.SalvadorA. (Eds.) (2011). Applied statistics for network biology. Hoboken, NJ: John Wiley & Sons.
16.
DehmerM.Emmert-StreibF.MehlerA. (Eds.) (2011). Towards an information theory of complex networks: Statistical methods and applications. Boston: Birkhäuser Publishing.
17.
DehmerM.GrabnerM.VarmuzaK. (2012). Information indices with high discriminative power for graphs. PLoS ONE, 7, e31214.
18.
DehmerM.MehlerA. (2007). A new method of measuring similarity for a special class of directed graphs. Tatra Mountains Mathematical Publications, 36, 39–59.
19.
DehmerM.SivakumarL.VarmuzaK. (2012). Uniquely discriminating molecular structures using novel eigenvalue-based descriptors. MATCH Communications in Mathematical and in Computer Chemistry, 67, 147–172.
20.
Emmert-StreibF.DehmerM. (2011). Networks for systems biology: Conceptual connection of data and function. IET Systems Biology, 5, 185–207.
21.
Emmert-StreibF.DehmerM.KilianJ. (2006). Classification of large graphs by a local tree decomposition. ArabniaH. R.ScimeA. (Eds.), Proceedings of DMIN'05, International Conference on Data Mining, Las Vegas, USA (pp. 200–207).
22.
Emmert-StreibF.GlazkoG. V.AltayG.deMatos SimoesR. (2012). Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Frontiers in Genetics, 3(8). doi:10.3389/fgene.2012.00008.
23.
GambianoM. L. (2008). Complexity and nursing theory: A seismic shift. In LindbergC.NashS.LindbergC. (Eds.), On the edge: Nursing in the age of complexity (pp. 49–73). Bordentown, NJ: PlexusPress.
24.
HacklW.RaucheggerF. (2011). Nursing Data Marts: Nutzungsmöglichkeiten von Routinedaten aus der elektronischen Pflegedokumentation im Rahmen patientenübergreifender Auswertungen. In FischerP. M.HöpferH.KleinJ.NicklasD.SeegerB.UmbliaT.VirginM. (Eds.), Proceedings BTW workshops 2011 (Vol. 30 of Schriftenreihe des Fachbereichs Informatik, pp. 71–75). Kaiserslautern, Germany: Technische Universität Kaiserslautern.
25.
HacklW.SchwarzmayrT.RaucheggerF.EdererC.HandlerM.DanderA., … PfeiferB. (2010). From electronic documentation to evidence based nursing: Creating data marts for analysis, evaluation and improvement of processes in patient care. In SafranC.MarinH.RetiS. (Eds.), Proceedings of the 13th world conference on medical and health informatics MEDINFO 2010 (Vol. 160 of Studies in Health Technology and Informatics, pp. 71–75). Cape Town, South Africa: IOS Press.
HerdmanT. H. (Ed.) (2009). NANDA international nursing diagnoses: Definitions & classification, 2009–2011. Oxford: Wiley-Blackwell.
28.
HuebnerU.AmmenwerthE.FlemmingD.SchaubmayrC.SellemannB. (2010). IT adoption of clinical information systems in Austrian and German hospitals: Results of a comparative survey with a focus on nursing. BMC Medical Informatics and Decision Making, 10. doi:10.1186/1472-6947-10-8
29.
LarrabeeJ. H.SionsJ.FanningM.WithrowM.FerrettiA. (2007). Evaluation of a program to increase evidence-based practice change. The Journal of Nursing Administration, 37(6), 302–310.
30.
LindbergC.NashS.LindbergC. (2008). On the edge: Nursing in the age of complexity. Bordentown, NJ: PlexusPress.
31.
LugtenbergM.BurgersJ.WestertG. (2009). Effects of evidence-based clinical practice guidelines on quality of care: A systematic review. Quality and Safety in Health Care, 18(5), 385–392.
32.
MainzerK. (2004). Symmetry and complexity. Hackensack, NJ: World Scientific.
33.
MasonO.VerwoerdM. (2007). Graph theory and networks in biology. IET Systems Biology, 1(2), 89–119.
34.
MehlerA.WeißP.LückingA. (2010). A network model of interpersonal alignment. Entropy, 12(6), 1440–1483. doi:10.3390/e12061440
35.
MeyerP. E.KontosK.LafitteF.BontempiG. (2007). Information-theoretic inference of large transcriptional regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology, 2007, 8–8. doi:10.1155/2007/79879
36.
MinoliD. (1975). Combinatorial graph complexity. Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali, 59, 651–661.
37.
MowshowitzA. (1968). Entropy and the complexity of the graphs I: An index of the relative complexity of a graph. The Bulletin of Mathematical Biophysics, 30, 175–204.
38.
Müller-StaubM.NeedhamI.OdenbreitM.LavinM.vanAchterbergT. (2007). Improved quality of nursing documentation: Results of a nursing diagnoses, interventions, and outcomes implementation study. International Journal of Nursing Terminologies and Classifications, 18(1), 5–17.
39.
NewmanM. E. J.BarabásiA. L.WattsD. J. (2006). The structure and dynamics of networks. Princeton, NJ: Princeton University Press.
40.
NicolisG.PrigogineI. (1989). Exploring complexity: An introduction. New York: Freeman.
41.
PesutD. J. (2008). Thoughts on thinking with complexity in mind. In LindbergC.NashS.LindbergC. (Eds.), On the edge: Nursing in the age of complexity (pp. 211–238). Bordentown, NJ: PlexusPress.
42.
SchönauE.HeeringC.Müller-StaubM. (2009). Evidenzbasierte Pflege und diagnostische Genauigkeit. Krankenpflege, 202(2), 15–17.
43.
SobikF. (1982). Graphmetriken und Klassifikation strukturierter Objekte. ZKI-Informationen, Akademie der Wissenschaften der DDR, 2(82), 63–122.
44.
SobikF. (1986). Modellierung von Vergleichsprozessen auf der Grundlage von Ähnlichkeitsmaßen für Graphen. ZKI-Informationen, Akademie der Wissenschaften der DDR, 4, 104–144.
45.
StephensJ. C. (1970). Managing complexity. University Press of Washington.
46.
TsangS. S.ChenT. Y.WangS. F.TaiH. L. (2012). Nursing work stress: The impacts of social network structure and organizational citizenship behavior. Journal of Nursing Research, 20, 8–18.
47.
WalshM. B.YuraH. (1967). The nursing process; assessing, planning, implementing, and evaluating. Washington, DC: Catholic University of America Press.
48.
WassermanS.FaustK. (1994). Social network analysis: Methods and applications. Cambridge, UK: Cambridge University Press.
49.
WattsD. J. (1999). Small worlds: The dynamics of networks between order and randomness. Princeton, NJ: Princeton University Press.
50.
WattsD. J.StrogatzS. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.
51.
ZelinkaB. (1975). On a certain distance between isomorphism classes of graphs. Časopis pro pěstování Matematiky, 100, 371–373.
