Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T13:22:59.515Z Has data issue: false hasContentIssue false
  • English
  • Français

The Language of Caring: Quantitating Medical Practice Patternsusing Symbolic Dynamics

Published online by Cambridge University Press:  28 April 2010

J. Paladino ,
A. M. Kaynar ,
P. S. Crooke  and
J. R. Hotchkiss
J. Paladino
Affiliation:
Department of Medicine, University of Hawaii
A. M. Kaynar
Affiliation:
Departments of Critical Care Medicine and Anesthesiology, University of Pittsburgh
P. S. Crooke
Affiliation:
Department of Mathematics , Vanderbilt University
J. R. Hotchkiss*
Affiliation:
Departments of Critical Care Medicine and Medicine, University of Pittsburgh and Pittsburgh Veterans Affairs Healthcare System
*
* Corresponding author. E-mail:hotchkissjr@upmc.edu
Get access

Abstract

Real-world medical decisions rarely involve binary Ðsole condition present or absent-patterns of patient pathophysiology. Similarly, provider interventions are rarely unitaryin nature: the clinician often undertakes multiple interventions simultaneously.Conventional approaches towards complex physiologic derangements and their associatedmanagement focus on the frequencies of joint appearances, treating the individualderangements of physiology or elements of intervention as conceptually isolated. Thisframework is ill suited to capture either the integrated patterns of derangement displayedby a particular patient or the integrated patterns of provider intervention. Here weillustrate the application of a different approach-that of symbolic dynamics-in which theintegrated pattern of each patients derangement, and the associated provider response, arecaptured by defining words based on the elements of the pattern offailure. We will use as an example provider practices in the context of mechanicalventilation- a common, potentially harmful, and complex life support technology. We alsodelineate other domains in which symbolic dynamics approaches might aid in quantitatingpractice patterns, assessing quality of care, and identifying best practices.

Keywords

symbol dynamicspatient carelanguage
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 5 , Issue 3: Mathematical modeling in the medical sciences , 2010 , pp. 165 - 172
Copyright
© EDP Sciences, 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Engbert, R., Scheffczyk, C., Krampe, R.T., Rosenblum, M., Kurths, J., Kliegl, R.. Tempo-induced transitions in polyrhythmic hand movements . Phys. Rev. E, 56 (1997), No. 5, 5823–5833. CrossRefGoogle Scholar
Hao, B.. Symbolic dynamics and characterization of complexity . Physica D, 51 (1991),161–176. CrossRefGoogle Scholar
Engbert, R., Schiek, M., Kurths, J., Krampe, R., Kliegl, R., Drepper, F.. Symbolic dynamics of physiological synchronization: Examples from bimanual movements and cardiorespiratory interaction . Nonlin. Anal. Theor. Meth. Appl., 30 (1997), No. 2, 973–984. CrossRefGoogle Scholar
Schwarz, U., Benz, A.O., Kurths, J., Witt, A.. Analysis of solar spike events by means of symbolic dynamics methods . Astron. Astrophys., 277 (1993), 215–224. Google Scholar
Tang, X.Z., Tracy, E.R., Boozer, A.D., Debrauwa, A., Brown, R.. Symbol sequence statistics in noisy chaotic signal reconstruction . Phys. Rev. E, 51 (1995), 3871–3889. CrossRefGoogle ScholarPubMed
Daw, C.S., Kennel, M.B., Finney, C.E.A., Connolly, F.T.. Observing and modeling nonlinear dynamics in an internal combustion engine . Phys. Rev. E, 57 (1998), No. 3, 2811–2819. CrossRefGoogle Scholar
Daw, C.S., Finney, C.E.A., Tracy, E.R.. A review of symbolic analysis of experimental data . Rev. Scien. Instru., 47 (2003), No. 2, 915–930. CrossRefGoogle Scholar
Hotchkiss, J.R., Crooke, P.S., Adams, A.B., Marini, J.J.. Implications of a biphasic two compartment model of constant flow ventilation for the clinical setting . J. Crit. Care. 9 (1994), No. 2, 114–123. CrossRefGoogle ScholarPubMed
Crooke, P.S., Head, J.D., Marini, J.J., Hotchkiss, J.R.. Patient-ventilator interaction: A general model for non-passive mechanical ventilation . IMA J. Math. Appl. Med. Biol., 15 (1998), 321–337. CrossRefGoogle Scholar
Crooke, P.S., Hota, S., Marini, J.J., Hotchkiss, J.R.. A mathematical model for carbon dioxide exchange during mechanical ventilation with TGI . Math. Comp. Mod., 29 (1999), 45–61. CrossRefGoogle Scholar
Adams, A.B., Bliss, P., Hotchkiss, J.R.. Effects of respiratory impedance on the performance of bi-level pressure ventilators . Respir. Care, 45 (2000), No. 4, 390–400. Google Scholar
Hotchkiss, J.R., Dries, D.J., Marini, J.J., Crooke, P.S.. Dynamical behavior during noninvasive ventilation: chaotic support? Am. J. Resp. Crit. Care Med., 163 (2001), 374–378. CrossRefGoogle Scholar
Hotchkiss, J.R., Adams, A.B., Stone, M.K., Dries, D.J., Marini, J.J.. Oscillations and noise: inherent instability of pressure support ventilation? Am. J. Resp. Crit. Care Med., 165 (2002), 47–53. CrossRefGoogle Scholar
Crooke, P.S., Hotchkiss, J.R., Marini, J.J.. Linear and nonlinear mathematical models for non-invasive, passive ventilation . Math. Comp. Mod., 35 (2002),1297–1313. CrossRefGoogle Scholar
Crooke, P.S., Hotchkiss, J.R., Marini, J.J.. Modeling recruitment maneuvers with a variable compliance model for pressure preset ventilation . J. Theor. Med., 43 (2002),No. 3, 197–207. CrossRefGoogle Scholar