1. Proceedings
  2. I3M
  3. 2018
  4. IWISH
  5. 10.46354/i3m.2018.iwish.001

Simulation framework for Mobile Patient Monitoring Systems

  • a Michael Aleithe , 
  • b Philipp Skowron , 
  • c Angela Carell  , 
  • d Daniel Böttger  , 
  • e Tobias Goblirsch  , 
  • f Bogdan Franczyk   
  • a,b,e,f Leipzig University, Grimmaische Straße 12, 04109 Leipzig, Germany
  • c adesso AG, Stockholmer Allee 20, 44269 Dortmund, Germany
  • d Stiftung Deutsche Depressionshilfe, Semmelweisstraße 10, 04103 Leipzig, Germany
Cite as
Aleithe M., Skowron P., Carell A., Böttger D., Goblirsch T., Franczyk B. (2018). Simulation framework for Mobile Patient Monitoring Systems. Proceedings of the 7th International Workshop on Innovative Simulation for Healthcare (IWISH 2018), pp. 1-7. DOI: https://doi.org/10.46354/i3m.2018.iwish.001

Abstract

There is a large variety of data-centered therapy support systems involving self-management for medical purposes, usually referred to as Mobile Patient Monitoring Systems (MPMS). The considerable growth of MPMS and the variety of options afforded by the mobile sensors of smart instruments lead to enormous development cost. The range of options is commonly explored in trial and error, incurring significant expenditures of time and funds. This paper presents a simulation framework that offers a capability to run these trials in a simulation-based fashion and thereby achieve quicker and more cost-effective results than was previously possible.

References

  1. Ahmed S., Amagoh F., 2008. Modeling Hospital Resources with Process Oriented Simulation. Central Asia Business 2008 . pp. 5–20.
  2. Almagooshi S., 2015. Simulation Modelling in Healthcare: Challenges and Trends. Procedia Manufacturing 3 . pp. 301–307.
  3. Andreev V. P., Head T., Johnson N., Deo S. K., Daunert S., Goldschmidt-Clermont, P. J., 2013. Discrete event simulation model of sudden cardiac death predicts high impact of preventive interventions. Scientific reports 3 . pp. 1771.
  4. Baig M. M., Gholamhosseini H., Connolly M. J., 2015. Mobile Healthcare Applications: System Design Review: Critical Issues and Challenges. Australasian Physical & Engineering Sciences in Medicine 38(1): pp. 23–38.
  5. Bolaji A., 2014. Simulation of a Real-time Mobile Health Monitoring System Model for Hypertensive Patient in Rural Nigeria. African Journal of Computing & ICT.
  6. Cabrera E., Luque E., Taboada M., Epelde F., Iglesias M. L., 2012. Optimization of emergency departments by agent-based modeling and simulation. 13th International Conference on  Information Reuse & Integration (IRI) . pp. 423–430. Las Vegas, NV, USA.
  7. Carlson C. S., 2012. Effective FMEAs. Hoboken, NJ, USA: John Wiley & Sons, Inc.
  8. Fernandez R., Shah S., Rosenman E. D., Kozlowski S. W. J., Parker S. H., Grand J. A., 2017. Developing Team Cognition: A Role for Simulation. Simulation in healthcare. journal of the Society for Simulation in Healthcare 12(2): pp. 96–103.
  9. Geis G. L., Pio B., Pendergrass T. L., Moyer M. R., Patterson M. D., 2011. Simulation to assess the safety of new healthcare teams and new facilities. Simulation in healthcare. journal of the Society for Simulation in Healthcare 6(3): pp. 125–133.
  10. Gittinger M., Brolliar S. M., Grand J. A., Nichol G., Fernandez R., 2017. Using Simulation as an Investigational Methodology to Explore the Impact of Technology on Team Communication and Patient Management: A Pilot Evaluation of the Effect of an Automated Compression Device. Simulation in healthcare. journal of the Society for Simulation in Healthcare 12(3): pp. 139–147.
  11. Lammers R. L., Willoughby-Byrwa M., Fales W. D., 2014. Errors and error-producing conditions during a simulated, prehospital, pediatric cardiopulmonary arrest. Simulation in healthcare. journal of the Society for Simulation in Healthcare 9(3): pp. 174–183.
  12. Lee B. Y., Brown S. T., Korch G. W., Cooley P. C., Zimmerman R. K., Wheaton W. D. et al., 2010. A computer simulation of vaccine prioritization.  allocation, and rationing during the 2009 H1N1 influenza pandemic. Vaccine 28(31): pp. 4875–4879.
  13. Lunze J., 1995. Künstliche Intelligenz für Ingenieure. München, Wien: Oldenbourg. pp. 182-192.
  14. MIL-P-1629. 1949. Procedures for Performing a Failure Mode, Effects and Criticality Analysis.
  15. Monks T., Pitt M., Stein K., James M., 2012. Maximizing the population benefit from thrombolysis in acute ischemic stroke: a modeling study of in-hospital delays. Stroke 43(10): pp. 2706–2711.
  16. Nguyen K. D., Cutcutache I., Sinnadurai S., Liu S., Basol C., Sim E., 2008. Fast and accurate simulation of biomonitoring applications on a wireless body area network. 5th International Summer School and Symposium on Medical Devices and Biosensors . pp. 145–148. Hong Kong, China.
  17. Panigrahi D., Chiasserini C., Dey S., Rao R., Raghunathan A., Lahiri K., 2001. Battery life
    estimation of mobile embedded systems. Proceedings of 14th International Conference on
    VLSI Design . pp. 57–63. Bangalore, India.
  18. Rakhmatov D., Vrudhula S., Wallach D. A., 2003. A model for battery lifetime analysis for organizing applications on a pocket computer. IEEE Trans. VLSI Syst. 11(6): pp. 1019–1030.
  19. Shi J., Peng Y., Erdem E., 2014. Simulation analysis on patient visit efficiency of a typical VA primary care clinic with complex characteristics. Simulation Modelling Practice and Theory 47: pp. 165–181.
  20. Tietjen T., Decker A., Müller D. H., 2011. FMEA Praxis: Das Komplettpaket für Training und
    Anwendung. 3. überarb. Aufl. München. Hanser.
  21. Yun L., Roychoudhury A., Mitra T., Herbstritt M., 2007. Timing Analysis of Body Area Network Applications. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany.