Abstract
As in many embedded systems domains, in modern healthcare we experience increasing adoption of (medical) cyber-physical systems of systems. In hospitals, for instance, different types of medical systems are integrated dynamically to render higher-level services in cooperation. One important task is the realization of smart alarms as well as, in a second step, the realization of automated interventions, such as the administration of specific drugs. A fundamental correlated problem is insufficient risk awareness, which are caused by fluctuating context conditions, insufficient context awareness, and a lack of reasoning capabilities to deduce the current risk. A potential solution to this problem is to make systems context- and risk-aware by introducing a runtime risk assessment approach. In this paper, we introduce such an approach for a wider identification of relevant risk parameters and risk assessment model building based on Bayesian Networks (BN). This model considers not only changes in the actual health status of the patient but also the changing capabilities to detect and react according to this status. This includes changing capabilities due to adding or removing different types of sensors (e.g. heart rate sensors) and replacing sensors of the same type but with other integrity level. In addition, we present an evaluation of the approach based on a simulated clinical environment for patient-controlled analgesia.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arney, D., et al.: Toward patient safety in closed-loop medical device systems. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems - ICCPS 2010, p. 139. ACM Press, New York (2010)
Brito, M., Griffiths, G.: A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions. Reliab. Eng. Syst. Saf. 146, 55–67 (2016)
Fenelon, P., et al.: Towards integrated safety analysis and design. ACM SIGAPP Appl. Comput. Rev. 2(1), 21–32 (1994)
Goldman, J.M.: Medical devices and medical systems - essential safety requirements for equipment comprising the patient-centric integrated clinical environment (ICE) - Part 1: general requirements and conceptual model (2009)
Jiang, Y., et al.: A self-adaptively evolutionary screening approach for sepsis patient. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, August 2016, pp. 60–65 (2016)
Kurd, Z., Kelly, T., McDermid, J., Calinescu, R., Kwiatkowska, M.: Establishing a framework for dynamic risk management in ‘intelligent’ aero-engine control. In: Buth, B., Rabe, G., Seyfarth, T. (eds.) SAFECOMP 2009. LNCS, vol. 5775, pp. 326–341. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04468-7_26
Leite, F.L., Schneider, D., Adler, R.: Dynamic risk management for cooperative autonomous medical cyber-physical systems. In: Gallina, B., Skavhaug, A., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2018. LNCS, vol. 11094, pp. 126–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99229-7_12
Leite, F.L., Adler, R., Feth, P.: Safety assurance for autonomous and collaborative medical cyber-physical systems. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10489, pp. 237–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66284-8_20
Lynn, L.A., Curry, J.P.: Patterns of unexpected in-hospital deaths: a root cause analysis. Patient Saf. Surg. 5(1), 3 (2011)
Maddox, R.R., et al.: Continuous respiratory monitoring and a “smart” infusion system improve safety of patient-controlled analgesia in the postoperative period. Agency for Healthcare Research and Quality (US), Rockville, MD, USA (2008)
McCarter, T., et al.: Capnography monitoring enhances safety of postoperative patient-controlled analgesia. Am. Heal. drug benefits. 1(5), 28–35 (2008)
Pajic, M., et al.: Model-driven safety analysis of closed-loop medical systems. IEEE Trans. Ind. inform. 10(1), 3–16 (2012)
Schneider, D., Trapp, M.: Conditional safety certification of open adaptive systems. ACM Trans. Auton. Adapt. Syst. 8(2), 1–20 (2013)
Stevens, N., et al.: Smart alarms: multivariate medical alarm integration for post CABG surgery patients. In: Proceedings of the 2nd ACM SIGHIT symposium on International health informatics - IHI 2012, p. 533. ACM Press, New York (2012)
Thieme, C.A., Utne, I.B.: A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 231(4), 446–464 (2017)
Wardziński, A.: Safety assurance strategies for autonomous vehicles. In: Harrison, M.D., Sujan, M.-A. (eds.) SAFECOMP 2008. LNCS, vol. 5219, pp. 277–290. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87698-4_24
Zeng, Z., Zio, E.: Dynamic risk assessment based on statistical failure data and condition-monitoring degradation data. IEEE Trans. Reliab. 67(2), 609–622 (2018)
Zio, E.: The future of risk assessment. Reliab. Eng. Syst. Saf. 177(March), 176–190 (2018)
Acknowledgments
The ongoing research that led to this paper is being funded by the Brazilian National Research Council (CNPq) under grant CSF 201715/2014-7 in cooperation with Fraunhofer IESE and TU Kaiserslautern. We would also like to thank Sonnhild Namingha for proofreading.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Leite, F.L., Schneider, D., Adler, R. (2019). Dynamic Risk Assessment Enabling Automated Interventions for Medical Cyber-Physical Systems. In: Romanovsky, A., Troubitsyna, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2019. Lecture Notes in Computer Science(), vol 11698. Springer, Cham. https://doi.org/10.1007/978-3-030-26601-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-26601-1_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26600-4
Online ISBN: 978-3-030-26601-1
eBook Packages: Computer ScienceComputer Science (R0)