Cybersecurity design considerations for cross-boundary clinical decision support

  • Obinna Anya
  • Hissam Tawfik
  • Mohammed M. AlaniEmail author
  • Jia Hu
Original Article


The idea of cross-boundary clinical decision support has the potential to transform the design of open data and decision support platforms through a connected system that allows for harnessing of information and peer opinion across geographical and organizational boundaries for more effective decision making. In health care, cross-boundary clinical decision systems pose a major challenge from the perspective of e-security design. When clinical decision support systems, which essentially enable the transfer and storage of patient data, become cross-boundary systems, the protection of this data at different storage locations and in transit becomes more challenging. In this paper, we present a model of awareness for cross-boundary clinical decision support, which takes account of the concept of work practice as a design feature for enabling context-aware information sharing and secured health data management in cross-boundary clinical decision support. The proposed model is based on the practice theoretic paradigm and draws from a notion of context awareness as an interaction problem with a view to representing work practices as a context parameter for the design of computational systems for cross-boundary decision support. We illustrate how the approach addresses key security and privacy challenges in clinical decision support systems for cross-boundary support.


Clinical decision support Cross-boundary e-health Patient data Practice-centered awareness Context-aware design Conceptual framework Security and privacy challenges 



  1. 1.
    Abidi SSR (2006) Healthcare knowledge sharing: purpose, practices and prospects. In: Bali R, Dwivedi A (eds) Healthcare knowledge management: issues, advances and successes. Springer, New York, pp 65–86Google Scholar
  2. 2.
    Allert H, Richter C (2008) Practices, systems, and context working as core concepts in modeling socio-technical systems. In: Proceedings of of the 5th international workshop on philosophy and informatics, WSPI’08Google Scholar
  3. 3.
    Anya O (2012) Practice-centred e-health system design for cross-boundary clinical decision support. University of LiverpoolGoogle Scholar
  4. 4.
    Anya O, Tawfik H, Amin S, Nagar A, Shaalan K (2010) Context-aware knowledge modelling for decision support in e-health. In: 2010 International joint conference on neural networks (IJCNN), pp 1–7Google Scholar
  5. 5.
    Anya O, Tawfik H, Naguib RNG (2018) Applying the practice theoretical perspective to healthcare knowledge management. In: Theories to inform superior health informatics research and practice, pp 375–390Google Scholar
  6. 6.
    Anya O, Tawfik H (2017) Designing for practice-based context-awareness in ubiquitous e-health environments. J Comput Electr Eng 61(2017):312–326CrossRefGoogle Scholar
  7. 7.
    Anya O, Tawfik H, Al-Jumeily D (2015) Context-aware clinical knowledge sharing in cross-boundary e-health: a conceptual model. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computingGoogle Scholar
  8. 8.
    Appari A, Eric Johnson M (2010) Information security and privacy in healthcare: current state of research. Int J Internet Enterp Manag 6(4):279–314CrossRefGoogle Scholar
  9. 9.
    Batra I, Luhach AK, Pathak N (2016) Research and analysis of lightweight cryptographic solutions for internet of things. In: Proceedings of the second international conference on information and communication technology for competitive strategies, no. 23Google Scholar
  10. 10.
    Clancey WJ, Sachs P, Sierhuis M, van Hoof R (1998) Brahms: simulating practice for work systems design. Int J Hum Comput Stud 49:831–865CrossRefGoogle Scholar
  11. 11.
    Corno F, Guercio E, De Russis L, Gargiulo E (2015) Designing for user confidence in intelligent environments. J Reliab Intell Environ 1:11–21CrossRefGoogle Scholar
  12. 12.
    Crabtree A, Lodge T, Colley J et al (2018) Building accountability into the Internet of Things: the IoT Databox model. J Reliab Intell Environ 4(39):39–55CrossRefGoogle Scholar
  13. 13.
    De Florio V (2015) On resilient behaviors in computational systems and environments. J Reliab Intell Environ 1(33):33–46CrossRefGoogle Scholar
  14. 14.
    Dhillon PK, Kalra S (2018) Multi-factor user authentication scheme for IoT-based healthcare services. J Reliab Intell Environ 4(141):141–160CrossRefGoogle Scholar
  15. 15.
    Dourish P, Grinter RE, Delgado De La Flor J, Joseph M (2004) Security in the wild: user strategies for managing security as an everyday, practical problem. Pers Ubiquitous Comput 8(6):391–401CrossRefGoogle Scholar
  16. 16.
    Dourish P (2004) What we talk about when we talk about context. Pers Ubiquitous Comput 8(1):19–30CrossRefGoogle Scholar
  17. 17.
    Endsley MR, Bolstad CA, Jones DG, Riley JM (2003) Situation awareness oriented design: from user’s cognitive requirements to creating effective supporting technologies. In: Human factors and ergonomics 47th annual meeting, DenverGoogle Scholar
  18. 18.
    Engeström Y (1987) Learning by expanding: an activity-theoretical approach to developmental work research. Orienta-konsultit, HelsinkiGoogle Scholar
  19. 19.
    England DA, Taleb-Bendiab A, Lisboa P, Murphy K, Jarman I (2004) Decision support for post-operative breast cancer care, coping with complexity: sharing new approaches for the design of human–computer systems in complex settings. University of Bath, BathGoogle Scholar
  20. 20.
    Gabbay J, Le May (2011) Practice-based evidence for healthcare: clinical mindlines. Routledge, LondonGoogle Scholar
  21. 21.
    Giunchiglia F, Maltese V, Dutta B (2012) Domains and context: first steps towards managing diversity in knowledge. J Web Semant 12–13:53–63CrossRefGoogle Scholar
  22. 22.
    Karacapilidis N (2006) An overview of future challenges of decision support technologies. In: Gupta J, Forgionne G, Mora M (eds) Intelligent decision-making support systems: foundations, applications and challenges. Springer, London, pp 385–399CrossRefGoogle Scholar
  23. 23.
    Kirsh D (2001) The context of work. Hum Comput Interact 16(2–4):305–322CrossRefGoogle Scholar
  24. 24.
    Kock N (ed) (2008) Encyclopedia of e-Collaboration. IGI Global Publishers, New YorkGoogle Scholar
  25. 25.
    Kuziemsky CE, Varpio L (2011) A model of awareness to enhance our understanding of interprofessional collaborative care delivery and health information system design to support it. Int J Med Inform 80(8):150–160CrossRefGoogle Scholar
  26. 26.
    Liu X, Lu R, Ma J, Chen L, Qin B (2016) Privacy-preserving patient-centric clinical decision support system on naïve Bayesian classification. IEEE J Biomed Health Inf 20(2):655–668CrossRefGoogle Scholar
  27. 27.
    Manadhata P, Wing JM (2004) Measuring a system’s attack surface. Technical Report CMUCS-04-102. School of Computer Science, Carnegie Mellon University, PittsburghGoogle Scholar
  28. 28.
    McCarthy J, Hayes PJ (1969) Some philosophical problems from the standpoint of artificial intelligence. In: Meltzer B, Michie D (eds) Machine intelligence, vol 4. Edinburgh University Press, pp 463–502Google Scholar
  29. 29.
    Mejia DA, Favela J, Morán AL (2010) Understanding and supporting lightweight communication in hospital work. IEEE Trans Inf Technol Biomed 14(1):140–146CrossRefGoogle Scholar
  30. 30.
    Nunes VT, Santoro FM, Borges MRS (2009) A context-based model for knowledge management embodied in work processes. J Inf Sci 179:2538–2554CrossRefGoogle Scholar
  31. 31.
    Patkar V, Acosta D, Davidson T, Jones A, Fox J, Keshtgar M (2011) Cancer multidisciplinary team meetings: evidence, challenges, and the role of clinical decision support technology. Int J Breast Cancer 2011:831605CrossRefGoogle Scholar
  32. 32.
    Porzel R (2011) Contextual computing: models and applications. Springer, BerlinCrossRefzbMATHGoogle Scholar
  33. 33.
    Preuveneers D, Joosen W (2016) Semantic analysis and verification of context-driven adaptive applications in intelligent environments. J Reliab Intell Environ 2(2):53–73CrossRefGoogle Scholar
  34. 34.
    Resmini A, Rosati L (2011) Pervasive information architecture: designing cross-channel user experiences. ElsevierGoogle Scholar
  35. 35.
    Respício A, Adam F, Phillips-Wren G, Teixeira C, Telhada J (2010) Bridging the socio-technical gap in decision support systems: challenges for the next decade, Frontiers in Artificial Intelligence and Applications, vol 212. IOS Press, AmsterdamzbMATHGoogle Scholar
  36. 36.
    Sandell P (2007) Framework for securing personal health data in clinical decision support systems. J Health Inf Manag Spring 21(2):34–40Google Scholar
  37. 37.
    Tawfik H, Anya O, Nagar AK (2012) Understanding clinical work practices for cross-boundary decision support in e-health. IEEE Trans Inf Technol Biomed 16(4):530–541CrossRefGoogle Scholar
  38. 38.
    Vithanwattana N, Mapp G, George C (2017) Developing a comprehensive information security framework for mHealth: a detailed analysis. J Reliab Intell Environ 3(21):21–39CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Google Inc.Mountain ViewUSA
  2. 2.School of Computing, Creative Technology and EngineeringLeeds Beckett UniversityLeedsUK
  3. 3.Department of Information TechnologyKhawarizmi International CollegeAbu DhabiUAE
  4. 4.College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK

Personalised recommendations