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ICT-powered Health Care Processes

(Position Paper)
  • Marco Carbone
  • Anders Skovbo Christensen
  • Flemming Nielson
  • Hanne R. Nielson
  • Thomas Hildebrandt
  • Martin Sølvkjær
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8315)

Abstract

The efficient use of health care ressources requires the use of Information and Communication Technology (ICT). During a treatment process, patients have often been tested and partially treated with different diagnoses in mind before the precise diagnosis is identified. To use ressources well it becomes necessary to adapt the prescribed treatments to make use of the tests and partial treatments already performed, rather than always starting from square one. We propose to facilitate this through the design of declarative process models accounting for the involvement of distributed groups of medical specialists and the adaptation of treatments, and through the evaluation of the trustworthiness of models taking account of test results and actual treatments compared to the clinical guidelines.

Keywords

Clinical guidelines declarative and stochastic process models adaptability trustworthiness 

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References

  1. 1.
    Baier, C., Katoen, J.-P.: Principles of Model Checking. MIT Press (2008)Google Scholar
  2. 2.
    Bartak, R.: Modelling soft constraints: A survey. Neural Network World 12, 421–431 (2002)Google Scholar
  3. 3.
    Bates, D.W., Cohen, M., Leape, L.L., Overhage, J.M., Shabot, M.M., Sheridan, T.: White paper - reducing the frequency of errors in medicine using information technology. Journal of the American Medical Informatics Association 8(4), 299–308 (2001)CrossRefGoogle Scholar
  4. 4.
    Berg, M.: The search for synergy: interrelating medical work and patient care information systems. Methods of Information in Medicine 42, 337–344 (2003)Google Scholar
  5. 5.
    Bernstein, K., Bruun-Rasmussen, M., Vingtoft, S., Andersen, S.K., Nohr, C.: Modelling and implementing electronic health records in denmark. Stud. Health Technol. Inform. 95, 245–250 (2003)Google Scholar
  6. 6.
    Bhatti, R., Bertino, E., Ghafoor, A.: A trust-based context-aware access control model for web-services. Distrib. Parallel Databases 18(1), 83–105 (2005)CrossRefGoogle Scholar
  7. 7.
    Chesani, F., Mello, P., Montali, M., Storari, S.: Testing careflow process execution conformance by translating a graphical language to computational logic. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 479–488. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    European Commission. ehealth action plan 2012-2020 - innovative healthcare for the 21st century. Webpage (December 2012), https://ec.europa.eu/digital-agenda/en/news/ehealth-action-plan-2012-2020-innovative-healthcare-21st-century
  9. 9.
    de Moura, L.M., Bjørner, N.: Satisfiability modulo theories: introduction and applications. Commun. ACM 54(9), 69–77 (2011)CrossRefGoogle Scholar
  10. 10.
    Divitini, M., Simone, C.: Supporting different dimensions of adaptability in workflow modeling. Computer Supported Cooperative Work 9(3-4), 365–397 (2000)CrossRefGoogle Scholar
  11. 11.
    Dong, G., Hull, R., Kumar, B., Su, J., Zhou, G.: A framework for optimizing distributed workflow executions. In: Connor, R.C.H., Mendelzon, A.O. (eds.) DBPL 1999. LNCS, vol. 1949, pp. 152–167. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Dumas, M., van der Aalst, W.M., ter Hofstede, A.H.: Process Aware Information Systems: Bridging People and Software Through Process Technology. Wiley-Interscience (2005)Google Scholar
  13. 13.
    Fahland, D.: Towards analyzing declarative workflows. In: Koehler, J., Pistore, M., Sheth, A.P., Traverso, P., Wirsing, M. (eds.) Autonomous and Adaptive Web Services. Dagstuhl Seminar Proceedings, vol. 07061, p. 6. Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl (2007)Google Scholar
  14. 14.
    Grimshaw, J., Eccles, M., Thomas, R., Mac Lennan, G., Ramsay, C., Fraser, C., Vale, L.: Toward evidence-based quality improvement. evidence (and its limitations) of the effectiveness of guideline dissemination and implementation strategies 1966-1998. J. Gen. Intern. Med. 21, 14–20 (2006)Google Scholar
  15. 15.
    Grol, R., Grimshaw, J.: From best evidence to best practice: effective implementation of change in patients’ care. The Lancet 362(9391), 1225–1230 (2003)CrossRefGoogle Scholar
  16. 16.
    Nielson, H.R., Nielson, F., Vigo, R.: A calculus for quality. In: Păsăreanu, C.S., Salaün, G. (eds.) FACS 2012. LNCS, vol. 7684, pp. 188–204. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  17. 17.
    Hildebrandt, T., Mukkamala, R.R., Slaats, T.: Designing a cross-organizational case management system using dynamic condition response graphs. In: 2011 15th IEEE International on Enterprise Distributed Object Computing Conference (EDOC), August 29-September 2, pp. 161–170 (2011)Google Scholar
  18. 18.
    Hildebrandt, T., Mukkamala, R.R.: Declarative event-based workflow as distributed dynamic condition response graphs. In: Post-proceedings of PLACES 2010 (2010)Google Scholar
  19. 19.
    Hildebrandt, T., Mukkamala, R.R., Slaats, T.: Declarative modelling and safe distribution of healthcare workflows. In: International Symposium on Foundations of Health Information Engineering and Systems, Johannesburg, South Africa (August 2011)Google Scholar
  20. 20.
    Honda, K., Yoshida, N., Carbone, M.: Multiparty asynchronous session types. In: POPL, pp. 273–284 (2008)Google Scholar
  21. 21.
    Hull, R.: Formal study of business entities with lifecycles: Use cases, abstract models, and results. In: Proceedings of 7th International Workshop on Web Services and Formal Methods. LNCS, vol. 6551 (2010)Google Scholar
  22. 22.
    Rahmanzadeh, A., Fox, J., Johns, N.: Disseminating medical knowledge: the proforma approach. Artificial Intelligence in Medicine 14, 157–182 (1998)CrossRefGoogle Scholar
  23. 23.
    Kawamoto, K., Houlihan, C.A., Andrew Balas, E., Lobach, D.F.: Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 330(7494), 765 (2005)CrossRefGoogle Scholar
  24. 24.
    Kindler, E., Martens, A., Reisig, W.: Inter-operability of workflow applications: Local criteria for global soundness. In: van der Aalst, W.M.P., Desel, J., Oberweis, A. (eds.) Business Process Management. LNCS, vol. 1806, pp. 235–253. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  25. 25.
    Klein, M., Dellarocas, C., Bernstein, A.: Introduction to the special issue on adaptive workflow systems. Computer Supported Cooperative Work 9(3-4), 265–267 (2000)CrossRefGoogle Scholar
  26. 26.
    Lenz, R., Blaser, R., Beyer, M., Heger, O., Biber, C., Baumlein, M., Schnabel, M.: It support for clinical pathways–lessons learned. International Journal of Medical Informatics 76(suppl. 3), S397–S402 (2007); Ubiquity: Technologies for Better Health in Aging Societies - MIE 2006Google Scholar
  27. 27.
    Lyng, K.M., Hildebrandt, T., Mukkamala, R.R.: From paper based clinical practice guidelines to declarative workflow management. In: Proceedings of 2nd International Workshop on Process-oriented information systems in healthcare (ProHealth 2008), Milan, Italy, pp. 36–43. BPM 2008 Workshops (2008)Google Scholar
  28. 28.
    Lyng, K.M.: Clinical guidelines in everyday praxis, implications for computerization. Journal of Systems and Information Technology (2009)Google Scholar
  29. 29.
    Maggi, F.M., Montali, M., Westergaard, M., van der Aalst, W.M.P.: Monitoring business constraints with linear temporal logic: An approach based on colored automata. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 132–147. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  30. 30.
    Milner, R.: Communicating and Mobile Systems: the Pi-Calculus. Cambridge University Press (1999)Google Scholar
  31. 31.
    Møller, N.H., Bjørn, P.: Layers in sorting practices: Sorting out patients with potential cancer. Computer Supported Cooperative Work 20, 123–153 (2011)CrossRefGoogle Scholar
  32. 32.
    Montali, M.: Specification and Verification of Declarative Open Interaction Models: A Logic-Based Approach. LNBIP, vol. 56. Springer (2010)Google Scholar
  33. 33.
    Mukkamala, R.R.: A Formal Model For Declarative Workflows - Dynamic Condition Response Graphs. PhD thesis, IT University of Copenhagen (March 2012) (forthcomming)Google Scholar
  34. 34.
    Mukkamala, R.R., Hildebrandt, T., Tøth, J.B.: The resultmaker online consultant: From declarative workflow management in practice to ltl. In: Proceedings of the 2008 12th Enterprise Distributed Object Computing Conference Workshops, EDOCW 2008, pp. 135–142. IEEE Computer Society, Washington, DC (2008)Google Scholar
  35. 35.
    Mulyar, N., Pesic, M., van der Aalst, W.M., Peleg, M.: Towards the flexibility in clinical guideline modelling languages. BPM Center Report (Ext. rep. BPM-07-04) 8 (2007)Google Scholar
  36. 36.
    Mulyar, N., Pesic, M., van der Aalst, W.M.P., Peleg, M.: Declarative and procedural approaches for modelling clinical guidelines: Addressing flexibility issues. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 335–346. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  37. 37.
    Myers, A.C., Liskov, B.: A decentralized model for information flow control. In: SOSP, pp. 129–142 (1997)Google Scholar
  38. 38.
    Nielson, F., Nielson, H.R., Hankin, C.: Principles of program analysis (2. corr. print). Springer (2005)Google Scholar
  39. 39.
    Object Management Group BPMN Technical Committee. Business Process Model and Notation, version 2.0. Webpage (January 2011), http://www.omg.org/spec/BPMN/2.0/PDF
  40. 40.
    Pesic, M., Schonenberg, M.H., Sidorova, N., van der Aalst, W.M.P.: Constraint-based workflow models: Change made easy. In: Meersman, R., Tari, Z. (eds.) OTM 2007, Part I. LNCS, vol. 4803, pp. 77–94. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  41. 41.
    Pesic, M.: Constraint-Based Workflow Management Systems: Shifting Control to Users. PhD thesis, Eindhoven University of Technology, Netherlands (2008)Google Scholar
  42. 42.
    Quaglini, S., Stefanelli, M., Lanzola, G., Caporusso, V., Panzarasa, S.: Flexible guideline-based patient careflow systems. Artificial Intelligence in Medicine 22(1), 65–80 (2001); Workflow Management and Clinical GuidelinesCrossRefGoogle Scholar
  43. 43.
    Reichert, M.U., Bauer, T., Dadam, P.: Flexibility for distributed workflows. In: Handbook of Research on Complex Dynamic Process Management: Techniques for Adaptability in Turbulent Environments, pp. 32–171. IGI Global, Hershey (2009)Google Scholar
  44. 44.
    Robertson, D.: A lightweight coordination calculus for agent systems. In: Leite, J., Omicini, A., Torroni, P., Yolum, p. (eds.) DALT 2004. LNCS (LNAI), vol. 3476, pp. 183–197. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  45. 45.
    SENSORIA. Software engineering for service-oriented overlay computers (2010), http://www.sensoria-ist.eu
  46. 46.
    Smith, T.J., Hillner, B.E.: Ensuring quality cancer care by the use of clinical practice guidelines and critical pathways. Journal of Clinical Oncology 19(11), 2886–2897 (2001)Google Scholar
  47. 47.
    Terenziani, P., Montani, S., Bottrighi, A., Torchio, M., Molino, G., Correndo, G.: The glare approach to clinical guideline: Main features. Symposium on Computerized Guidelines and Protocols 101, 62–66 (2004)Google Scholar
  48. 48.
    van der Aalst, W., Pesic, M., Schonenberg, H., Westergaard, M., Maggi, F.M.: Declare. Webpage (2010), http://www.win.tue.nl/declare/
  49. 49.
    van der AAlst, W.M.P., Jablonski, S., Jablonski, S.: Dealing with workflow change: identification of issues and solutions. International Journal of Computer Systems Science & Engineering 15(5), 267–276 (2000)Google Scholar
  50. 50.
    van der Aalst, W.M.P., Lohmann, N., Massuthe, P., Stahl, C., Wolf, K.: Multiparty Contracts: Agreeing and Implementing Interorganizational Processes. The Computer Journal 53(1), 90–106 (2010)CrossRefGoogle Scholar
  51. 51.
    van der Aalst, W.M.P., Pesic, M., Schonenberg, H.: Declarative workflows: Balancing between flexibility and support. Computer Science - R&D 23(2), 99–113 (2009)Google Scholar
  52. 52.
    van der Aalst, W.M.P., Pesic, M.: DecSerFlow: Towards a truly declarative service flow language. In: Bravetti, M., Núñez, M., Zavattaro, G. (eds.) WS-FM 2006. LNCS, vol. 4184, pp. 1–23. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  53. 53.
    van der Aalst, W.M.P.: Inheritance of interorganizational workflows: How to agree to disagree without loosing control? Information Technology and Management 4, 345–389 (2003)CrossRefGoogle Scholar
  54. 54.
    Vanderaalst, W., Weske, M., Grunbauer, D.: Case handling: a new paradigm for business process support. Data & Knowledge Engineering 53(2), 129–162 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Marco Carbone
    • 1
  • Anders Skovbo Christensen
    • 3
  • Flemming Nielson
    • 2
  • Hanne R. Nielson
    • 2
  • Thomas Hildebrandt
    • 1
  • Martin Sølvkjær
    • 3
  1. 1.IT University of CopenhagenCopenhagenDenmark
  2. 2.DTU ComputeLyngbyDenmark
  3. 3.IMT Region H, Innovation & ConfigurationCopenhagenDenmark

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