Testing Careflow Process Execution Conformance by Translating a Graphical Language to Computational Logic

  • Federico Chesani
  • Paola Mello
  • Marco Montali
  • Sergio Storari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4594)


Careflow systems implement workflow concepts in the clinical domain in order to administer, support and monitor the execution of health care services performed by different health care professionals and structures. In this work we focus on the monitoring aspects and propose a solution for the conformance verification of careflow process executions.

Given a careflow model, we have defined an algorithm capable of translating it to a formal language based on computational logic and abductive logic programming in particular. The main advantage of this formalism lies in its operational proof-theoretic counterpart, which is able to verify the conformance of a given careflow process execution (in the form of an event log) w.r.t. the model.

The feasibility of the approach has been tested on a case study related to the careflow process described in the cervical cancer screening protocol.


Careflow management Clinical practice guidelines Conformance verification Computational logic 


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  1. 1.
    Muir, G.: Evidence-based Healthcare. Churchill Livingston, London (1997)Google Scholar
  2. 2.
  3. 3.
    Chesani, F., Matteis, P.D., Mello, P., Montali, M., Storari, S.: A framework for defining and verifying clinical guidelines: A case study on cancer screening. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 338–343. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Alberti, M., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: Specification and verification of agent interactions using social integrity constraints. ENTCS 85(2) (2003)Google Scholar
  5. 5.
    Societies of computees (SOCS) Available at:
  6. 6.
    Jaffar, J., Maher, M.: Constraint logic programming: a survey. Journal of Logic Programming 19-20, 503–582 (1994)CrossRefMathSciNetGoogle Scholar
  7. 7.
    The SCIFF abductive proof procedure, Available at
  8. 8.
    Cervical cancer screening in emilia romagna (italy), Available at:
  9. 9.
  10. 10.
    Terenziani, P., Montani, S., Bottrighi, A., Torchio, M., Molino, G., Correndo, G.: Applying artificial intelligence to clinical guidelines: The GLARE approach. In: AI*IA. vol. 101, pp. 536–547 (2003)Google Scholar
  11. 11.
    Fox, J., Johns, N., Rahmanzadeh, A.: Disseminating medical knowledge-the proforma approach. Artificial Intelligence in Medicine 14, 157–181 (1998)CrossRefGoogle Scholar
  12. 12.
    Ciccarese, P., Caffi, E., Boiocchi, L., Quaglini, S., Stefanelli, M.: A guideline management system. In: MEDINFO 2004, pp. 28–32. IOS Press, Amsterdam (2004)Google Scholar
  13. 13.
    Mulyar, N.A., v.d. Aalst, W.M.P., Peleg, M.: A pattern-based analysis of clinical computer-interpretable guideline modelling languages. Technical Note (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Federico Chesani
    • 1
  • Paola Mello
    • 1
  • Marco Montali
    • 1
  • Sergio Storari
    • 2
  1. 1.DEIS – Università di Bologna, viale Risorgimento, 2 – 40136 – BolognaItaly
  2. 2.ENDIF – Università di Ferrara, Via Saragat, 1 – 44100 – FerraraItaly

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