Complex Decision Support in Practice

  • Prakash M. Nadkarni
Part of the Health Informatics book series (HI)


In the last three chapters, we discussed some of the issues that make the field of decision support challenging, as well as the theoretical foundations of several widely used approaches to decision support. Business workflow technology has been emphasized because it is much more mature (if pricey and proprietary), as a basis for decision support, than similar approaches that are now being explored in the medical area. While practitioners of medicine have long claimed that the problems of medicine are unique, the assertion of exceptionalism can be made truthfully for almost any complex domain, whether it involves the construction of aircraft or the operations of a multi-national investment firm. Proper operations of business workflows that involve large financial transactions is every bit as important to the organization’s well-being as the proper management of patients in a healthcare organization. My belief is that reusing and adapting ideas that have worked elsewhere is likely to be more productive in the long term than trying to rediscover first principles.


Object Constraint Language Unify Medical Language System Simple Object Access Protocol Common Object Request Broker Architecture Distribute Computing Environment 


  1.  1.
    Miller R, Masarie FE, Myers JD. Quick medical reference (QMR) for diagnostic assistance. MD Comput. 1986;3(5):34-48.PubMedGoogle Scholar
  2.  2.
    Warner HR Jr. Iliad: moving medical decision-making into new frontiers. Methods Inf Med. 1989;28(4):370-372.PubMedGoogle Scholar
  3.  3.
    Peleg M, Tu S, Bury J, et al. Comparing computer-interpretable guideline models: a case-study approach. J Am Med Inform Assoc. 2003;10(1):52-68.PubMedCrossRefGoogle Scholar
  4.  4.
    Boxwala AA, Peleg M, Tu S, et al. GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J Biomed Inform. 2004;37(3):147-161.PubMedCrossRefGoogle Scholar
  5.  5.
    Wikipedia. IntelliSense. Available from: Cited 3/2/2010.
  6.  6.
    Tu SW, Musen MA, Shankar R, et al. Modeling guidelines for integration into clinical workflow. Stud Health Technol Inform. 2004;107(Pt 1):174-178.PubMedGoogle Scholar
  7.  7.
    Tu SW, Campbell JR, Glasgow J, et al. The SAGE Guideline Model: achievements and ­overview. J Am Med Inform Assoc. 2007;14(5):589-598.PubMedCrossRefGoogle Scholar
  8.  8.
    Ram P, Berg D, Mansfield G, et al. Executing clinical practice guidelines with the SAGE execution engine. Stud Health Technol Inform. 2004;107(251–5):251-255.PubMedGoogle Scholar
  9.  9.
    Sordo M, Ogunyemi O, Boxwala AA, Greenes RA. GELLO: an object-oriented query and expression language for clinical decision support. AMIA Annu Symp Proc. 2003:1012.Google Scholar
  10. 10.
    Warmer JB, Kleppe AG. The Object Constraint Language: Precise Modeling with Uml, Addison-Wesley Object Technology Series (Paperback). Reading: Addison-Wesley; 1998.Google Scholar
  11. 11.
    Demuth B. The Dresden OCL toolkit and the business rules approach. Available from: Cited 7/6/2010.
  12. 12.
    Chiorean D, Bortes M, Corutiu D. Proposals for a widespread use of OCL. Workshop on tool support for OCL and related formalisms; 2005; EPFL, Montego Bay, Jamaica.Google Scholar
  13. 13.
    Kawamoto K, Lobach DF. Design, implementation, use, and preliminary evaluation of SEBASTIAN, a standards-based Web service for clinical decision support. AMIA Annu Symp Proc. 2005:380–384.Google Scholar
  14. 14.
    Kawamoto K, Lobach D. Proposal for fulfilling strategic objectives of the U.S. Roadmap for national action on decision support through a service-oriented architecture leveraging HL7 services. J Am Med Inform Assoc. 2007;14:146-155.PubMedCrossRefGoogle Scholar
  15. 15.
    Kawamoto K, Honey A, Rubin K. The HL7-OMG healthcare services specification project: motivation, methodology, and deliverables for enabling a semantically interoperable service-oriented architecture for healthcare. J Am Med Inform Assoc. 2009;16(6):874-881.PubMedCrossRefGoogle Scholar
  16. 16.
    Kay R. QuickStudy: representational state transfer (REST). Available from: Cited 11/2/10.
  17. 17.
    Martin R. Web services: hope or hype? Available from: Cited 4/6/10.

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Prakash M. Nadkarni
    • 1
  1. 1.School of MedicineYale UniversityNew HavenUSA

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