Abstract
Clinicians can benefit from automated support to guideline (GL) application at the point of care. However, several conceptual dimensions should be considered for a realistic application: 1) The representation of the knowledge might be through structured text (semi-formal), or specified in a machine-comprehensible language (formal); 2) The availability of electronic patient data might be partial or full; 3) GL-based recommendations might be triggered by a human-initiated (synchronous) session, or data–driven (asynchronous). In addition, several requirements must be fulfilled, such as an evaluation of the GL application engine by a GL simulation engine. Finally, to apply multiple GLs, by multiple users, in multiple settings, the GL-application engine should be designed as an enterprise architecture that can plug into any Electronic Medical Record (EMR). We present an architecture fulfilling these desiderata, describe application examples with different conceptual dimensions and requirements, using our new GL-application engine, PICARD, discuss lessons learned, and briefly describe a clinical evaluation of the current framework in the domain of pre-eclampsia/toxemia of pregnancy.
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References
Qualigini, S., Ciccarese, P., Micieli, G., Cavallini, A.: Non-compliance with guidelines: motivations and consequences in a case study. In: Proceedings of Symposium on Computerized Guidelines and Protocols (CGP 2004), Studies in Health Technology and Informatics, Prague, Czech Republic, vol. 101, pp. 75–87. IOS Press (2004)
Peleg, M., Tu, S.W., Bury, J., Ciccarese, P., Fox, J., Greenes, R.A., Hall, R., Johnson, P.D., Jones, N., Kumar, A., Miksch, S., Quaglini, S., Seyfang, A., Shortliffe, E.H., Stefanelli, M.: Comparing Computer-Interpretable Guideline Models: A Case-Study Approach. JAMIA 10(1), 52–68 (2002)
De Clercq, P.A., Blom, J.A., Korsten, H.H., Hasman, A.: Approaches for creating computer-interpretable guidelines that facilitate decision support. J. Artif. Intell. Med. 31(1), 1–27 (2004)
Isern, D., Moreno, A.: Computer-based execution of clinical guidelines: a review. Int. J. Med. Inform. 77(12), 787–808 (2008)
Fox, J., Glasspool, D., Grecu, D., Modgil, S., South, M., Patkar, V.: Argumentation-Based Inference and Decision Making - A Medical Perspective. IEEE Intelligent Systems 22, 34–41 (2007)
Latoszek-Berendsen, A., Tange, H., van den Herik, H.J., Hasman, A.: From clinical practice guidelines to computer-interpretable guidelines. A literature overview. J. Methods Inf. Med. 49(6), 550–570 (2010)
Anselma, L., Terenziani, P., Montani, S., Bottrighi, A.: Towards a comprehensive treatment of repetitions, periodicity and temporal constraints in clinical guidelines. J. Artif. Intell. Med. 38(2), 171–195 (2006)
Goud, R., Hasman, A., Peek, N.: Development of a guideline-based decision support system with explanation facilities for outpatient therapy. Comput. eMethods Programs Biomed. 91(2), 145–153 (2008)
Peleg, M., Shachak, A., Wang, D., Karnieli, E.: Using multi-perspective methodologies to study users’ interactions with the prototype front end of a guideline-based decision support system for diabetic foot care. Int. J. Med. Inform. 78(7), 482–493 (2009)
Wang, D., Peleg, M., Tu, S.W., Boxwala, A.A., Ogunyemi, O., Zeng, Q., Greenes, R.A., Patel, V.L., Shortliffe, E.H.: Design and implementation of the GLIF3 guideline execution engine. J. Biomed. Inform. 37(5), 305–318 (2004)
Young, O., Shahar, Y., Liel, Y., Lunenfeld, E., Bar, G., Shalom, E., Martins, S.B., Vaszar, L.T., Marom, T., Goldstein, M.K.: Runtime application of Hybrid-Asbru clinical guidelines. J. Biomed. Inform. 40(5), 507–526 (2007)
Chan, A.S., Coleman, R.W., Martins, S.B., Advani, A., Musen, M.A., Bosworth, H.B., Oddone, E.Z., Shlipak, M.G., Hoffman, B.B.: Evaluating provider adherence in a trial of a guideline-based decision support system for hypertension. Stud. Health Technol. Inform. 107(Pt 1), 125–129 (2007)
Fox, J., Patkar, V., Thomson, R.: Decision Support for Healthcare: the PROforma evidence base. Inf. Prim. Care 14(1), 49–54 (2006)
De Clercq, P.A., Hasman, A., Blom, J.A., Korsten, H.H.: Design and implementation of a framework to support the development of clinical guidelines. Int. J. Med. Inform. 64(2-3), 285–318 (2001)
Tu, S.W., Campbell, J.R., Glasgow, J., et al.: The SAGE Guideline Model: Achievements and overview. J. Am. Med. Inform. Assoc. 14(5), 589–598 (2007)
Seyfang, A., Paesold, M., Votruba, P., Miksch, S.: Improving the execution of clinical guidelines and temporal data abstraction high-frequency domains. Stud. Health Technol. Inform. 139, 263–272 (2008)
Eccher, C., Seyfang, A., Ferro, A., Miksch, S.: Embedding oncologic protocols into the provision of care: the Oncocure project. Stud. Health Technol. Inform. 150, 663–667 (2009)
Choi, J., Currie, L.M., Wang, D., Bakken, S.: Encoding a clinical practice guideline using guideline interchange format: a case study of a depression screening and management guideline. Int. J. Med. Inform. 76(suppl. 2), S302–S307 (2007)
Grando, A., Peleg, M., Glasspool, D.: A goal-oriented framework for specifying clinical guidelines and handling medical errors. J. Bio. Inform. 43(2), 287–299 (2010)
Isern, D., Moreno, A., Sanchez, D., Hajnal, A., Pedone, G., Varga, L.Z.: Agent-based execution of personalised home care treatments. J. App. Intelligence 34, 155–180 (2011)
Isern, D., Senchez, D., Moreno, A.: Ontology-driven execution of clinical guidelines. Comput. Meth. Prog. Biomed. (2011), doi:10.1016/j.cmpb.2011.06.006
Shahar, Y., Miksch, S., Johnson, P.: The Asgaard project: A task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artif. Intell. Med. (14), 29–51 (1998)
Microsoft Developer Network, http://msdn.microsoft.com/en-us/netframework/aa663324
Shahar, Y., Young, O., Shalom, E., Galperin, M., Mayaffit, A., Moskovitch, R., Hessing, A.: A framework for a distributed, hybrid, multiple-ontology clinical-guideline library and automated guideline-support tools. J. Biomed. Inform. 37(5), 325–344 (2004)
Hatsek, A., Shahar, Y., Taieb-Maimon, M., Shalom, E., Klimov, D., Lunenfeld, E.: A Scalable Architecture for Incremental Specification and Maintenance of Procedural and Declarative Clinical Decision-Support Knowledge. The Open Medical Informatics Journal 4, 255–277 (2010)
Shalom, E., Shahar, Y., Taieb-Maimon, M., Bar, G., Young, O., Martins, B.S., Vaszar, L., Liel, Y., Leibowitz, A., Marom, T., Lunenfeld, E.: A quantitative evaluation of a methodology for collaborative specification of clinical guidelines at multiple representation levels. Journal of Biomedical Informatics 41(6) (2008)
Boaz, D., Shahar, Y.: A framework for distributed mediation of temporal-abstraction queries to clinical databases. Artificial Intelligence in Medicine 34(1), 3–24 (2005)
German, E., Leibowitz, A., Shahar, Y.: An architecture for linking medical decision-support applications to clinical databases and its evaluation. JBI 42(2), 203–218 (2009)
Shalom, E.: A multi-dimensional framework for realistic application of clinical guidelines. Ph.D. Thesis Dissertation, Department of Information Systems Engineering, Ben Gurion University, Beer Sheva, Israel (submitted, 2012)
Friedman, I., Shalom, E., Shahar, Y.: Evaluation of guideline-application engines by longitudinal simulation: A position paper. In: Proceedings of the 3rd International Workshop on Knowledge Representation for Health Care (KR4HC 2011), Bled, Slovenia (2011)
MobiGuide FP7 EU project (FP7-287811) (2012), http://www.mobiguide-project.eu
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Shalom, E., Fridman, I., Shahar, Y., Hatsek, A., Lunenfeld, E. (2013). Towards a Realistic Clinical-Guidelines Application Framework: Desiderata, Applications, and Lessons Learned. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., ten Teije, A. (eds) Process Support and Knowledge Representation in Health Care. ProHealth KR4HC 2012 2012. Lecture Notes in Computer Science(), vol 7738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36438-9_4
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