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Journal of Medical Systems

, 41:193 | Cite as

A Knowledge-Modeling Approach to Integrate Multiple Clinical Practice Guidelines to Provide Evidence-Based Clinical Decision Support for Managing Comorbid Conditions

  • Samina Abidi
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

Clinical management of comorbidities is a challenge, especially in a clinical decision support setting, as it requires the safe and efficient reconciliation of multiple disease-specific clinical procedures to formulate a comorbid therapeutic plan that is both effective and safe for the patient. In this paper we pursue the integration of multiple disease-specific Clinical Practice Guidelines (CPG) in order to manage co-morbidities within a computerized Clinical Decision Support System (CDSS). We present a CPG integration framework—termed as COMET (Comorbidity Ontological Modeling & ExecuTion) that manifests a knowledge management approach to model, computerize and integrate multiple CPG to yield a comorbid CPG knowledge model that upon execution can provide evidence-based recommendations for handling comorbid patients. COMET exploits semantic web technologies to achieve (a) CPG knowledge synthesis to translate a paper-based CPG to disease-specific clinical pathways (CP) that include specialized co-morbidity management procedures based on input from domain experts; (b) CPG knowledge modeling to computerize the disease-specific CP using a Comorbidity CPG ontology; (c) CPG knowledge integration by aligning multiple ontologically-modeled CP to develop a unified comorbid CPG knowledge model; and (e) CPG knowledge execution using reasoning engines to derive CPG-mediated recommendations for managing patients with comorbidities. We present a web-accessible COMET CDSS that provides family physicians with CPG-mediated comorbidity decision support to manage Atrial Fibrillation and Chronic Heart Failure. We present our qualitative and quantitative analysis of the knowledge content and usability of COMET CDSS.

Keywords

Comorbidity Clinical practice guidelines Clinical decision support system Ontology Semantic web Usability evaluation 

Notes

Acknowledgements

This research has been supported by grant from Green Shield Canada Foundation.

Compliance with Ethical Standards

Conflict of Interests

The author declares that they have no conflicts of interest in the research.

References

  1. 1.
    Comorbidity Statistics (n.d). Centers for Disease Control and Prevention. Retrieved Oct. 11, 2017, from https://www.cdc.gov/arthritis/data_statistics/comorbidities.htm
  2. 2.
    Wang, T.J., Larson, M.G., Levy, D., Vasan, R.S., Leip, E.P., Wolf, P.A., et al., Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: The Framingham heart study. Circulation. 107(23):2920–2925, 2003.CrossRefPubMedGoogle Scholar
  3. 3.
    Wolff, J.L., Starfield, B., and Anderson, G., Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 162(20):2269–2276, 2002.CrossRefPubMedGoogle Scholar
  4. 4.
    Garcıa-Olmos, L., Salvado, C.H., Alberquilla, A., et al., Comorbidity patterns in patients with chronic diseases in general practice. PLoS One. 7(2):e32141, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Barnett, K., Mercer, S.W., Norbury, M., Watt, G., Wyke, S., and Guthrie, B., Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. Lancet. 380:37–43, 2012.CrossRefPubMedGoogle Scholar
  6. 6.
    Boyd, C., Darer, J., Boult, C., Fried, L., Boult, L., and Clinical, W.A., Practice guidelines and quality of care for older people with multiple co-morbid diseases. JAMA. 294:716–724, 2005.CrossRefPubMedGoogle Scholar
  7. 7.
    Woolf, S.H., Grol, R., Hutchinson, A., Eccles, M., and Grimshaw, J., Clinical guidelines: Potential benefits, limitations, and harms of clinical guidelines. BMJ. 318:527–530, 1999.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Guthrie, B., Payne, K., Alderson, P., McMurdo, M.E., and Mercer, S.W., Adapting clinical guidelines to take account of multimorbidity. BMJ. 345:e6341, 2012.CrossRefPubMedGoogle Scholar
  9. 9.
    Roland, M., and Paddison, C., Better management of patients with multimorbidity. BMJ. 346:f2510, 2013 May 2.CrossRefPubMedGoogle Scholar
  10. 10.
    Salisbury, C., Johnson, L., Purdy, S., Valderas, J., and Montgomery, A., Epidemiology and impact of multimorbidity in primary care. Br J Gen Pract. 61:e12–e21, 2011.CrossRefPubMedGoogle Scholar
  11. 11.
    Trafton, J.A., et al., Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain. Implement Sci : IS. 5:26, 2010.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    KON3 (Knowledge and ONtology on ONcology protocol). Retrieved on April 15th 2011, from http://www.koncube.org/index.php?lang=en
  13. 13.
    Bouamrane, M., Rector, R., and Hurrel, M., Using OWL ontologies for adaptive patient information modeling and preoperative clinical decision support. Knowl Inf Syst:1–14, 2010.Google Scholar
  14. 14.
    Prcela, M., Gamberger, D., and Jovic, A., Semantic web ontology utilization for heart failure expert system design. In: Andersen, S.K., et al. (Eds.), eHealth beyond the horizon-get IT there. IOS Press, Amsterdam, pp. 851–856, 2008.Google Scholar
  15. 15.
    Mabotuwana, T., and Warren, J., An ontology-based approach to enhance querying capabilities of general practice medicine for better management of hypertension. Artif Intell Med. 47(2):87–103, 2009.CrossRefPubMedGoogle Scholar
  16. 16.
    Abidi, S.R., Abidi, S.S.R., Hussain, S., and Shepherd, M., Ontology-based modeling of clinical practice guidelines: A clinical decision support system for breast cancer follow-up interventions at primary care setting. In: Kuhn, K., et al. (Eds.), MEDINFO (2007) 847–854. IOS Press, Amsterdam, 2007.Google Scholar
  17. 17.
    Abidi, S., Abidi, S.S.R., Hussain, S. & Butlor, L. Ontology-Based Modeling and Merging of Institution-Specific Prostate Cancer Clinical Pathways. Knowledge Management for Healthcare Processes Workshop at 18th European conference on artificial intelligence (ECAI 2008), Patras, (Greece)Google Scholar
  18. 18.
    Pinto, S.F., and Martins, J.P., Ontologies: How they can be built? Knowledge Inform Sys. 6:411–464, 2004.CrossRefGoogle Scholar
  19. 19.
    Kushnirk, A.W., and Patel, V.L., Cognitive and usability engineering methods for the evaluation of clinical information systems. J Biomed Inform. 37(1):56–76, 2004.CrossRefGoogle Scholar
  20. 20.
    Nielsen, J. (1994). Enhancing the explanatory power of usability heuristics. Proceedings of CHI 94 ACM Conference on Human Factors in Computer Systems. Boston, MA, US. April 24–28, 1994. Ed. Adelson, B., Dumais, S. & Olson, J. New York: ACM, 1994. 152–158Google Scholar
  21. 21.
    WHO global strategy on people-centered and integrated health services. Geneva: World Health Organization, 2015. Retrieved on Oct. 5 2017 from http://www.who.int/servicedeliverysafety/areas/people-centred-care/global-strategy/en/
  22. 22.
    Harrison, M.B., Graham, I.D., van den Hoek, J., Dogherty, E.J., Carley, M.E., and Angus, V., Guideline adaptation and implementation planning: A prospective observational study. Implement Sci. 8:49, 2013.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Fervers, B., Burgers, J.S., Haugh, M.C., et al., Adaptation of clinical guidelines: Literature review and proposition for a framework and procedure. Int J Qual Health Care. 18(3):167–176, 2006.CrossRefPubMedGoogle Scholar
  24. 24.
    Kidney Disease, Improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 3:1–150, 2013.CrossRefGoogle Scholar
  25. 25.
    National Clinical Guideline Centre. Chronic kidney disease: National clinical guideline for early identification and management in adults in primary and secondary care (update). National Institute for Health and Care Excellence, 2014. http://www.nice.org.uk/guidance/cg182/resources/cg182-chronic-kidneydisease-update-full-guideline3.
  26. 26.
    Real F and Riaño D. Automatic combination of formal intervention plans using SDA* representation model, in Proceedings of the 2007 conference on knowledge management for health care procedures, 2008, vol. Amsterdam, The Netherlands, pp. 75–86.Google Scholar
  27. 27.
    Real, F., and Riano, D., An autonomous algorithm for generating and merging clinical algorithms. In: Riaño, D. (Ed.), Knowledge Management for Health Care Procedures. Berlin / Heidelberg, Springer, pp. 13–24, 2009.CrossRefGoogle Scholar
  28. 28.
    Riaño D, Collado A. Model-Based Combination of Treatments for the Management of Chronic Comorbid Patients. 14th Int. Conf. on Artificial Intelligence in Medicine, AIME 2013, Murcia, Spain. In: Artificial Intelligence in Medicine. Springer LNAI 7885, 11–16.Google Scholar
  29. 29.
    Michalowski M, Wilk S, Michalowski W, Lin D, Farion K, Mohapatra S. In: Peek N, Morales RM, Peleg M, editors. Using constraint logic programming to implement iterative actions and numerical measures during mitigation of concurrently applied clinical practice guidelines; artificial intelligence in medicine, 14th conference on artificial intelligence in medicine, AIME 2013; Murcia, Spain. May/June 2013; springer; 2013. Pp. 17–22. Proceedings.Google Scholar
  30. 30.
    Wilk S, Michalowski M, Hing MM, Michalowski W and Farion K. Reconciliation of concurrently applied clinical practice guidelines using constraint logic programming, in Proceedings of the 6th international symposium on health informatics and bioinformatics, (HIBIT 2011), Izmir, Turkey, 2011, pp. 138–144.Google Scholar
  31. 31.
    Michalowski, M., Wilk, S., Tan, X., and Michalowski, W., First-Order Logic Theory for Manipulating Clinical Practice Guidelines Applied to Comorbid Patients. A Case Study AMIA Annu Symp Proc. 2014:892–898, 2014.PubMedGoogle Scholar
  32. 32.
    Jafarpour B, Abidi S, and Abidi SSR. Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines. IEEE Journal of Biomedical and Health Informatics. 2014:PP(99)Google Scholar
  33. 33.
    Heflin, J., and Hendler, J., A portrait of the semantic web in action. IEEE Intell Syst. 16(2):54–59, 2001.CrossRefGoogle Scholar
  34. 34.
    Grüninger M and Fox MS. Methodology for the design and evaluation of ontologies. Proc. Int’l Joint Conf. AI Workshop on Basic Ontological Issues in Knowledge Sharing, 1995.Google Scholar
  35. 35.
    Fernández, M., Gómez-Pérez, A., and Juristo, N.M.E.T.H.O.N.T.O.L.O.G.Y., From ontological art towards ontological engineering. Proc. AAAI Spring Symp. Series, AAAI Press. Menlo Park, Calif, pp. 33–40, 1997.Google Scholar
  36. 36.
    Arnold, J.M.O., et al., Canadian cardiovascular society consensus conference recommendations on heart failure 2006: Diagnosis and management. Can J Cardiol. 22(1):23–45, 2006.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Kerr C, Roy D. Canadian Cardiovascular Society Consensus Conference: Atrial Fibrillation 2004 executive summary. Retrieved April 21 2011, from http://www.ccs.ca/download/CCS_Consensus_Report.pdf
  38. 38.
    Roy, D., et al., Rhythm control versus rate control for atrial fibrillation and heart failure. N Engl J Med. 358(25):2667–2677, 2009.CrossRefGoogle Scholar
  39. 39.
    Fdez-Olivares J, Sánchez-Garzón I, González-Ferrer A, Cózar J, Fdez-Teijeiro A, Cabello M, Castillo L. Task network based modeling, dynamic generation and adaptive execution of patient-tailored treatment plans based on smart process management technologies. In: Riaño D, ten Teije A, Miksch C. (eds.) Knowledge representation for healthcare. KR4HC 2011. LNCS, vol. 6924, pp. 37–50. Springer, Berlin, Heidelberg (2011)Google Scholar
  40. 40.
    Yturralde, F.R., and Gaasch, W.H., Diagnostic criteria for diastolic heart failure. Prog Cardiovasc Dis. 47(5):341–319, 2005.CrossRefGoogle Scholar
  41. 41.
    Stevens, R., Goble, C.A., and Bechhofer, S., Ontology-based knowledge representation for bioinformatics. Brief Bioinform. 1(4):398–414, 2000.CrossRefPubMedGoogle Scholar
  42. 42.
    Boxwala, A.A., Peleg, M., Tu, S., Oqunyemi, O., Zeng, Q.T., Wang, D., et al., GLIF3: A representation format for sharable computer-interpretable clinical practice guidelines. J Biomed Inform. 37(3):147–161, 2004.CrossRefPubMedGoogle Scholar
  43. 43.
    De Clercq, P.A., Hasman, A., Blom, J.A., and Korsten, H.H.M., Design and implementation of a framework to support the development of clinical guidelines. Int J Med Inform. 64:285–318, 2001.CrossRefPubMedGoogle Scholar
  44. 44.
    Kong, G., Xu, D., and Yang, J., Clinical decision support systems: A review of knowledge representation and inference under uncertainties. International Journal of Computational Intelligence Systems. 1(2):159–167, 2008.Google Scholar
  45. 45.
    Green, L.A., and Seifert, C.M., Translation of research into practice: Why we Can’t “just do it”? J Am Board Fam Pract. 18:541–545, 2005.CrossRefPubMedGoogle Scholar
  46. 46.
    Uschold, M., and Gruninger, M., Ontologies: Principles, methods and applications. Knowl Eng Rev. 11(2):93–136, 1996.CrossRefGoogle Scholar
  47. 47.
    Abidi, Samina. A Knowledge Management Framework to Develop, Model, ALign and Operationalize Clinical Pathways to Provide Decision Support for Comorbid Diseases. Diss. Dalhousie University. Accessed on October 7 2017 from http://dalspace.library.dal.ca/handle/10222/13009
  48. 48.
    Abidi S. A Knowledge Management Framework to Develop, Model, Align and Operationalize Clinical Pathways to Provide Decision Support for Comorbid Diseases. Diss. Dalhousie University, Halifax, 2010. Faculty of Graduate Studies Online Theses. Web. Sept. 20 2015.Google Scholar
  49. 49.
    Danyal A, Abidi SR & Abidi SSR. (2009). Computerizing Clinical Pathways: Ontology-Based Modeling and Execution. K.-P. Adlassnig et al. (Eds). Medical Informatics in a United and Healthy Europe (pp. 643–647). IOS PressGoogle Scholar
  50. 50.
    Wei, H., and Yuzhong Qu, K., Discovering simple mappings between relational database schemas and ontologies. In: Aberer et al. (Eds.): ISWC/ASWC 2007, LNCS 4825, pp. 225–238, 2007. Springer-Verlag Berlin Heidelberg, 2007.Google Scholar
  51. 51.
    Chen, H., Wang, Y., Wang, H., Mao, Y., Tang, J., Zhou, C., Yin, A., and Wu, Z., Towards a semantic web of relational databases: A practical semantic toolkit and an in-use case from traditional Chinese medicine. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., and Aroyo, L. (Eds.), ISWC 2006. LNCS. Vol. 4273. Springer, Heidelberg, pp. 750–763, 2006.CrossRefGoogle Scholar
  52. 52.
    Chen, H., Wu, Z., Wang, H., and Mao, Y., RDF/RDFS-based relational database integration. In: ICDE 2006. Proceedings of the 22nd international conference on data engineering, p. 94, 2006.Google Scholar
  53. 53.
    Dragut, E., and Lawrence, R., Composing mappings between schemas using a reference ontology. In: ODBASE 2004. Proceedings of international conference on ontologies databases and applications of semantics, pp. 783–800, 2004.Google Scholar
  54. 54.
    Papapanagiotou, P., Katsiouli, P., Tsetsos, V., Anagnostopoulos, C., and Hadjiefthymiades, S., RONTO: Relational to ontology schema matching. AIS SIGSEMIS BULLETIN. 3(3–4):32–36, 2006.Google Scholar
  55. 55.
    Ruttenberg, A., Clark, T., Bug, W., Samwald, M., Bodenreider, O., Chen, H., Doherty, D., Forsberg, K., Gao, Y., Kashyap, V., Kinoshita, J., Luciano, J., Marshall, M.S., Ogbuji, C., Rees, J., Stephens, S., Wong, G.T., Wu, E., Zaccagnini, D., Hongsermeier, T., Neumann, E., Herman, I., and Cheung, K., Advancing translational research with the semantic web. BMC Bioinformatics. 8(3):S2, 2007.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Gómez-Pérez, A., Ontology Evaluation. Handbook of Ontologies. Springer, Berlin, pp. 251–271, 2004.CrossRefGoogle Scholar
  57. 57.
    Abidi, S., Stewart, S., Shepherd, M., and Abidi, R., Usability evaluation of family physicians’ interaction with COMET: Comorbidity ontological modeling and ExecuTion system. MEDINFO 2013. Aug. 20–23. Denmark, Copenhagen, 2013.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Medical Informatics, Faculty of MedicineDalhousie UniversityHalifaxCanada

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