Abstract
Clinical pathways are among the main tools used to manage the quality in health-care concerning the standardization of care processes. This paper deals with a recommendation service to support adaptive clinical pathways. The proposed approach can guide physicians in clinical pathways by providing recommendations on possible next steps based on the measurement of the target patient status and medical knowledge from completed clinical cases. The efficiency and usability of the proposed method is validated by experiments referring to a real data set extracted from Electronic Patient Records. The experimental results indicate that the recommendation service can provide its users with advice rationales that remain consistent even when patient status has changed. This makes adaptive clinical pathways possible.
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Clinical Pathway, http://en.wikipedia.org/wiki/Clinical_pathway. Last access at 24 December 2010.
Hunter, B., and Segrott, J., Re-mapping client journeys and professional identities: A review of the literature on clinical pathways. Int. J. Nurs. Stud. 45:608–625, 2008.
Weiland, D. E., Why use clinical pathways rather than practice guidelines? Am. J. Surg. 174:592–595, 1997.
Uzark, K., Clinical pathways for monitoring and advancing congenital heart disease care. Prog. Pediatr. Cardiol. 18:131–139, 2003.
Loeb, M., Carusone, S. C., Goeree, R., Walter, S. D., Brazil, K., Krueger, P., Simor, A., Moss, L., and Marrie, T., Effect of a clinical pathway to reduce hospitalizations in nursing home residents with pneumonia. J. Am. Med. Assoc. 295:2503–2510, 2006.
Lenz, R., Blaser, R., Beyer, M., Heger, O., Biber, C., BAumlein, M., and Schnabel, M., IT support for clinical pathways-lessons learned. Int. J. Med. Inform. 76(3):S397–S402, 2007.
Lee, K. H., and Anderson, Y., The association between clinical pathways and hospital length of stay: a case study. J. Med. Syst. 31:79–83, 2007. doi:10.1007/s10916-006-9045-9.
Zand, D. J., Brown, K. M., Konecki, U. L., Campbell, J. K., Salehi, V., and Chamberlain, J. M., Effectiveness of a clinical pathway for the emergency treatment of patients with inborn errors of metabolism. Pediatrics 122:1191–1195, 2008.
Cardoen, B., and Demeulemeester, E., Capacity of clinical pathwaysa strategic multi-level evaluation tool. J. Med. Syst. 32:443–452, 2008. doi:10.1007/s10916-008-9150-z.
Lin, Y. K., Chen, C. P., Tsai, W. C., Chiao, Y .C., and Lin, B., Cost-effectiveness of clinical pathway in coronary artery bypass surgery. J. Med. Syst. 1–11, 2009. doi:10.1007/s10916-009-9357-7.
Lenz, R., and Reichert, M., IT support for healthcare processes-premises, challenges, perspectives. Data Knowl. Eng. 61(1):39–58, 2007.
Westbrook, J. I., Coiera, E. W., Gosling, A. S., and Braithwaite, J., Critical incidents and journey mapping as techniques to evaluate the impact of online evidence retrieval systems on health care delivery and patient outcomes. Int. J. Med. Inform. 76:234–245, 2007.
Ying, M. K., and Sadahiro, S. J., Charting by variance. J. Am. Diet. Assoc. 96:A34, 1996.
Lin, F., Chou, S., Pan, S., and Chen, Y., Mining time dependency patterns in clinical pathways. Int. J. Med. Inform. 62:11–25, 2001.
Okita, A., et. al., Variance analysis of a clinical pathway of video-assisted single lobectomy for lung cancer. Surg. Today 39(2):104–109, 2009.
Davis, J. T., Allen, H. D., Felver, K., Rummell, H. M., Powers, J. D., and Cohen, D. M., Clinical pathways can be based on acuity, not diagnosis. Ann. Thorac. Surg. 59:1074–1078, 1995.
Chu, S., and Cesnik, B., Improving clinical pathway design: lessons learned from a computerised prototype. Int. J. Med. Inform. 51:1–11, 1998.
Wakamiya, S., and Yamauchi, K., What are the standard functions of electronic clinical pathways? Int. J. Med. Inform. 78:543–550, 2009.
Walldal, E., Anund, I., and Furaker, C., Quality of care and development of a critical pathway. J. Nurs. Manag. 10:115–122, 2002.
Pearson, K. C., Role of evidence-based medicine and clinical practice guidelines in treatment decisions. Clin. Ther. 20:C80–C85, 1998.
Du, G., Jiang, Z., Diao, X., Yao Y., Knowledge extraction algorithm for variances handling of cp using integrated hybrid genetic double multi-group cooperative pso and dpso. J. Med. Syst. 1–16, 2010. doi:10.1007/s10916-010-9562-4.
Campbell, H., Hotchkiss, R., Bradshaw, N., and Porteous, M., Integrated care pathways. Br. Med. J. 316:133–137, 1998.
Fox, R., Moran, S., and MacCormick, A., Guidance for integrated care pathways: a reference document for an acute NHS trust. Journal of Integrated Care Pathways 7:100–106, 2003.
Chinese Clinial Pathways. http://www.ch-cp.com/. Last access at 24 December 2010.
Duftschmid, G., and Miksch. S., Knowledge-based verification of clinical guidelines by detection of anomalies. Artif. Intell. Med. 22(1):23–41, 2001 (Workflow Management and Clinical Guidelines).
Isern, D., and Moreno, A., Computer-based execution of clinical guidelines: a review. Int. J. Med. Inform. 77(12):787–808, 2008.
Goud, R., Hasman, A., and Peek, N., Development of a guideline-based decision support system with explanation facilities for outpatient therapy. Comput. Methods Programs Biomed. 91:145–153, 2008.
Chen, C., Chen, K., Hsu, C. Y, Chiu, W. T., and Li, Y. C., A guideline-based decision support for pharmacological treatment can improve the quality of hyperlipidemia management. Comput. Methods Programs Biomed. 97:280–285, 2010.
Asbru, http://www.openclinical.org/gmm_asbru.html. Last access at 30 November 2010.
GUIDE, http://www.openclinical.org/gmm_guide.htmll. Last access at 30 November 2010.
PROforma, http://www.openclinical.org/gmm_proforma.html. Last access at 30 November 2010.
Tu, S. W., Musen, M. A., A flexible approach to guideline modeling. In: AMIA Symposium, 1999.
Tu, S. W., Musen, M. A., From guideline modeling to guideline execution: defining guideline-based decision-support services. In: AMIA Symposium, 2000.
GLIF, http://www.openclinical.org/gmm_glif.htmll. Last access at 30 November 2010.
Peleg, M., Tu, S. W., Bury, J., Ciccarese, P., et al., Comparing computer-interpretable guideline models: A case-study approach. J. Am. Med. Inform. Assoc. 10(1):52–68, 2003.
Quaglini, S., Stefanelli, M., Lanzola, G., Caporusso, V., and Panzarasa, S., Flexible guideline-based patient careflow systems. Artif. Intell. Med. 22(1):65–80, 2001 (Workflow Management and Clinical Guidelines).
Alexandrou, D., Skitsas, I., Mentzas, G., A holistic environment for the design and execution of self-adaptive clinical pathways. IEEE Trans. Inf. Technol. Biomed., doi:10.1109/TITB.2010.2074205.
Ye, Y., Jiang, Z., Diao, D., Yang, D., and Du, G., An ontology-based hierarchical semantic modeling approach to clinical pathway workflows. Comput. Biol. Med. 39:722–732, 2009.
Schuld, J., Schafer, T., Nickel, S., Jacob, P., Schilling, M. K., and Richter, S., Impact of it-supported clinical pathways on medical staff satisfaction. a prospective longitudinal cohort study. Int. J. Med. Inform., doi:10.1016/j.ijmedinf.2010.10.012.
Wakamiya, S., and Yamauchi, K., What are the standard functions of electronic clinical pathways? Int. J. Med. Inform., 78(8):543–550, 2009.
BPMN, http://www.bpmn.org/. Last access at 30 November 2010.
BPEL, http://en.wikipedia.org/wiki/Business_Process_Execution_Language. Last access at 30 November 2010.
van der Aalst, W. M. P., and van Hee, K. M., Workflow Management: M odels, M ethods, and S ystems. Cambridge, MA: MIT Press, 2002.
Chung, P. W. H., Cheung, L., Stader, J., Jarvis, P., Moore, J., and Macintosh, A., Knowledge-based process management - an approach to handling adaptive workflow. Knowl.-Based Syst. 16(3):149–160, 2003.
Ellis, C. A., Keddara, K., Rozenberg, G., Dynamic change within workflow systems. In: the Conference on Organizational Computing Systems, ACM SIGOIS, 1995.
Reichert, M., Dadam, P., Adeptex: supporting dynamic changes of workflow without loosing control. Journal of Intelligent Information Systems 10:93–129, 1998.
Rinderle, S., Reichert, M., Dadam P., Correctness criteria for dynamic changes in workflow systems: a survey. Data Knowl. Eng. 50:9–34, 2004.
van der Aalst, W. M. P., Adams, M., ter Hofstede, A. H. M., Pesic, M., and Schonenberg, H., Flexibility as a service. Technical report, Eindhoven University of Technology, 2008.
Du, G., Jiang, Z., Diao, X., Yan, Y., and Yao, Y., Variances handling method of clinical pathways based on t-s fuzzy neural networks with novel hybrid learning algorithm. J. Med. Syst. 1–18, 2010. doi:10.1007/s10916-010-9589-6.
Cercone, N., An, A., and Chan, C., Rule-induction and case-based reasoning: Hybrid architectures appear adavantageous. IEEE Trans. Knowl. Data Eng. 11:166–175, 1999.
Adlassnig, K. P., Combi, C., Das, A. K., Keravnou, E. T., and Pozzi, G., Temporal representation and reasoning in medicine: research directions and challenges. Artif. Intell. Med. 38(2):101–13, 2006.
Peleg, M., and Tu, S. W., Design patterns for clinical guidelines. Artif. Intell. Med. 47(1):1–24, 2009.
Pawlak, Z., Rough sets. Int. J. Comput. Inf. Sci. 11:341–356, 1982.
Wojciech, Z., and Shan, N., Discovering attribute relationships, dependencies and rules by using rough sets. In: Proceedings of the 28th Annual Hawaii International Conference on System Sciences (HICSS’95), pp. 293–299, 1995.
Pawlak, Z., Rough sets and intelligent data analysis. Inf. Sci. 147:1–12, 2002.
Pawlak, Z., and Skowron, A., Rough sets: some extensions. Inf. Sci. 177:28–40, 2007.
Pawlak, Z., and Skowron, A., Rudiments of rough sets. Inf. Sci. 177:3–27, 2007.
Wang, X., Yang, J., Jensen, R., and Liu, X., Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Comput. Methods Programs Biomed. 83:147–156, 2006.
Yang, H., and Wu, C., Rough sets to help medical diagnosis - evidence from a taiwan’s clinic. Expert Syst. Appl. 36:9293–9298, 2009.
Sintchenko, V., Iredell, J. R., Gilbert, G. L., and Coiera, E., Handheld computer-based decision support reduces patient length of stay and antibiotic prescribing in critical care. J. Am. Med. Inform. Assoc. 12(4):398–402, 2005.
Acknowledgements
The author is especially thankful to the endocrinology department of Chinese Huzhou Center hospital for the positive support despite their hard work, and more particularly to all medical staff involved.
The authors would like to thank the anonymous reviewers for their constructive comments on an earlier draft of this paper.
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Huang, Z., Lu, X. & Duan, H. Using Recommendation to Support Adaptive Clinical Pathways. J Med Syst 36, 1849–1860 (2012). https://doi.org/10.1007/s10916-010-9644-3
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DOI: https://doi.org/10.1007/s10916-010-9644-3