Abstract
Clinical care pathway analysis is the process of discovering how clinical activities impact patients in their care journeys, and uses the discovered knowledge for various applications including the redesign and optimization of clinical pathways. We present an approach for mining clinical care pathways correlated with patient outcomes that involves a combination of clustering, process mining and frequent pattern mining. Our approach is implemented as a set of interactive tools in the business process insight (BPI) platform, a a collaborative software as a service platform, that provides an event-driven process-aware analytics toolset. After interactively utilizing the individual clustering, process mining, and frequent pattern mining capabilities in BPI, users can overlay frequent patterns, ranked according to their correlation with a particular patient outcome, on a mined model of the patient population with that outcome. We have tested our approach for mining care pathways correlated with outcomes on electronic medical record data obtained from a US based healthcare provider on congestive heart failure (CHF) patients. Experimental results show that the tools we have developed and implemented can provide new insights to facilitate the improvement of existing clinical care pathways.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, San Francisco, CA, USA (1994)
Aiolli, F., Burattin, A., Sperduti, A.: A business process metric based on the alpha algorithm relations. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 141–146. Springer, Heidelberg (2012)
Weijters, A.J.M.M., van der Aalst, W., de Medeiros, A.A.: Process mining with the heuristics miner-algorithm. BETA Working Paper (2006)
C.P., et al.: Searching electronic health records for temporal patterns in patient histories: A case study with microsoft amalga. In: AMIA Annual Symposium, pp. 601–605 (2008)
Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential pattern mining using a bitmap representation. In: KDD, pp. 429–435. ACM Press (2002)
Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: Towards improving process mining results. In: SDM, pp. 401–412 (2009)
Jagadeesh Chandra Bose, R.P., van der Aalst, W.: Trace alignment in process mining: Opportunities for process diagnostics. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 227–242. Springer, Heidelberg (2010)
Caron, F., Vanthienen, J., De Weerdt, J., Baesens, B.: Advanced care-flow mining and analysis. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011 Workshops, Part I. LNBIP, vol. 99, pp. 167–168. Springer, Heidelberg (2012)
Cheng, H., Yan, X., Han, J., Yu, P.S.: Direct discriminative pattern mining for effective classification. In: ICDE, pp. 169–178 (2008)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. Computer (6), 226–231 (1996)
Fails, J.A., Karlson, A.K., Shahamat, L., Shneiderman, B.: A visual interface for multivariate temporal data: Finding patterns of events across multiple histories. In: IEEE VAST, pp. 167–174 (2006)
Goodman, S.N.: Toward evidence-based medical statistics. 1: The p value fallacy. Annals of Internal Medicine 130, 995–1004 (1999)
Greco, G., Guzzo, A., Pontieri, L., Saccá, D.: Mining expressive process models by clustering workflow traces. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 52–62. Springer, Heidelberg (2004)
Huang, Z., Lu, X., Duan, H.: On mining clinical pathway patterns from medical behaviors. Artif. Intell. Med. 56(1), 35–50 (2012)
Huang, Z., Lu, X., Duan, H.: Using recommendation to support adaptive clinical pathways. Journal of Medical Systems 36(3), 1849–1860 (2012)
Ireson, C.L.: Critical pathways: Effectiveness in achieving patient outcomes. Nursing Administration 27(6), 16–23 (1997)
Jung, J.-Y., Bae, J.: Workflow clustering method based on process similarity. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3981, pp. 379–389. Springer, Heidelberg (2006)
Kastner, M., Wagdy Saleh, M., Wagner, S., Affenzeller, M., Jacak, W.: Heuristic methods for searching and clustering hierarchical workflows. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 737–744. Springer, Heidelberg (2009)
Lakshmanan, G., Khalaf, R.: Leveraging process mining techniques to analyze semi-structured processes. IT Professional PP (99), 1–1 (2012)
Lang, M., Bürkle, T., Laumann, S., Prokosch, H.U.: Process mining for clinical workflows: Challenges and current limitations. In: MIE, pp. 229–234 (2008)
de Leoni, M., Adams, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Visual support for work assignment in process-aware information systems: Framework formalisation and implementation. Decision Support Systems 54(1), 345–361 (2012)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707–710 (1966)
Ren Lin, F., Chao Chou, S.: Mining time dependency patterns in clinical pathways. International Journal of Medical Informatics, 11–25 (2001)
Lo, D., Cheng, H.: Lucia: Mining closed discriminative dyadic sequential patterns. In: International Conference on Extending Database Technology, pp. 21–32 (2011)
Mans, R.S., Schonenberg, H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of process mining in healthcare - a case study in a dutch hospital. In: BIOSTEC (Selected Papers), pp. 425–438 (2008)
Mans, R., van der Aalst, W.M.P., Vanwersch, R.J.B., Moleman, A.J.: Process mining in healthcare: Data challenges when answering frequently posed questions. In: ProHealth/KR4HC, pp. 140–153 (2012)
Moskovitch, R., Shahar, Y.: Medical temporal-knowledge discovery via temporal abstraction. In: AMIA Annual Symposium, pp. 452–456 (2009)
Norén, G.N., Bate, A., Hopstadius, J., Star, K., Edwards, I.R.: Temporal pattern discovery for trends and transient effects: its application to patient records. In: SIGKDD, pp. 963–971. ACM (2008)
Perimal-Lewis, L.: Gaining insight from patient journey data using a process-oriented analysis approach. In: HIKM 2012, vol. 129, pp. 59–66 (2012)
Poelmans, J., Dedene, G., Verheyden, G., Van der Mussele, H., Viaene, S., Peters, E.: Combining business process and data discovery techniques for analyzing and improving integrated care pathways. In: Perner, P. (ed.) ICDM 2010. LNCS, vol. 6171, pp. 505–517. Springer, Heidelberg (2010)
Qiao, M., Akkiraju, R., Rembert, A.J.: Towards efficient business process clustering and retrieval: Combining language modeling and structure matching. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 199–214. Springer, Heidelberg (2011)
Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: A methodology based on process mining. Inf. Syst. 37(2), 99–116 (2012)
Rozsnyai, S., Lakshmanan, G.T., Muthusamy, V., Khalaf, R., Duftler, M.J.: Business process insight: An approach and platform for the discovery and analysis of end-to-end business processes. In: SRII Global Conference, pp. 80–89 (2012)
Silva, V., Fernando Chirigati, K.M.A.O., de Oliveira, D., Braganholo, V., Murta, L., Mattoso, M.: Similarity-based workflow clustering. Journal of Computational Interdisciplinary Sciences 2(1), 23–35 (2011)
Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009)
Weerdt, J.D., Caron, F., Vanthienen, J., Baesens, B.: Getting a grasp on clinical pathway data: An approach based on process mining. In: PAKDD Workshops, pp. 22–35 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lakshmanan, G.T., Rozsnyai, S., Wang, F. (2013). Investigating Clinical Care Pathways Correlated with Outcomes. In: Daniel, F., Wang, J., Weber, B. (eds) Business Process Management. Lecture Notes in Computer Science, vol 8094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40176-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-40176-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40175-6
Online ISBN: 978-3-642-40176-3
eBook Packages: Computer ScienceComputer Science (R0)