Abstract
Clinical pathway can be used to reduce medical cost and improve medical efficiency. Traditionally, clinical pathways are designed by experts based on their experience. However, it is time consuming and sometimes not adaptive for specific hospitals, and mining clinical pathways from historic data can be helpful. Clinical pathway naturally can be regarded as a kind of process, and process mining can be used for clinical pathway mining. However, due to the complexity and dynamic of medical behaviors, traditional process mining methods often generate spaghetti-like clinical pathways with too many nodes and edges. To reduce the number of nodes in the resulting models, we put correlated events into clinical-event-packages as new units of log event for further mining. The experiment results has shown that our approach is a good way of generating more comprehensible clinical process as well as packages with better quality according to medical practitioners.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
These seven approaches are the \(\alpha \) algorithm, the \(\alpha \)++ algorithm, the Heuristic mining algorithm, the DWS-algorithm, the multiphase-algorithm, the genetic miner, and theory-of-regions-based algorithm.
- 2.
The categories of activities of billing data are standardized by national specification, for example, there are 14 categories in our dataset: “checking free”, “medicine costs”, “checkups fee”, “blood test fee”, “pathological examination fee”, “surgery fee”, “anesthesia fee”, “nursing fee”, “heating fee”, “bedding fee”, “treatment cost”, “western medicine cost”, “supplies of special fees”, and “other expenses”.
- 3.
We define these two concepts for the reason that for a given day trace we do not know the exact order of that each event happens (the order given by the system is surely unreliable), so we assume that the repeat of activities on a day should be removed and that we sort the sets under a unified standard.
- 4.
LOS: length of stay in the hospital.
- 5.
Due to space limitations, we only illustrate the main structure of the CEPM algorithm here.
- 6.
Patient Trace Coverage of an activity/package is the percentage of patients that the activity (package) was executed during his/her therapy among all patients.
- 7.
As mentioned in Sect. 4.1, the interference packages are packages that only has one event in it and appears only a couple of times in all event traces.
- 8.
Some of them are listed in Fig. 4(b) in detail.
References
Aalst, W., Desel, J., Oberweis, A. (eds.): Business Process Management. LNCS, vol. 1806. Springer, Heidelberg (2000)
Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75183-0_24
Lang, M., Bürkle, T., Laumann, S., et al.: Process mining for clinical processes: challenges and current limitations. In: EHealth Beyond the Horizon: Get IT There: Proceedings of MIE2008, the XXIst International Congress of the European Federation for Medical Informatics, p. 229. IOS Press (2008)
Gotz, D., Wang, F., Perer, A.: A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data[J]. J. Biomed. Inform. 48, 148–159 (2014)
Ayres, J., Flannick, J., Gehrke, J., et al.: Sequential pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 429–435. ACM (2002)
Huang, Z., Lu, X., Duan, H.: On mining clinical pathway patterns from medical behaviors. Artif. Intell. Med. 56(1), 35–50 (2012)
Zhang, Y., Padman, R., Wasserman, L.: On learning and visualizing practice-based clinical pathways for chronic kidney disease. In: AMIA Annual Symposium Proceedings, p. 1980. American Medical Informatics Association (2014)
Perer, A., Wang, F., Hu, J.: Mining and exploring care pathways from electronic medical records with visual analytics. J. Biomed. Inform. 56, 369–378 (2015)
Günther, C.W., Rozinat, A.: Disco: discover your processes. BPM (Demos) 940, 40–44 (2012)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceeding 20th International Conference Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
Acknowledgments
This work was supported by The National Key Tech- nology R&D Program (No. 2015BAH14F02), and Project 61325008 (Mining and Management of Large Scale Process Data) supported by NSFC.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Huang, H., Jin, T., Wang, J. (2017). Extracting Clinical-event-packages from Billing Data for Clinical Pathway Mining. In: Xing, C., Zhang, Y., Liang, Y. (eds) Smart Health. ICSH 2016. Lecture Notes in Computer Science(), vol 10219. Springer, Cham. https://doi.org/10.1007/978-3-319-59858-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-59858-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59857-4
Online ISBN: 978-3-319-59858-1
eBook Packages: Computer ScienceComputer Science (R0)