research-article

Mining processes in dentistry

Authors:

Michiel van Genuchten,

Daniel WismeijerAuthors Info & Claims

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

Pages 379 - 388

https://doi.org/10.1145/2110363.2110407

Published: 28 January 2012 Publication History

Abstract

Business processes in dentistry are quickly evolving towards 'digital dentistry'. This means that many steps in the dental process will increasingly deal with computerized information or computerized half products. A complicating factor in the improvement of process performance in dentistry, however, is the large number of independent dental professionals that are involved in the entire process. In order to reap the benefits of digital dentistry, it is essential to obtain an accurate view on the current processes in practice. In this paper, so called process mining techniques are applied in order to demonstrate that, based on automatically stored data, detailed process knowledge can be obtained on dental processes, e.g. it can be discovered how dental processes are actually executed. To this end, we analyze a real case of a private dental practice, which is responsible for the treatment of patients (diagnosis, placing of implants and the placement of the final restoration), and the dental lab that is responsible for the production of the final restoration. To determine the usefulness of process mining, the entire process has been investigated from three different perspectives: (1) the control-flow perspective, (2) the organizational perspective and (3) the performance perspective. The results clearly show that process mining is useful to gain a deep understanding of dental processes. Also, it becomes clear that dental process are rather complex, which require a considerable amount of flexibility. We argue that the introduction of workflow management technology is needed in order to make digital dentistry a success.

References

[1]

W.M.P. van der Aalst. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer-Verlag, Berlin, 2011.

Digital Library

[2]

W.M.P. van der Aalst, H.A. Reijers, and M.S. Song. Discovering Social Networks from Event Logs. Computer Supported Cooperative Work, 14(6):549--593, 2005.

Digital Library

[3]

W.M.P. van der Aalst, B.F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A.J.M.M. Weijters. Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering, 47(2):237--267, 2003.

Digital Library

[4]

W.M.P. van der Aalst, A.J.M.M. Weijters, and L. Maruster. Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering, 16(9):1128--1142, 2004.

Digital Library

[5]

R. Agrawal, D. Gunopulos, and F. Leymann. Mining Process Models from Workflow Logs. In Sixth International Conference on Extending Database Technology, pages 469--483, 1998.

Digital Library

[6]

A. Datta. Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches. Information Systems Research, 9(3):275--301, 1998.

Digital Library

[7]

P.C. Diniz and D.R. Ferreira. Automatic Extraction of Process Control Flow from I/O Operations. In M. Dumas, M. Reichert, and M.-C. Shan, editors, Proceedings of the Business Process Management 6th International Conference, BPM 2008, Milan, Italy, September 2--4, 2008, volume 5240 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, 2008.

Digital Library

[8]

B.F. van Dongen and W.M.P. van der Aalst. Multi-Phase Process Mining: Building Instance Graphs. In P. Atzeni, W. Chu, H. Lu, S. Zhou, and T.W. Ling, editors, International Conference on Conceptual Modeling (ER 2004), volume 3288 of Lecture Notes in Computer Science, pages 362--376. Springer-Verlag, Berlin, 2004.

[9]

M. Dumas, W.M.P. van der Aalst, and A.H.M. ter Hofstede. Process-Aware Information Systems: Bridging People and Software through Process Technology. Wiley & Sons, 2005.

Digital Library

[10]

J. Ghattas, M. Peleg, P. Soffer, and Y. Denekamp. Learning the Context of a Clinical Process . In S. Rinderle-Ma, S. Sadiq, and F. Leymann, editors, BPM 2009 International Workshops, Ulm, Germany, September 7, 2009. Revised Papers, volume 43 of Lecture Notes in Business Information Processing, pages 545--556. Springer-Verlag, Berlin, 2010.

[11]

J. Herbst. A Machine Learning Approach to Workflow Management. In Proceedings 11th European Conference on Machine Learning, volume 1810 of Lecture Notes in Computer Science, pages 183--194. Springer-Verlag, Berlin, 2000.

Digital Library

[12]

C. Li, M. Reichert, and A. Wombacher. Mining Business Process Variants: Challenges, Scenarios, Algorithms. Data & Knowledge Engineering, 70(5):409--434, 2011.

Digital Library

[13]

L. T. Ly, S. Rinderle, P. Dadam, and M. Reichert. Mining Staff Assignment Rules from Event-Based Data. In C. Bussler et al., editor, Business Process Management 2005 Workshops, volume 3812 of Lecture Notes in Computer Science, pages 177--190. Springer-Verlag, Berlin, 2006.

Digital Library

[14]

R.S. Mans, M.H. Schonenberg, G. Leonardi, S. Panzarasa, S. Quaglini, and W.M.P. van der Aalst. Process Mining Techniques : An Application to Stroke Care. In S.K. Andersen et al., editor, eHealth Beyond the Horizon - Get IT There (Proceedings 21st International Congress of the European Federation for Medical Informatics, MIE 2008, volume 136 of Studies in Health Technology and Informatics, pages 573--578. IOS Press, 2008.

[15]

R.S. Mans, M.H. Schonenberg, M.S. Song, W.M.P. van der Aalst, and P.J.M. Bakker. Process Mining in Healthcare: a Case Study. In L. Azevedo and A.R. Londral, editors, Proceedings of the First International Conference on Health Informatics (HEALTHINF 2008), volume 1, pages 118--125. INSTICC Press, 2008.

[16]

R.S. Mans, W.M.P. van der Aalst, N.C. Russell, P.J. Bakker, and A.J. Moleman. Lightweight Interacting Patient Treatment Processes. Accepted for Publication in the International Journal of Knowledge-Based Organizations (IJKBO), 2012. Accepted for publication.

[17]

J. Poelmans, G. Dedene, G. Verheyden, H. van der Mussele, S. Viaene, and E. Peters. Combining Business Process and Data Discovery Techniques for Analyzing and Improving Integrated Care Pathways. In P. Perner, editor, Advances in Data Mining. Applications and Theoretical Aspects 10th Industrial Conference, ICDM 2010, volume 6171 of Lecture Notes in Computer Science, pages 505--517. Springer-Verlag, Berlin, 2010.

Digital Library

[18]

A. Rebuge and D.R. Ferreira. Business Process Analysis in Healthcare Environments: A Methodology Based on Process Mining. Information Systems, 2011. In Press.

Digital Library

[19]

W. Reisig. Petri Nets: An Introduction, volume 4 of EATCS Monographs in Theoretical Computer Science. Springer-Verlag, Berlin, 1985.

Digital Library

[20]

A. Rozinat, R.S. Mans, M.S. Song, and W.M.P. van der Aalst. Discovering Simulation Models. Information Systems, 34(3):305--327, 2009.

Digital Library

[21]

A. Rozinat and W.M.P. van der Aalst. Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems, 33:64--95, 2008.

Digital Library

[22]

Y. Shan, D. Jeacocke, D.W. Murray, and A. Sutinen. Mining Medical Specialist Billing Patterns for Health Service Management. In J.F. Roddick, J. Li, P. Christen, and P.J. Kennedy, editors, Conferences in Research and Practice in Information Technology, volume 87, pages 105--110, 2008.

Digital Library

[23]

M.S. Song and W.M.P. van der Aalst. Supporting Proces Mining by Showing Events at a Glance. In K. Chari and A. Kumar, editors, Proceedings of the Seventeenth Annual Workshop on Information Technologies and Systems (WITS 2007), pages 139--145, 2007.

[24]

M.S. Song and W.M.P. van der Aalst. Towards Comprehensive Support for Organizational Mining. Decision Support Systems, 46(1):300--317, 2008.

Digital Library

[25]

A. Tahmaseb, R. de Clerck, and D. Wismeijer. Computer Guided Implant Placement: 3D Planning Software, Fixed Intraoral Reference Points and CAD/CAM Technology. A Case Report. The International Journal of Oral and Maxillofacial Implants, 24(3), 2009.

[26]

J.M.E.M. van der Werf, B.F. van Dongen, C.A.J. Hurkens, and A. Serebrenik. Process Discovery using Integer Linear Programming. Fundamentae Informaticae, 94(3--4):387--412, 2009.

Digital Library

[27]

A.J.M.M. Weijters and W.M.P. van der Aalst. Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering, 10(2):151--162, 2003.

[28]

L. Wen, J. Wang, W.M.P. van der Aalst, B. Huang, and J. Sun. A Novel Approach for Process Mining Based on Event Types. Journal of Intelligent Information Systems, 32(2):163--190, 2009.

Digital Library

[29]

W.-S. Yang and S.-Y. Hwang. A Process-Mining Framework for the Detection of Healthcare Fraud and Abuse. Expert Systems with Applications, 31:56--68, 2006.

Cited By

Muyama LNeuraz ACoulet A(2024)Machine learning approaches for the discovery of clinical pathways from patient data: A systematic reviewJournal of Biomedical Informatics10.1016/j.jbi.2024.104746160(104746)Online publication date: Dec-2024
https://doi.org/10.1016/j.jbi.2024.104746
Ali MMilani FDumas M(2024)Data-Driven Identification and Analysis of Waiting Times in Business ProcessesBusiness & Information Systems Engineering10.1007/s12599-024-00868-5Online publication date: 15-May-2024
https://doi.org/10.1007/s12599-024-00868-5
Farhat LSeleme ACarvalho DVianna GOrsi J(2023)MINERAÇÃO DE PROCESSOS PARA AVALIAÇÃO DA DEMANDA POR SERVIÇOS ODONTOLÓGICOSRevista Contemporânea10.56083/RCV3N9-0573:9(14543-14564)Online publication date: 15-Sep-2023
https://doi.org/10.56083/RCV3N9-057
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Mining processes in dentistry

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Published In

cover image ACM Conferences

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

January 2012

914 pages

ISBN:9781450307819

DOI:10.1145/2110363

General Chairs:
Gang Luo
IBM Research, USA
,
Jiming Liu
Hong Kong Baptist University
,
Program Chair:
Christopher C. Yang
Drexel University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGHIT: ACM Special Interest Group on Health Informatics

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Published: 28 January 2012

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IHI '12: ACM International Health Informatics Symposium

January 28 - 30, 2012

Florida, Miami, USA

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Muyama LNeuraz ACoulet A(2024)Machine learning approaches for the discovery of clinical pathways from patient data: A systematic reviewJournal of Biomedical Informatics10.1016/j.jbi.2024.104746160(104746)Online publication date: Dec-2024
https://doi.org/10.1016/j.jbi.2024.104746
Ali MMilani FDumas M(2024)Data-Driven Identification and Analysis of Waiting Times in Business ProcessesBusiness & Information Systems Engineering10.1007/s12599-024-00868-5Online publication date: 15-May-2024
https://doi.org/10.1007/s12599-024-00868-5
Farhat LSeleme ACarvalho DVianna GOrsi J(2023)MINERAÇÃO DE PROCESSOS PARA AVALIAÇÃO DA DEMANDA POR SERVIÇOS ODONTOLÓGICOSRevista Contemporânea10.56083/RCV3N9-0573:9(14543-14564)Online publication date: 15-Sep-2023
https://doi.org/10.56083/RCV3N9-057
Rismanchian FKassani SShavarani SLee Y(2023)A Data-Driven Approach to Support the Understanding and Improvement of Patients’ Journeys: A Case Study Using Electronic Health Records of an Emergency DepartmentValue in Health10.1016/j.jval.2022.04.00226:1(18-27)Online publication date: Jan-2023
https://doi.org/10.1016/j.jval.2022.04.002
Pecoraro FLuzi D(2022)Using Unified Modeling Language to Analyze Business Processes in the Delivery of Child Health ServicesInternational Journal of Environmental Research and Public Health10.3390/ijerph19201345619:20(13456)Online publication date: 18-Oct-2022
https://doi.org/10.3390/ijerph192013456
Fox FWhelton HJohnson OAggarwal V(2022)Dental Extractions under General Anesthesia: New Insights from Process MiningJDR Clinical & Translational Research10.1177/238008442210888338:3(267-275)Online publication date: 11-Apr-2022
https://doi.org/10.1177/23800844221088833
McInerney CMcCrorie CBenn JHabli ILawton TMebrahtu TRandell RSheikh NJohnson O(2022)Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocolBMJ Open10.1136/bmjopen-2021-05409012:3(e054090)Online publication date: 1-Mar-2022
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Pradana MKurniati AWisudiawan G(2022)Inductive Miner Implementation to Improve Healthcare Efficiency on Indonesia National Health Insurance Data2022 International Conference on Data Science and Its Applications (ICoDSA)10.1109/ICoDSA55874.2022.9862837(239-244)Online publication date: 6-Jul-2022
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Dogan O(2022)Process mining based on patient waiting time: an application in health processesInternational Journal of Web Information Systems10.1108/IJWIS-02-2022-002718:5/6(240-254)Online publication date: 21-Jun-2022
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Andrews RGoel KCorry PBurdett RWynn MCallow D(2022)Process data analytics for hospital case-mix planningJournal of Biomedical Informatics10.1016/j.jbi.2022.104056129(104056)Online publication date: May-2022
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