Abstract
Problem Definition: Queensland’s Compulsory Third-Party (CTP) Insurance Scheme provides a mechanism for persons injured as a result of a motor vehicle accident to receive compensation. Managing CTP claims involves multiple stakeholders with potentially conflicting interests. It is therefore pertinent to investigate whether ‘best practice’ for claims processing can be identified and measured so all claimants receive fair and equitable treatment. The project set out to test the applicability of a mixed-method approach to identify ‘best-practice’ using qualitative, process mining, and data mining techniques in an insurance claims processing domain. Relevance: Existing approaches typically identify ‘best practice’ from literature or surveys of practitioners. The study provides insights into an alternative, mixed-method approach to deriving best practice from historical data and domain knowledge. Methodology: The study is a reflective analysis of insights gained from a practical application of a mixed-method approach to determine ‘best practice’. Results: The mixed-method approach has a number of benefits over traditional approaches in uncovering best practice process behavior from historical data in the real-world context (i.e., can identify process behavior differences between high and low performing cases). The study also highlights a number of challenges with regards to the quality and detail of data that needs to be available to perform the analysis. Managerial Implications: The ‘lessons learned’ from this study will directly benefit others seeking to implement a data-driven approach to understand a ‘best-practice’ process in their own organization.
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Notes
Funding provided by MAIC (https://maic.qld.gov.au/).
References
Aggarwal CC (2015) Data mining: the textbook. Springer, Heidelberg
Andrews R, Wynn MT, ter Hofstede AHM, Xu J, Horton K, Taylor P, Plunkett-Cole S (2018) Exposing impediments to insurance claims processing. Business process management cases. Springer, Heidelberg, pp 275–290
Bogetoft P, Otto L (2010) Benchmarking with DEA, SFA, and R, vol 157. Springer, Heidelberg
Bose J, Mans R, van der Aalst W (2013) Wanna improve process mining results? It’s high time we consider data quality issues seriously. In: CIDM, IEEE, pp 127–134
Brand N, Van der Kolk H (1995) Workflow analysis and design. Kluwer Bedrijfswetenschappen, Deventer
Brock G, Pihur V, Datta S, Datta S, et al. (2011) clValid, an R package for cluster validation. J Stat Softw
Cho M, Song M, Comuzzi M, Yoo S (2017) Evaluating the effect of best practices for business process redesign: an evidence-based approach based on process mining techniques. Decis Support Syst 104:92–103
Christmann P (2000) Effects of “best practices” of environmental management on cost advantage: The role of complementary assets. Acad Manag J 43(4):663–680
Creswell JW, Miller DL (2000) Determining validity in qualitative inquiry. Theor Pract 39(3):124–130
De Leoni M, van der Aalst WM (2013) Data-aware process mining: Discovering decisions in processes using alignments. In: Proceedings of the 28th annual ACM symposium on applied computing, pp 1454–1461
De Leoni M, van der Aalst WM, Dees M (2016) A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf Syst 56:235–257
De Smedt J, Hasić F, vanden Broucke SK, Vanthienen J (2017) Towards a holistic discovery of decisions in process-aware information systems. International conference on business process management. Springer, Heidelberg, pp 183–199
De Weerdt J, Schupp A, Vanderloock A, Baesens B (2013) Process mining for the multi-faceted analysis of business processes - a case study in a financial services organization. Comput Indust 64(1):57–67
del Rio-Ortega A, Resinas M, Cabanillas C, Ruiz-Cortes A (2012) On the definition and design-time analysis of process performance indicators. Inf Syst 38(4):470–490
Dumas M, La Rosa M, Mendling J, Reijers HA (2018) Three process discovery challenges. In: Fundamentals of business process management, 2nd edn, Springer, Heidelberg, chap 5.1.2, pp 162–165
Flick U (2018) An introduction to qualitative research. Sage, Thousand Oaks
Francis C, Iglesias M, Walsh J (2009) Accident compensation claims management-lessons learnt and claimant outcomes. Presented to the Institute of Actuaries of Australia 12th Accident Compensation Seminar 22–24 November 2009, Melbourne. https://www.actuaries.asn.au/Library/ACS09_Paper_Francis%20et%20al._Accident%20Compensation%20Claims%20Management.pdf
Glaser B (1998) Doing grounded theory: Issues and discussions. Sociology Press
Greenland S, Robins JM, Pearl J (1999) Confounding and collapsibility in causal inference. Statist Sci 14(1):29–46
Kis I, Bachhofner S, Di Ciccio C, Mendling J (2017) Towards a data-driven framework for measuring process performance. In: Reinhartz-Berger I et al (eds) Enterprise, business-process and information systems modeling. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-59466-8_1
Leemans SJJ, Fahland D, van der Aalst WMP (2014) Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann N, Song M, Wohed P (eds) BPM workshops. Springer, Heidelberg, pp 66–78
MacKenzie SB, Podsakoff PM, Podsakoff NP (2011) Construct measurement and validation procedures in mis and behavioral research: Integrating new and existing techniques. MIS Q 35(2):293–334
Mannhardt F, De Leoni M, Reijers HA (2015) The multi-perspective process explorer. BPM (Demos) 1418:130–134
Mannhardt F, De Leoni M, Reijers HA, Van Der Aalst WM (2016) Decision mining revisited-discovering overlapping rules. International conference on advanced information systems engineering. Springer, Heidelberg, pp 377–392
Mansar S, Reijers HA (2007) Best practices in business process redesign: Use and impact. Bus Process Manag J 13(2):193–213
Myers MD, Newman M (2007) The qualitative interview in is research: Examining the craft. Inf Organ 17(1):2–26
Newman I, Lim J, Pineda F (2013) Content validity using a mixed methods approach: Its application and development through the use of a table of specifications methodology. J Mixed Methods Res 7(3):243–260
Queensland Government (2014) Civil Liability Regulation 2014. Queensland Government
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106
Reijers H, Mansar S (2005) Best practices in process redesign: An overview and qualitative evaluation of successful redesign heuristics. Omega 33(4):283–306
Rondini A, Pezzotta G, Cavalieri S, Ouertani MZ, Pirola F (2018) Standardizing delivery processes to support service transformation: A case of a multinational manufacturing firm. Comput Indust 100:115–128
Rozinat A, van der Aalst WM (2006) Decision mining in ProM. International conference on business process management. Springer, Heidelberg, pp 420–425
Starczewski A, Krzyźak A (2015) Performance evaluation of the silhouette index. International conference on artificial intelligence and soft computing. Springer, Heidelberg, pp 49–58
van der Aalst WM, Dustdar S (2012) Process mining put into context. IEEE Internet Comput 16(1):82–86
van der Aalst WM, Van Dongen BF (2013) Discovering petri nets from event logs. In: Jensen K et al (eds) Transactions on petri nets and other models of concurrency VII. Springer, Heidelberg, pp 372–422. https://doi.org/10.1007/978-3-642-38143-0_10
van der Aalst WMP (2016) Process mining: Data science in action. Springer, Heidelberg
van Eck ML, Lu X, Leemans SJJ, van der Aalst WMP (2015) PM\(^2\): A process mining project methodology. LNCS 9097:297–313
Venkatesh V, Brown SA, Sullivan YW (2016) Guidelines for conducting mixed-methods research: An extension and illustration. J Assoc Inf Syst 17(7):435–494
Wynn MT, Poppe E, Xu J, ter Hofstede AHM, Brown R, Pini A, van der Aalst WMP (2017) ProcessProfiler3D: A visualisation framework for log-based process performance comparison. Decis Support Syst 100:93–108
Wynn MT, Suriadi S, Eden R, Poppe E, Pika A, Andrews R, ter Hofstede AHM, (2019) Grounding process data analytics in domain knowledge: A mixed-method approach to identifying best practice. In: Proceedings of the business process management forum, Vienna. Springer, Heidelberg, pp 163–179
Acknowledgements
The work presented in this paper was funded by a grant from the Queensland Motor Accident Insurance Commission (MAIC). We gratefully acknowledge the contributions of Dr Suriadi Suriadi (Business Process Management group, Queensland University of Technology) to the project on which this paper is based.
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Accepted after two revisions by Jörg Becker.
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Poppe, E., Pika, A., Wynn, M.T. et al. Extracting Best-Practice Using Mixed-Methods. Bus Inf Syst Eng 63, 637–651 (2021). https://doi.org/10.1007/s12599-021-00698-9
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DOI: https://doi.org/10.1007/s12599-021-00698-9