RSS-Feed abonnieren
![](/products/assets/desktop/img/oa-logo.png)
DOI: 10.1055/a-1996-8479
Characterization of Laboratory Flow and Performance for Process Improvements via Application of Process Mining
![](https://www.thieme-connect.de/media/10.1055-s-00035026/202301/lookinside/thumbnails/10-1055-a-1996-8479_202206ra0168-1.jpg)
Abstract
Background The rising level of laboratory automation provides an increasing number of logged events that can be used for the characterization of laboratory performance and process improvements. This abundance of data is often underutilized for improving laboratory efficiency.
Objectives The first aim of this descriptive study is to provide a structured approach for transforming raw laboratory data to data that is suitable for process mining. The second aim is to describe a process mining approach for mapping and characterizing the sample flow in a clinical chemistry laboratory to identify areas for improvement in the testing process.
Methods Data were extracted from instrument log files and the middleware between laboratory instruments and information technology infrastructure. Process mining was used for automated process discovery and analysis. Laboratory performance was quantified in terms of relevant key performance indicators (KPIs): turnaround time, timeliness, workload, work-in-process, and machine downtime.
Results The method was applied to two Dutch university hospital clinical chemistry laboratories. We identified areas where alternative routes might increase laboratory efficiency and observed the negative effects of machine downtime on laboratory performance. This encourages the laboratory to review sample routes in its analyzer lines, the routes of high priority samples during instrument downtime, as well as the preventive maintenance policy.
Conclusion This article provides the first application of process mining to event data from a medical diagnostic laboratory for automated process model discovery. Our study shows that process mining, with the use of relevant KPIs, provides valuable insights for laboratories that motivates the disclosure and increased utilization of laboratory event data, which in turn drive the analytical staff to intervene in the process to achieve the set performance goals. Our approach is vendor independent and widely applicable for all medical diagnostic laboratories.
Keywords
process mining - laboratory logistics - key performance indicators - clinical chemistry - process discoveryProtection of Human and Animal Subjects
No human or animal subjects were included in this study.
* Joint first authors.
Publikationsverlauf
Eingereicht: 13. Juni 2022
Angenommen: 03. November 2022
Accepted Manuscript online:
12. Dezember 2022
Artikel online veröffentlicht:
22. Februar 2023
© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Cornes MP, Atherton J, Pourmahram G. et al. Monitoring and reporting of preanalytical errors in laboratory medicine: the UK situation. Ann Clin Biochem 2016; 53 (pt 2): 279-284
- 2 Sciacovelli L, Lippi G, Sumarac Z. et al; Working Group “Laboratory Errors and Patient Safety” of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). Quality Indicators in Laboratory Medicine: the status of the progress of IFCC Working Group “Laboratory Errors and Patient Safety” project. Clin Chem Lab Med 2017; 55 (03) 348-357
- 3 van der Aalst WMP. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Berlin, Germany: Springer; 2011
- 4 Yu HE, Lanzoni H, Steffen T. et al. Improving laboratory processes with total laboratory automation. Lab Med 2019; 50 (01) 96-102
- 5 Dallagassa MR, dos Santos Garcia C, Scalabrin EE, Ioshii SO, Carvalho DR. Opportunities and challenges for applying process mining in healthcare: a systematic mapping study. J Ambient Intell Humaniz Comput 2021; •••: 1-18
- 6 Batista E, Solanas A. Process mining in healthcare: a systematic review. In: 2018 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018; 2019
- 7 Rao G, Kirley K, Epner P. et al. Identifying, analyzing, and visualizing diagnostic paths for patients with nonspecific abdominal pain. Appl Clin Inform 2018; 9 (04) 905-913
- 8 Durojaiye AB, McGeorge NM, Puett LL. et al. Mapping the flow of pediatric trauma patients using process mining. Appl Clin Inform 2018; 9 (03) 654-666
- 9 Rebuge A, Ferreira DR. Business process analysis in healthcare environments: a methodology based on process mining. Inf Syst 2012; 37 (02) 99-116
- 10 Conca T, Saint-Pierre C, Herskovic V. et al. Multidisciplinary collaboration in the treatment of patients with type 2 diabetes in primary care: analysis using process mining. J Med Internet Res 2018; 20 (04) e127
- 11 De Weerdt J, Caron F, Vanthienen J, Baesens B. Getting a grasp on clinical pathway data: an approach based on process mining. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining; 2013: 22-35
- 12 Gatta R, Vallati M, Lenkowicz J. et al. Generating and comparing knowledge graphs of medical processes using pMineR. In: Proceedings of the Knowledge Capture Conference, K-CAP 2017; 2017: 1-4
- 13 Kim E, Kim S, Song M. et al. Discovery of outpatient care process of a tertiary university hospital using process mining. Healthc Inform Res 2013; 19 (01) 42-49
- 14 Mannhardt F, Blinde D. Analyzing the trajectories of patients with sepsis using process mining. In: RADAR + EMISA 2017; 2017: 72-80
- 15 Suriadi S, Mans RS, Wynn MT, Partington A, Karnon J. Measuring patient flow variations: a cross-organisational process mining approach. In: Asia-Pacific Conference on Business Process Management; 2014: 43-58
- 16 Tsai ER, Tintu AN, Demirtas D, Boucherie RJ, de Jonge R, de Rijke YB. A critical review of laboratory performance indicators. Crit Rev Clin Lab Sci 2019; 56 (07) 458-471
- 17 IFCC. Quality Indicators - Key Processes. Accessed December 27, 2022, at: http://217.148.121.44/MqiWeb/resources/doc/Quality_Indicators_Key_Processes.pdf
- 18 R Core Team. R: A Language and Environment for Statistical Computing; 2019. Accessed December 27, 2022, at: https://www.r-project.org/
- 19 RStudio Team. RStudio: Integrated Development for R.; 2019. Accessed December 27, 2022, at: https://www.rstudio.com/
- 20 Günther CW, Rozinat A. Disco: Discover your processes. In: CEUR Workshop Proceedings; 2012: 40-44
- 21 Rojas E, Munoz-Gama J, Sepúlveda M, Capurro D. Process mining in healthcare: a literature review. J Biomed Inform 2016; 61: 224-236
- 22 Tsai ER, Demirtas D, Tintu AN, de Jonge R, De Rijke YB, Boucherie RJ. Optimal design of networks of FIFO queues and Incubation queues with an application to the design of clinical chemistry laboratories (unpublished results)
- 23 Weijters AJMM, Van Der Aalst WMP. Rediscovering workflow models from event-based data using little thumb. Integr Comput Aided Eng 2003; 10 (02) 151-162
- 24 Günther CW, Van Der Aalst WMP. Fuzzy mining - adaptive process simplification based on multi-perspective metrics. In: International Conference on Business Process Management; 2007: 328-343
- 25 Fernández-Llatas C, Benedi JM, García-Gómez JM, Traver V. Process mining for individualized behavior modeling using wireless tracking in nursing homes. Sensors (Basel) 2013; 13 (11) 15434-15451
- 26 Fernandez-Llatas C, Lizondo A, Monton E, Benedi JM, Traver V. Process mining methodology for health process tracking using real-time indoor location systems. Sensors (Basel) 2015; 15 (12) 29821-29840
- 27 Steindel SJ, Howanitz PJ. Physician satisfaction and emergency department laboratory test turnaround time. Arch Pathol Lab Med 2001; 125 (07) 863-871