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DOI: 10.1055/s-0040-1705105
Mapping Patient Data to Colorectal Cancer Clinical Algorithms for Personalized Guideline-Based Treatment
Funding The research reported in this publication was supported by the West German Cancer Center and the Institute of Medical Informatics, Biometry and Epidemiology in Essen, Germany. This study was performed free of charge and for noncommercial purposes. The content is solely the responsibility of the authors and does not necessarily represent the official views of the University Hospital in Essen, Germany.Publication History
30 August 2019
22 January 2020
Publication Date:
18 March 2020 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035026/202002/lookinside/thumbnails/10-1055-s-0040-1705105_190193ra-1.jpg)
Abstract
Background Colorectal cancer is the most commonly occurring cancer in Germany, and the second and third most commonly diagnosed cancer in women and men, respectively. In this context, evidence-based guidelines positively impact the quality of treatment processes for cancer patients. However, evidence of their impact on real-world patient care remains unclear. To ensure the success of clinical guidelines, a fast and clear provision of knowledge at the point of care is essential.
Objectives The objectives of this study are to model machine-readable clinical algorithms for colon carcinoma and rectal carcinoma annotated by Unified Medical Language System (UMLS) based on clinical guidelines and the development of an open-source workflow system for mapping clinical algorithms with patient-specific information to identify patient's position on the treatment algorithm for guideline-based therapy recommendations.
Methods This study qualitatively assesses the therapy decision of clinical algorithms as part of a clinical pathway. The solution uses rule-based clinical algorithms, which were developed based on the corresponding guidelines. These algorithms are executed on a newly developed open-source workflow system and are visualized at the point of care. The aim of this approach is to create clinical algorithms based on an established business process standard, the Business Process Model and Notation (BPMN), which is annotated by UMLS terminologies. The gold standard for the validation process was set by manual extraction of clinical datasets from 86 rectal cancer patients and 89 colon cancer patients.
Results Using this approach, the algorithm achieved a precision value of 87.64% for colon cancer and 84.70% for rectal cancer with recall values of 87.64 and 83.72%, respectively.
Conclusion The results indicate that the automatic positioning of a patient on the decision pathway is possible with tumor stages that have a less complex clinical algorithm with fewer decision points reaching a higher accuracy than complex stages.
Keywords
clinical decision support system - decision support algorithm - clinical practice guidelines - colorectal cancer - oncologyProtection of Human and Animal Subjects
The study was performed in compliance with the World Medical Association Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the University Hospital Ethics Committee, Essen Germany.
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References
- 1 Robert-Koch-Institut. Gesellschaft der epidemiologischen Krebsregister in Deutschland. Krebs in Deutschland, 8th ed. Berlin, Germany: 2012: 36-39
- 2 Robert-Koch-Institut. Bericht zum Krebsgeschehen in Deutschland 2016. Available at: https://edoc.rki.de/bitstream/handle/176904/3264/28oaKVmif0wDk.pdf?sequence=1&isAllowed=y . Accessed February 11, 2020
- 3 Majek O, Gondos A, Jansen L. , et al; GEKID Cancer Survival Working Group. Survival from colorectal cancer in Germany in the early 21st century. Br J Cancer 2012; 106 (11) 1875-1880
- 4 Leitlinie-Detailansicht Kolorektales Karzinom. Available at: http://www.awmf.org/leitlinien/detail/ll/021-007OL.html . Accessed October 2, 2019
- 5 Haeske-Seeberg H. Handbuch Qualitätsmanagement im Krankenhaus: Strategien, Analysen, Konzepte. Stuttgart, Germany: Kohlhammer Verlag; 2008: 248
- 6 Grimshaw JM, Thomas RE, MacLennan G. , et al. Effectiveness and efficiency of guideline dissemination and implementation strategies. Health Technol Assess 2004; 8 (06) iii-iv , 1–72
- 7 Oberender PO. Clinical Pathways: Facetten eines neuen Versorgungsmodells. Krankenhaus. Stuttgart, Germany: Kohlhammer Verlag; 2005: 22
- 8 Lelgemann M, Ollenschläger G. [Evidence based guidelines and clinical pathways: complementation or contradiction?] (German). Internist (Berl) 2006; 47 (07) 690-692 , 697
- 9 Lenz R, Kuhn K. Aspekte einer prozessorientierten Systemarchitektur für Informationssysteme im Gesundheitswesen. P. Dadam und M. Reichert - Informatik 2004. Informatik Verbindet Ulm; 2004: 530-536
- 10 Shea S, DuMouchel W, Bahamonde L. A meta-analysis of 16 randomized controlled trials to evaluate computer-based clinical reminder systems for preventive care in the ambulatory setting. J Am Med Inform Assoc 1996; 3 (06) 399-409
- 11 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330 (7494): 765
- 12 Steffen HM, Griebenow R, Meuthen I, Schrappe M, Ziegenhagen DJ. Internistische Differenzialdiagnostik: Ausgewählte evidenzbasierte Entscheidungsprozesse und diagnostische Pfade. Schattauer; Stuttgart, Germany: 2008: 12
- 13 Jacobs B. Ableitung von klinischen Pfaden aus evidenzbasierten Leitlinien am Beispiel der Behandlung des Mammakarzinoms der Frau. Dissertation (PhD thesis). Medizinische Fakultät der Universität Duisburg-Essen. Germany; 2006
- 14 Bernstein K, Andersen U. Managing care pathways combining SNOMED CT, archetypes and an electronic guideline system. Stud Health Technol Inform 2008; 136: 353-358
- 15 Lasorsa I, Antrassi DP, Ajčević M. , et al. Personalized support for chronic conditions. A novel approach for enhancing self-management and improving lifestyle. Appl Clin Inform 2016; 7 (03) 633-645
- 16 Militello LG, Diiulio JB, Borders MR. , et al. Evaluating a modular decision support application for colorectal cancer screening. Appl Clin Inform 2017; 8 (01) 162-179
- 17 Suner A, Karakülah G, Dicle O, Sökmen S, Çelikoğlu CC. CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree. Appl Clin Inform 2015; 6 (01) 56-74
- 18 Becker M. . Guide2Treat: Software. Available at: https://ieee-dataport.org/documents/guide2treat-software . Accessed February 11, 2019
- 19 Wittekind C, Meyer HJ. TNM Classification of Malignant Tumours, Wiley-VCH. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co.; 2010: 94-100
- 20 McInnes BT, Pedersen T, Carlis J. Using UMLS concept unique identifiers (CUIs) for word sense disambiguation in the biomedical domain. AMIA Annu Symp Proc 2007; 2007: 533-537
- 21 Ramos-Merino M, Álvarez-Sabucedo LM, Santos-Gago JM, Sanz-Valero J. A BPMN Based Notation for the Representation of Workflows in Hospital Protocols. J Med Syst 2018; 42 (10) 181
- 22 Scheuerlein H, Rauchfuss F, Dittmar Y. , et al. New methods for clinical pathways-Business Process Modeling Notation (BPMN) and Tangible Business Process Modeling (t.BPM). Langenbecks Arch Surg 2012; 397 (05) 755-761
- 23 Andrzejewski D, Ledwon P, Beck E. Möglichkeiten und Grenzen der BPMN als Werkzeug zur Modellierung medizinischer Prozesse und evidenzbasierter Behandlungspfade. Senologie Zeitschrift Mammadiagnostik Therap 2012; 9: A5
- 24 Beck E, Streit B, Meissner T. , et al. Modellierung medizinischer Prozesse mit der Business Process Modeling Notation (BPMN). Geburtshilfe Frauenheilkd 2011; •••: 71-P515 . Doi: 10.1055/s-0031-1278635
- 25 Kirchner K, Malessa C, Scheuerlein H, Settmacher U. . Experience from collaborative modeling of clinical pathways. Available at: https://www.researchgate.net/publication/306426138 . Accessed February 11, 2020
- 26 AWMF. Das Leitlinien-Manual. Zeitschrift für ärztliche Fortbildung und Qualitätssicherung. Unter Mitarbeit von Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften (AWMF) und die Ärztliche Zentralstelle für Qualitätssicherung. Urban&Fischer. Available at: https://www.awmf.org/leitlinien/awmf-regelwerk/awmf-publikationen-zu-leitlinien/leitlinien-manual.html . Accessed February 11, 2020
- 27 Braun R, Schlieter H. Requirements-based development of BPMN extensions: The case of clinical pathways. 2014 IEEE 1st International Workshop on the Interrelations between Requirements Engineering and Business Process Management (REBPM). IEEE 2014; 39-44
- 28 Heiden K, Böckmann B. Structured knowledge acquisition for defining guideline-compliant pathways. Stud Health Technol Inform 2013; 186: 73-77
- 29 Heiden K. Modellbasierte Integration evidenzbasierter Leitlinien in klinische Pfade. U. Goltz, M.A. Magnor, J. Appelrath, H, H. Matthies, W.-T. Balke, L.C. Wolf (Hrsg.). Informatik 2012, 42. Jahrestagung der Gesellschaft für Informatik e.V. (GI). 16.-21.09.2012, Braunschweig, Germany; 2012: 1864-1870