Universität zu Köln

Hoier, David (2025). Decision support for structural improvement of melanoma tumor boards: using standard cases to optimize workflow. PhD thesis, Universität zu Köln.

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Abstract

Purpose Choosing optimal cancer treatment is challenging, and certified cancer centers must present all patients in multidisciplinary tumor boards (MDT). Our aim was to develop a decision support system (DSS) to provide treatment recommendations for apparently simple cases already at conference registration and to classify these as “standard cases”. According to certification requirements, discussion of standard cases is optional and would thus allow more time for complex cases. Methods We created a smartphone query that simulated a tumor conference registration and requested all information needed to provide a recommendation. In total, 111 out of 705 malignant melanoma cases discussed at a skin cancer center from 2017 to 2020 were identified as potential standard cases, for which a digital twin recommendation was then generated by DSS. Results The system provided reliable advice in all 111 cases and showed 97% concordance of MDT and DSS for therapeutic recommendations, regardless of tumor stage. Discrepancies included two cases (2%) where DSS advised discussions at MDT and one case (1%) with deviating recommendation due to advanced patient age. Conclusions Our work aimed not to replace clinical expertise but to alleviate MDT workload and enhance focus on complex cases. Overall, our DSS proved to be a suitable tool for identifying standard cases as such, providing correct treatment recommendations, and thus reducing the time burden of tumor conferences in favor for the comprehensive discussion of complex cases. The aim is to implement the DSS in routine tumor board software for further qualitative assessment of its impact on oncological care.

Item Type:	Thesis (PhD thesis)
Creators:	Creators Email ORCID ORCID Put Code Hoier, David davidhoier@hotmail.com UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-784690
Date:	2025
Language:	German
Faculty:	Faculty of Medicine
Divisions:	Faculty of Medicine > Innere Medizin
Subjects:	Medical sciences Medicine
Uncontrolled Keywords:	Keywords Language Malignant melanoma English Algorithm English Multidisciplinary tumor board English Digital recommendations English Expert-curated decision support system English Oncology English
Date of oral exam:	26 February 2025
Referee:	Name Academic Title Elter, Thomas Privatdozent Dr. med. Ernstmann, Nicole Universitätsprofessorin Dr. rer. medic.
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/78469