Abstract
The German Federal Criminal Police Office (BKA) reported damages of 72.6 million euros due to billing fraud in the German healthcare system in 2022, an increase of 25% from the previous year. However, existing literature on automated healthcare fraud detection focuses on US, Taiwanese, or private data, and detection approaches based on individual claims are virtually nonexistent. In this work, we develop machine learning methods that detect fraud in German hospital billing data.
The lack of publicly available and labeled datasets limits the development of such methods. Therefore, we simulated inpatient treatments based on publicly available statistics on main and secondary diagnoses, operations and demographic information. We injected different types of fraud that were identified from the literature. This is the first complete simulator for inpatient care data, enabling further research in inpatient care.
We trained and compared several Machine Learning models on the simulated dataset. Gradient Boosting and Random Forest achieved the best results with a weighted F1 measure of approximately 80%. An in-depth analysis of the presented methods shows they excel at detecting compensation-related fraud, such as DRG upcoding. An impact analysis on private inpatient claims data of a big German health insurance company revealed that up to 12% of all treatments were identified as potentially fraudulent.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bundesministerium für Gesundheit: Vorläufige Finanzergebnisse der GKV für das Jahr 2021. https://www.bundesgesundheitsministerium.de/presse/pressemitteilungen/vor laeufige-finanzergebnisse-gkv-2021.html. Accessed 23 Dec 2023
AOK Bundesverband GbR: Fehlverhalten im Gesundheitswesen. Bericht über die Arbeit und die Ergebnisse der Stellen zur Bekämpfung von Fehlverhalten im Gesundheitswesen (2021). https://aok-bv.de/imperia/md/aokbv/presse/pressemitteilungen/archiv/taetigkeitsbericht_fv _im_gesundheitswesen_2018-2019.pdf. Accessed 23 Dec 2023
Jürges, H., Köberlein, J.: First do no harm. Then do not cheat: DRG upcoding in German neonatology. DIW Discussion Papers (2013)
Institut für das Entgeltsystem im Krankenhaus: Fallpauschalen-Katalog gem. §17b Abs. 1 S. 4 KHG Katalog ergänzender Zusatzentgelte gem. §17b Abs. 1 S. 7 KHG Pflegeerlöskatalog gem. §17b Abs. 4 S. 5 KHG. https://www.g-drg.de/ag-drg-system-2021/fallpauschalen-katalog/fallpauschalen-katalog-2021. Accessed 26 Dec 2023
Busse, R., Geissler, A., Aaviksoo, A.: Diagnosis related groups in Europe: moving towards transparency, efficiency, and quality in hospitals? BMJ (Clin. Res. Ed.) (2013). https://doi.org/10.1136/bmj.f3197
van Herwaarden, S., Wallenburg, I., Messelink, J.: Opening the black box of diagnosis-related groups (DRGs): unpacking the technical remuneration structure of the Dutch DRG system. Health Econ. Policy Law (2020). https://doi.org/10.1017/S1744133118000324
Sievert, J.: Möglichkeiten der Abrechnungsmanipulation im Krankenhaus. Logos, Berlin (2011)
Statistisches Bundesamt: 23131-0003: Krankenhauspatienten: Deutschland, Jahre, Geschlecht, Altersgruppen, Wohnort des Patienten, Hauptdiagnose ICD-10 (1-3-Steller Hierarchie) (2022). https://www-genesis.destatis.de/genesis/downloads/00/tables/23131-0003_00.csv. Accessed 26 Dec 2023
Statistisches Bundesamt: 23141-0003: Nebendiagnosen der vollstationären Patienten: Deutschland, Jahre, Geschlecht, Altersgruppen, Wohnort des Patienten, Nebendiagnosen ICD-10 (1-3-Steller Hierarchie) (2022). https://www-genesis.destatis.de/genesis//online?operation=table &code=23141-0003. Accessed 26 Dec 2023
Statistisches Bundesamt: 23141-0111: Operationen und Prozeduren an vollstationären Patienten: Bundesländer, Jahre, Geschlecht, Altersgruppen, Operationen und Prozeduren (1-4-Steller Hierarchie) (2022). https://www-genesis.destatis.de/genesis//online?operation=table &code=23141-0111. Accessed 26 Dec 2023
Statistisches Bundesamt: Neues Krankenhausverzeichnis (2021). https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Krankenhaeuser/krankenhausverzeichnis.html. Accessed 26 Dec 2023
IMC clinicon: IMC Navigator https://www.imc-clinicon.de/tools/imc-navigator/index_ger.html. Accessed 26 Dec 2023
World Health Organization: Weight-for-age BOYS. https://cdn.who.int/media/docs/default-source/child-growth/child-growth-standards/indicators/weight-for-age/wfa-boys-0-13-zscores.pdf. Accessed 26 Dec 2023
World Health Organization: Weight-for-age GIRLS. https://cdn.who.int/media/docs/default-source/child-growth/child-growth-standards/indicators/weight-for-age/wfa-girls-0-13-zscores.pdf. Accessed 26 Dec 2023
Statistisches Bundesamt: Grunddaten der Krankenhäuser. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Kranken haeuser/Publikationen/Downloads-Krankenhaeuser/grunddaten-krankenhaeuser-2120611217004.pdf. Accessed 26 Dec 2023
Pedregosa, F., Varoquaux, G., Gramfort, A.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. (2011)
Li, J., Huang, K.-Y., Jin, J.: A survey on statistical methods for health care fraud detection. Health Care Manag. Sci. (2008). https://doi.org/10.1007/s10729-007-9045-4
Gee, J., Button, M., Brooks, G.: The financial cost of healthcare fraud. University of Portsmouth and Maclntyre Hudson LLP (2010). https://pure.port.ac.uk/ws/portalfiles/portal/1925942/The-Financial-Cost-of-Healthcare-Fraud---Final-%282%29.pdf. Accessed 26 Dec 2023
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Schrupp, B., Klede, K., Raab, R., Eskofier, B. (2024). Simulation and Detection of Healthcare Fraud in German Inpatient Claims Data. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14835. Springer, Cham. https://doi.org/10.1007/978-3-031-63772-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-63772-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-63771-1
Online ISBN: 978-3-031-63772-8
eBook Packages: Computer ScienceComputer Science (R0)