Function
Postdoctoral Researcher
Room
G03-2.9
Phone: +4933155091368
Mail: benedikt.langenber[email protected]
Short Bio
Dr. Benedikt Langenberger is a postdoctoral researcher at the Digital Health Cluster at the Hasso Plattner Institute. His research focuses on applying machine learning methods for prediction and causal inference in healthcare. Additionally, he is particularly interested in using econometric techniques to identify the causal effects of policies and interventions on health outcomes. Benedikt earned his M.Sc. from the Hamburg Center for Health Economics, which included a semester abroad at the University of Oslo, Norway. He completed his Ph.D. at the Technical University of Berlin and was a DAAD-funded Visiting Research Scholar at Stanford University, California. He also serves on the Early Career Committee of the European Health Economics Association (EuHEA).
Research Interests
Benedikt Langenberger's research interests are driven by a passion for leveraging innovative methods to improve healthcare. He is particularly focused on using quasi-experimental designs to uncover causal effects in health claims data, enabling the evaluation of healthcare interventions and outcomes. He is also deeply interested in the potential of machine learning algorithms to predict patient outcomes, such as length of stay, risk of adverse events, and high-cost patients. Furthermore, Benedikt is dedicated to developing and assessing clinical decision support tools, including remote monitoring systems and patient-reported outcome measures, to empower clinicians and enhance patient care.
Short Resume
Since 2024
Postdoctoral Researcher, Chair for Digital Health Economics and Policy, Hasso-Plattner-Institute, Potsdam
2023-2024
Visiting Research Scholar, Primary Care and Population Health / Health Policy, Stanford University, California
2020-2024
Doctoral Candidate and Research Assistant, Department of Health Care Management, Technische Universität Berlin, Berlin
Teaching
Winter Term 2024/2025 & Winter Term 2025/2026
Health Care Economics
Guest Lecture
Digital Health Spark - Igniting Need-Driven Innovation in Healthcare
Publications
Langenberger, B., Schrednitzki, D., Halder, A. et al. Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty – a development and validation study. BMC Med Inform Decis Mak 25, 106 (2025). https://doi.org/10.1186/s12911-025-02927-7
Langenberger, B., Siegel, M., Busse, R., & Vogt, V. (2025). Health economic evaluation of a medication safety intervention in elderly care: Identifying causal effects in a multi-center quasi-experimental study design. BMC Health Services Research, 25 (1), 773.
Tsatsaronis, C., Klemt, M., Kinder, K., Langenberger, B., Braun, A., Grobe, T. G., Busse, R., & Quentin,W. (2025). Definition zusammengefasster Krankheitsgruppen für ein Klassifikationssystem zur Messung des morbiditätsbezogenen Versorgungsbedarfs - Popgroup. Das Gesundheitswesen, 87 (04), 282–290.
Langenberger, B., Worsham, C., & Geldsetzer, P. (2024). The Effect of Length of Stay in Hospital on Patients' Health Outcomes: A Quasi-Experimental Study. medRxiv, 2024-12. DOI: 10.1101/2024.12.02.24318326
Schöner, L., Kuklinski, D., Wittich, L., Steinbeck, V., Langenberger, B., Breitkreuz, T., ... & Geissler, A. (2024). Cost-effectiveness of a patient-reported outcome-based remote monitoring and alert intervention for early detection of critical recovery after joint replacement: A randomised controlled trial. PLoS medicine, 21(10), e1004459. DOI: 10.1371/journal.pmed.1004459
Langenberger, Benedikt MSc; Steinbeck, Viktoria MSc; Busse, Reinhard MD. Who Benefits From Hip Arthroplasty or Knee Arthroplasty? Preoperative Patient-reported Outcome Thresholds Predict Meaningful Improvement. Clinical Orthopaedics and Related Research. DOI: 10.1097/CORR.0000000000002994
Steinbeck, V., Bischof, A.Y., Schöner, L., Langenberger, B., Kuklinski, D., Geissler, A., Pross, C., Busse, R. Gender health gap pre- and post-joint arthroplasty: identifying affected patient-reported health domains. Int J Equity Health 23, 44 (2024). https://doi.org/10.1186/s12939-024-02131-5
Kollmann, Nils Patrick; Langenberger, Benedikt; Busse, Reinhard; Pross, Christoph (2023): Stability of hospital quality indicators over time: A multi-year observational study of German hospital data. In: PloS one 18 (11), e0293723. DOI: 10.1371/journal.pone.0293723
Langenberger B. Who will stay a little longer? Predicting length of stay in hip and knee arthroplasty patients using machine learning. Intelligence-Based Medicine 2023;8:100111. DOI: 10.1016/j.ibmed.2023.100111
Langenberger, Benedikt; Schrednitzki, Daniel; Halder, Andreas M.; Busse, Reinhard; Pross, Christoph M. (2023): Predicting whether patients will achieve minimal clinically important differences following hip or knee arthroplasty. In: Bone & joint research 12 (9), S. 512–521. DOI: 10.1302/2046-3758.129.BJR-2023-0070.R2
Steinbeck V, Langenberger B, Schöner L, et al. Electronic Patient-Reported Outcome Monitoring to Improve Quality of Life After Joint Replacement: Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open. 2023;6(9):e2331301. doi:10.1001/jamanetworkopen.2023.31301
Langenberger, B (2023): Machine learning as a tool to identify inpatients who are not at risk of adverse drug events in a large dataset of a tertiary care hospital in the US. British Journal of Clinical Pharmacology. https://doi.org/10.1111/bcp.15846
Langenberger, B; Steinbeck, V; Schöner, L; Busse, R; Pross, C; Kuklinski, D (2023). Exploring treatment effect heterogeneity of a PROMs alert intervention in knee and hip arthroplasty patients: A causal forest application. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2023.107118
Langenberger, B., Vogt, V., Busse, R., Siegel, M. Evaluationsbericht (2023). Optimierte Arzneimittelversorgung für pflegebedürftige geriatrische Patienten. Berlin. https://innovationsfonds.g-ba.de/downloads/beschluss-dokumente/412/2023-05-12_OAV_Evaluationsbericht.pdf
Langenberger B, Schulte T, Groene O (2023). The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data. PLOS ONE 18(1): e0279540. https://doi.org/10.1371/journal.pone.0279540
Langenberger, B., Thoma, A. & Vogt, V. (2022). Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review. BMC Med Inform Decis Mak 22, 18. https://doi.org/10.1186/s12911-022-01751-7
Langenberger, B., Baier, N., Hanke, F. ‐ C., Fahrentholz, J., Gorny, C., Sehlen, S., Reber, K. C., Liersch, S., Radomski, R., Haftenberger, J., Heppner, H. J., Busse, R., & Vogt, V. (2022). The detection and prevention of adverse drug events in nursing home and home care patients: Study protocol of a quasi‐experimental study. Nursing Open, 9, 1477–1485. https://doi.org/10.1002/nop2.1146
Gröne, O., Langenberger, B., Catalá, E., Wendel, P., Hildebrandt, H. (2021) Erfolgspotenziale durch ein optimiertes Versorgungsmanagement. In: Hildebrandt, Stuppardt (Hrsg.). Zukunft Gesundheit – regionalisiert, vernetzt, patientenorientiert. medhochzwei Verlag Heidelberg.