TY - JOUR A1 - Köberlein-Neu, Juliane A1 - Mennemann, Hugo A1 - Hamacher, Stefanie A1 - Waltering, Isabell A1 - Jaehde, Ulrich A1 - Schaffert, Corinna A1 - Rose, Olaf T1 - Interprofessionelles Medikationsmanagement bei multimorbiden Patienten. Eine Cluster-randomisierte Studie (WestGem-Studie) JF - Deutsches Ärzteblatt Y1 - 2016 SN - 0012-1207 IS - 44 SP - 741 EP - 748 ER - TY - JOUR A1 - Bücker, Michael A1 - Hoti, Kreshnik A1 - Rose, Olaf T1 - Artificial intelligence to assist decision-making on pharmacotherapy: A feasibility study JF - Exploratory Research in Clinical and Social Pharmacy N2 - Background Artificial intelligence (AI) has the capability to analyze vast amounts of data and has been applied in various healthcare sectors. However, its effectiveness in aiding pharmacotherapy decision-making remains uncertain due to the intricate, patient-specific, and dynamic nature of this field. Objective This study sought to investigate the potential of AI in guiding pharmacotherapy decisions using clinical data such as diagnoses, laboratory results, and vital signs obtained from routine patient care. Methods Data of a previous study on medication therapy optimization was updated and adapted for the purpose of this study. Analysis was conducted using R software along with the tidymodels extension packages. The dataset was split into 74% for training and 26% for testing. Decision trees were selected as the primary model due to their simplicity, transparency, and interpretability. To prevent overfitting, bootstrapping techniques were employed, and hyperparameters were fine-tuned. Performance metrics such as areas under the curve and accuracies were computed. Results The study cohort comprised 101 elderly patients with multiple diagnoses and complex medication regimens. The AI model demonstrated prediction accuracies ranging from 38% to 100% for various cardiovascular drug classes. Laboratory data and vital signs could not be interpreted, as the effect and dependence were unclear for the model. The study revealed that the issue of AI lag time in responding to sudden changes could be addressed by manually adjusting decision trees, a task not feasible with neural networks. Conclusion In conclusion, the AI model exhibited promise in recommending appropriate medications for individual patients. While the study identified several obstacles during model development, most were successfully resolved. Future AI studies need to include the drug effect, not only the drug, if laboratory data is part of the decision. This could assist with interpreting their potential relationship. Human oversight and intervention remain essential for an AI-driven pharmacotherapy decision support system to ensure safe and effective patient care. KW - Artificial intelligence, Pharmacotherapy, Medication review, Cardiology, Clinical decision support system, Pharmacy practice Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-181429 UR - https://www.sciencedirect.com/science/article/pii/S266727662400088X SN - 2667-2766 VL - 15 SP - 100491 EP - 100491 ER - TY - CHAP A1 - Köberlein, Juliane A1 - Mennemann, Hugo A1 - Waltering, Isabelle A1 - Rose, Olaf T1 - Ergebnisbericht des Forschungsprojektes: Westphalian study on a medication therapy management and home care based intervention under gender specific aspects in elderly multimorbid patients (WestGem-study) T2 - Unveröffentlichter Forschungsbericht. Y1 - 2015 ER - TY - JOUR A1 - Feldmann, Carsten A1 - Rose, Olaf T1 - Additive manufacturing in community pharmacies: a framework for business model innovation JF - BMJ Innovations Y1 - 2021 U6 - http://dx.doi.org/10.1136/bmjinnov-2019-000416 VL - Published Online First: 16 March 2021 SP - 1 EP - 12 ER -