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  -