Developing Decision Support Systems for Healthcare Administration using AI - Pathan, Riyaz Rashid; Asiamah, Joel Osei-; Jadhav, Priyanka Nilesh; Saravanan, S.; Kumar Singh, Akhilesh; Kumar Sahoo, Srikanta (Journal of Neonatal Surgery, 2025)
“Artificial intelligence (AI) in healthcare administration has resulted in better decision making, the ease of operational efficiency and patient safety. The aim of this research is to build AI based on decision support systems (DSS) that can improve administrative process by using machine learning, deep learning, and fuzzy logic, natural language processing algorithms. Finally, these algorithms were evaluated in a dataset of 50,000 patient records, wherein they predict resource allocation, optimize scheduling, and ameliorate the time taken to complete administrative tasks. Results obtained from the experimental results were that, of all the models, the deep learning model yielded the best results, with an accuracy of 92.5%, followed next by the machine learning model (89.3%), fuzzy logic based model (85.7%) and then the natural language model (83.2%). Results of the comparative analysis revealed that the AI based DSS yielded 38% faster administrative delay and 44% better resource utilization as compared to the traditional methods. Finally, it suggested challenges of privacy of data, algorithmic bias, and the extent of readiness of AI by health professionals. Now, future research should be about the combination of AI with the blockchain and IoT to offer security and interoperability. AI driven DSS can increase transparency and ethical consideration that can drastically make a change in the way healthcare is administered; in such ways decision making process can be improved and operational efficiency can be enhanced without compromising on patient trust and data security.”