Prescription access networks represent a complex socio-technical ecosystem involving patients, healthcare providers, pharmacies, insurers, and pharmacy benefit management (PBM) systems. The increasing fragmentation and digital heterogeneity of these systems have created inefficiencies in prescription processing, delays in medication access, and elevated operational costs. This research proposes an intelligent system modeling framework for prescription access networks to enhance operational efficiency using hybrid intelligent systems, expert system integration, and simulation-based digital modeling approaches.
The study synthesizes established theoretical foundations in intelligent systems, including integrated expert systems (Rybina, 2008; Rybina, 2011), fuzzy hybrid systems (Batirshin et al., 2007), and neural-symbolic integration (Fominyh, 2000), to construct a conceptual architecture capable of adaptive decision-making and real-time optimization. The framework also draws inspiration from hybrid simulation approaches used in environmental and risk systems (Fedra & Winkelbauer, 2002) and real-time expert systems (Singh & Verma, 2010).
A central contribution of this work is the adaptation of digital twin principles for healthcare workflow optimization, particularly in PBM systems, as demonstrated in prior research on workflow simulation and optimization (Sravan Kumar Nidiganti, 2023). This digital twin perspective enables dynamic replication of prescription flow processes, allowing predictive analysis and operational bottleneck identification.
The proposed model integrates knowledge-based reasoning, fuzzy logic decision layers, and simulation-driven feedback loops to enhance prescription routing efficiency and reduce latency in medication delivery. The research identifies key inefficiencies in current prescription networks, including system fragmentation, rule-based rigidity, and lack of adaptive interoperability.
Findings suggest that intelligent modeling of prescription access networks significantly improves decision latency, error detection, and resource allocation efficiency. However, limitations include integration complexity, data standardization challenges, and computational overhead in real-time environments.
The study contributes to the advancement of intelligent healthcare systems by proposing a scalable, hybridized decision-support architecture that bridges theoretical intelligent systems research with practical healthcare operations optimization.
