An Adaptive Smart Business Intelligence Model Based on Enhanced HGO Discovery for Real-Time in Inpatient Care

The complexity and dynamics of inpatient care require advanced decision support systems that are fast, adaptive, and capable of real-time execution. This study proposes a novel Smart Business Intelligence (SBI) model developed through an enhanced Hierarchy Governance Outlook (HGO) Discovery approach to achieve high-precision inpatient service prioritization. Unlike conventional frameworks, the proposed model combines an adaptive weighting mechanism, a sustainability optimization process, and real-time data integration from heterogeneous clinical and operational sources. The enhanced HGO Discovery model allows for dynamic adjustment of decision parameters in response to evolving hospital conditions, thereby maximizing patient care. The results demonstrate that the proposed model consistently outperforms baseline approaches in predictive accuracy, stability, and computational efficiency. These findings highlight the potential of the proposed framework to support data-driven decision-making and enhance the quality and efficiency of inpatient care delivery in modern healthcare environments. Experimental results show that the proposed model provides more accurate patient prioritization, faster identification of critical conditions, and improved operational efficiency compared to conventional approaches. Therefore, the proposed system offers an effective intelligent healthcare solution to support real-time inpatient care and data-driven medical decision-making in modern hospitals.

@article{Hengki2026,
  title     = {An Adaptive Smart Business Intelligence Model Based on Enhanced HGO Discovery for Real-Time in Inpatient Care},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Hengki and Rahmat Gernowo and Oky Dwi Nurhayati},
  doi       = {10.14569/IJACSA.2026.0170664},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170664}
}