Advancements in the DRG system payment: an optimal volume/procedure mix model for the optimisation of the reimbursement in Italian healthcare organisations

Cinzia Muriana & Giovanni Vizzini

International Journal of Healthcare Technology and Management2024https://doi.org/10.1504/ijhtm.2024.140375article
AJG 1
Weight
0.40

What the paper says

In Italy, the reimbursement provided to healthcare organisations for medical and surgical procedures is based on the diagnosis related group weight (DRGW), which is an increasing function of the complexity of the procedures. This makes the reimbursement an upper unlimited function. This model does not include the relation of the volume with the complexity. The paper proposes a mathematical model for the optimisation of the reimbursement by determining the optimal mix of volume/procedure, considering the relation volume/complexity and DRGW/complexity. The decreasing, linear, and increasing returns to scale have been defined, and the optimal solution found. The comparison of the model with the traditional approach shows that the proposed model helps the healthcare system to discern the quantity of the reimbursement to provide to health organisations, while the traditional approach, neglecting the relation between the volume and the complexity, can result in an overestimation of the reimbursement.

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https://doi.org/https://doi.org/10.1504/ijhtm.2024.140375

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@article{cinzia2024,
  title        = {{Advancements in the DRG system payment: an optimal volume/procedure mix model for the optimisation of the reimbursement in Italian healthcare organisations}},
  author       = {Cinzia Muriana & Giovanni Vizzini},
  journal      = {International Journal of Healthcare Technology and Management},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1504/ijhtm.2024.140375},
}

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Evidence weight

0.40

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.24 × 0.4 = 0.10
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.