APPLICATION OF MIXED INTEGER LINEAR PROGRAMMING FOR OPTIMAL ALLOCATION OF QUALITY ANALYST IN HOSPITAL SAFETY PROGRAMS USING PYTHON

This study proposes a Mixed Integer Linear Programming (MILP) model to optimize the allocation of quality analysts in hospital units of a verticalized health system. The model considers the Euclidean distance and the criticality of the units, combined in a weighted cost matrix based on institutional safety indicators. The implementation was carried out in Python, using the CBC solver (COIN-OR Branch and Cut) in Google Colab, ensuring accessibility and reproducibility. The model respects institutional constraints such as exclusivity in critical units, workload and total available analysts, using the median criticality as a balancing criterion. The results point to an efficient, equitable allocation aligned with the institution's strategic priorities. By integrating mathematical modeling with open-source tools, the approach reinforces evidence-based governance and contributes to patient safety through the strategic management of specialized human resources.