Dynamic decision system for ENT surgery waiting list prioritization using M-Score and TOPSIS methodology

Abstract

Objective: This study aims to develop and evaluate a dynamic prioritization system to improve surgical waiting list management for otorhinolaryngology (ENT) patients in a high-complexity public hospital in Chile. The proposed model aims to reduce waiting times and improve equity and clinical outcomes by dynamically incorporating changes in patient condition. Methods: We implemented a dynamic scoring system (M-Score), updated weekly using multidimensional biopsychosocial criteria, and integrated it with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to prioritize patients. The evaluation was carried out using Monte Carlo simulations over a 52-week horizon, simulating patient inflows and outflows via a balanced flow model. The stability and performance of the proposed model were compared with a static model and a traditional first-come, first-served (FCFS) protocol. Results: The proposed approach reduced the average waiting time from 130 to 91 days compared to the static model (a 30 % relative and absolute decrease of 39 days) and from 157 to 91 days compared to FCFS (a 42 % relative and absolute reduction of 66 days). The greatest improvements were observed among high-risk patients, whose prioritization was adapted in real time to worsening clinical conditions. Conclusions: Our adaptive prioritization model demonstrates significant improvements in waiting time management, particularly for clinically vulnerable patients. Although the findings support its feasibility, further prospective validation is necessary before clinical implementation. Future research should focus on real-time integration with electronic medical records, scalability between specialties, and evaluation of impacts on patient satisfaction and health outcomes. Lay Summary: ENT patients in public hospitals often face long waiting times that increase health risks. This study introduces a weekly update to the prioritization model using social and health factors of the patient. The system reduced average waiting times by up to 66 days in simulation. High-risk patients were prioritized as their conditions worsened. This approach offers a promising data-driven strategy for improving waitlist management and resource allocation in public healthcare.