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A performance analysis of Brazilian public health: TOPSIS and neural networks application

机译：巴西公共卫生绩效分析：TOPSIS和神经网络应用

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Purpose - The purpose of this paper is to estimate the performance of Brazilian hospitals' services and to examine contextual variables in the socioeconomic, demographic and institutional domains as predictors of the performance levels attained. Design/methodology/approach - The paper applied a two-stage approach of the technique for order preference by similarity to the ideal solution (TOPSIS) in public hospitals in 92 Rio de Janeiro municipalities, covering the 2008-2013 period. First, TOPSIS is used to estimate the relative performance of hospitals in each municipality. Next, TOPSIS results are combined with neural networks in an effort to originate a performance model with predictive ability. Data refer to hospitals' outpatient and inpatient services, based on frequent indicators adopted by the healthcare literature. Findings - Despite a slight performance increase over the period, substantial room for improvement is observed. The most important performance predictors were related to the demographic and socioeconomic status (area in square feet and GDP per capita) and to the juridical nature and type of ownership of the healthcare facilities (number of federal and private hospitals). Practical implications - The results provide managerial insights regarding the performance of public hospitals and opportunities for better resource allocation in the healthcare sector. The paper also considers the impact of external socioeconomic, demographic and institutional factors on hospitals' performance, indicating the importance of integrative public health policies. Originality/value - This study displays an innovative context for applying the two-stage TOPSIS technique, with similar efforts not having been identified in the healthcare literature.
机译：目的-本文的目的是评估巴西医院服务的绩效，并检查在社会经济，人口和制度领域的环境变量，作为实现绩效水平的指标。设计/方法/方法-本文采用了两阶段的方法，即通过优先顺序与里约热内卢92个城市的公立医院的理想解决方案（TOPSIS）相似，涵盖了2008-2013年期间。首先，TOPSIS用于估计每个城市中医院的相对绩效。接下来，将TOPSIS结果与神经网络相结合，以尝试建立具有预测能力的绩效模型。数据指的是根据卫生保健文献采用的频繁指标得出的医院门诊和住院服务。调查结果-尽管在此期间性能有所提高，但仍存在很大的改进空间。最重要的绩效预测指标与人口统计和社会经济状况（平方英尺面积和人均GDP）以及医疗机构的司法性质和所有权类型（联邦和私人医院的数量）有关。实际意义-结果提供了有关公立医院绩效的管理见解，并为医疗保健部门提供了更好的资源分配机会。本文还考虑了外部社会经济，人口和体制因素对医院绩效的影响，表明了综合公共卫生政策的重要性。独创性/价值-这项研究显示了应用两阶段TOPSIS技术的创新环境，而医疗文献中尚未发现类似的努力。

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