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

Abstract

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.

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“…The TOPSIS method was initially proposed by Hwang and Yoon (1981) and is widely used to rank alternatives in order of preference. The TOPSIS technique chooses an alternative that is as close as possible to the ideal positive solution and as far as possible from the ideal negative solution (Araujo et al , 2018; Hwang and Yoon, 1981; Kasirian and Yusuff, 2013; Li et al , 2018; Lima Junior and Carpinetti, 2015).…”

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

“…Two distances were measured, geodesic distance and the probability distribution distance to determine the consensus degrees. The work in [ 39 ] assess the performance of Brazilian emergency clinics’ services TOPSIS and neural networks to inspect relevant factors in the socioeconomic, demographic and institutional areas as indicators of the performance levels accomplished. The outcome supply managerial insights concerning the performance of public hospitals and chances for superior resource apportionment in the healthcare field.…”

“… Ref. Aim of the study Method used Criteria used Case study [ 37 ] Selection and evaluation of mHealth applications AHP, fuzzy TOPSIS User satisfaction, quality of information, compatibility, functionality, security, ease of access, responsiveness, and ease of learning and understanding Numerical case example for different mHealth applications [ 38 ] settlement solution Drawing for an MAGDM problem through HFLTSs VIKOR, TOPSIS public approval, waste residuals, released-with-effects of health, reliability, treatment-usefulness, Net-cost-per-tonne Example rely on the valuation of a health-care waste disposal management system defined in Shanghai, China case study [ 47 ] [ 39 ] Experimental guide on hospital services’ performance to estimate performance levels' prediction TOPSIS, Neural Networks Inputs – TOPSIS criteria Surgical-beds, beds for clinical, life-support equipment, image diagnosis equipment, No. of nurses, No.…”

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