Assessment of the impact of the COVID-19 pandemic on the productivity of teaching hospitals in BrazilABSTRACT
Objectives To analyze the influence of the COVID-19 pandemic on the productivity of general teaching hospitals in Brazil, by region and legal entity, and to propose parameters of care.
Methods This was an observational study by means of mathematical modeling with data envelopment analysis and Malmquist index, using data on inputs and healthcare output before (2019) and during (2021) the pandemic.
Results A total of 149 general teaching hospitals were analyzed, 32 of which were considered efficient. There was a decrease in productivity across all regions and legal entity. To bring all inefficient hospitals to the efficiency frontier generated by the model, there is a need to increase output by 2,205,856 (96.5%) hospitalizations and 872,264 (107.4%) surgeries.
Conclusion The decline in hospital productivity resulted from the social commitment of hospitals during the pandemic, with a change in the care delivery pattern. The mathematical model used allows for the generation of parameters to facilitate the efficient recovery of care services after the end of public health emergency, and can be applied to hospital planning.
Keywords
Teaching Hospitals; Operations Research; Benchmarking; Organizational Efficiency; COVID-19
Study contributions
Main results The COVID-19 pandemic led to a reduction in productivity of general teaching hospitals in Brazil across all regions and in all legal entity types. In order to recover, these hospitals will need to increase their average hospitalizations by 96.5% and surgeries by 107.4%.
Implications for services The study presents a methodology that can be adapted and replicated in the management of healthcare services in the country, by defining an efficiency score and calculating the parameters, in a scenario of care recovery following the end of the public health emergency.
Perspectives Additional qualitative analysis and application of DEA- Malmquist in subsequent years will validate dynamic planning, which considers multiple fluctuations and the influence of new factors and contexts that impact productivity (such as the pandemic).
RESUMO
Objetivos Analisar a influência da pandemia de covid-19 na produtividade dos hospitais gerais de ensino do Brasil, por região e natureza jurídica, e propor parâmetros assistenciais.
Métodos Estudo observacional por modelagem matemática com análise envoltória de dados e índice de Malmquist, utilizando dados de recursos e produção assistencial antes (2019) e durante (2021) a pandemia.
Resultados Foram analisados 149 hospitais gerais de ensino, 32 dos quais foram considerados eficientes. Houve queda da produtividade em todas regiões e naturezas jurídicas. Para que todos os hospitais ineficientes atinjam a fronteira de eficiência gerada pela modelagem, há necessidade de aumento da produção em 2.205.856 (96,5%) internações e 872.264 (107,4%) cirurgias.
Conclusão A queda na produtividade hospitalar decorreu do compromisso social dos hospitais durante a pandemia, com modificação do padrão de assistência. O modelo matemático utilizado permite gerar parâmetros para recuperação assistencial eficiente depois de finalizada emergência sanitária, podendo ser aplicado para planejamento hospitalar.
Palavras-chave
Hospitais de Ensino; Pesquisa Operacional;
Benchmarking
; Eficiência Organizacional; Covid-19
Contribuições do estudo
Principais resultados A pandemia de covid-19 reduziu a produtividade dos hospitais gerais de ensino do Brasil em todas as regiões e em todas as naturezas jurídicas. Para a recuperação, os hospitais devem elevar, em média, 96,5% de internações e 107,4% de cirurgias.
Implicações para os serviços O estudo apresenta metodologia que pode ser adaptada e replicada na gestão de serviços de saúde do país, ao definir um escore de eficiência e calcular os parâmetros, num cenário de recuperação assistencial após o fim da emergência em saúde pública.
Perspectivas Análise qualitativa adicional e aplicação de DEA-Malmquist, nos anos subsequentes, validarão o planejamento dinâmico, que considera múltiplas oscilações e a influência de novos fatores e contextos que alteram a produtividade (caso da pandemia).
RESUMEN
Objetivos Analizar la influencia de la pandemia de COVID-19 en la productividad de hospitales generales docentes de Brasil, por región y naturaleza jurídica, y proponer parámetros asistenciales.
Métodos Estudio observacional mediante modelamiento matemático con análisis envolvente de datos e índice de Malmquist, utilizando datos de recursos y producción asistencial antes (2019) y durante (2021) la pandemia.
Resultados Se analizaron 149 hospitales docentes, 32 se consideraron eficientes. Hubo una baja productividad en todas las regiones y naturalezas jurídicas. Para que todos los hospitales ineficientes alcancen la frontera de eficiencia generada por el modelamiento, es necesario aumentar la producción en 2.205.856 (96,5%) hospitalizaciones y 872.264 (107,4%) cirugías.
Conclusión La caída de la productividad hospitalaria fue consecuencia del compromiso social de los hospitales durante la pandemia, con cambios en el estándar de atención. El modelo matemático utilizado permite generar parámetros para una recuperación eficiente de atención y puede aplicarse a planificación hospitalaria.
Palabras clave
Hospitales docentes; Investigación Operativa; Benchmarking; Eficiencia Organizacional; Covid-19
INTRODUCTION
Coping with the COVID-19 pandemic required a joint effort from society and national public health. Federal, state and municipal health resources were pooled for the hiring of healthcare professionals and acquisition of medical equipment, such as oxygen, sedatives and personal protective equipment.1 Given the high transmissibility and potential severity in the first two years of the pandemic, there was a 47.0% increase in intensive care unit (ICU) beds and a 4.7% increase in other beds, in addition to the establishment of field hospitals and the reconfiguration of units for exclusive care for people with COVID-19.1 ,2
Teaching hospitals (THs) played a role in this process with different strategies, such as suspension of outpatient appointments and elective surgeries, the expansion of intensive care beds, reinforcement of biosafety protocols, the hiring of new professionals, training of healthcare teams, suspension of teaching activities and the development of research on the topic.3
It is worth noting that the adoption of pandemic response measures meant that treatment for other diseases was foregone,4 leading to changes in the profile of hospital admissions and productive efficiency. Productivity is defined as the ratio between the volume of outputs provided and inputs used by the same productive unit. Technical efficiency is measured by comparing the productivity of similar units, in order to assess the maximum production potential regarding the available inputs.5 There are different methods for analyzing productivity, including the least squares method , total factor productivity, stochastic frontier analysis and data envelopment analysis – DEA.5
DEA is a linear programming technique that measures the performance of productive units, Known as decision-making unit (DMU) , which consume multiple inputs – such as beds, equipment and human resources – to generate various outputs, such as hospitalizations, surgeries and consultations.6 mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale (in multiple input and multiple output situations DMUs that produce more with the least use of inputs are considered efficient (with a score of 100.0%). The linear combination of inputs and outputs from these efficient units forms an efficient or productive frontier, which serves as a performance benchmark for the others. It is worth highlighting that DEA provides pathways for inefficient units to become efficient, through increased production or input reduction. DEA is frequently used in the health sector and has been applied to Brazilian hospitals for different purposes: efficiency analysis,7 search for efficiency determinants8 and assessment of public policy performance.9
The impact of the COVID-19 pandemic on the productivity of general THs in Brazil has not been measured yet, nor has it been determined whether this impact occurred uniformly. Following the introduction of vaccination in Brazil, in 2021, and the end of the global health emergency due to COVID-19, in 2023, the epidemiological profile of hospital admissions in general THs gradually returned to pre -pandemic levels, with a predominance of chronic-degenerative diseases. 10 Thus, the productivity of general HEs must be restored in order to recover their role in providing high-complexity care in the national level.
This study aims to analyze the influence of the COVID-19 pandemic on the productivity of general THs in Brazil, by region and legal entity, and to propose parameters of care.
METHODS
Study design and setting
THs have changed their healthcare delivery patterns to cope with the pandemic. An observational and analytical study on the comparative productivity of general THs in Brazil, before (2019) and during (2021) the COVID-19 pandemic, according to region of the country and legal entity, was conducted using non-parametric linear programming modeling, data envelopment analysis (DEA).
Participants and study size
The study included all THs in Brazil, certified in 2019 and 2021, excluding specialized hospitals and maternity hospitals. The general THs were classified according to the legal entity of their management and grouped based on the Brazilian Institute of Geography and Statistics categorization: direct public administration; corporate entity (public company under private law and private company); and private non-profit entity. 11 General THs were also classified according to the number of beds: medium-sized hospitals (51 to 150 beds) and large-sized hospitals (over 150 beds).
Variables
For the mathematical model, variables were selected based on their regular use in similar articles 12 and their availability in the administrative databases of the Brazilian National Health System (Sistema Único de Saúde - SUS).
Input variables included: number of hospital ward beds, number of ICU beds, the specialized services index and hospital mortality rate (HMR). In order to calculate the specialized services index – a measure of the complexity provided – a panel of experts (managers and epidemiologists) was invited to assign a score from 1 to 5 for each procedure included in the SUS table for high-complexity procedures, taking into account the following criteria: complexity (level of professional expertise required), cost (expenditure of physical and financial resources) and procedure duration/hospitalization time (for surgical procedures, the duration of the procedure; for clinical procedures, the average length of stay). Each hospital received a score based on the sum of its qualifications, weighted by complexity. This methodology was described in a previous study. 13 HMR is the percentage of deaths (from any cause) regarding the total number of hospital discharges and deaths.
The output variables were: number of hospitalizations adjusted for hospital complexity and the number of surgeries. For adjusting hospitalizations, the number of hospitalizations for each hospital was multiplied by the ratio between the specialized services index and the national average of the same index.
Specific COVID-19 indicators (external to the model) were: COVID-19 incidence rate (number of confirmed cases per 100,000 inhabitants) and COVID-19 mortality rate (number of deaths due to the disease per 100,000 inhabitants).
Data sources
Data were obtained from the information systems of the Brazilian National Health System Information Technology Department (Departamento de Informática do Sistema Único de Saúde), with support from the Microdatasus package. 14 These included: National Health Establishment Registry (Cadastro Nacional de Estabelecimentos de Saúde), for inputs and qualifications; Hospital Information System (Sistema de Informações Hospitalares), for output data; and Mortality Information System (Sistema de Informação sobre Mortalidade), for HMR. Specific COVID-19 indicators were obtained from the Coronavirus Panel (https://covid.saude.gov.br) of the Ministry of Health. Data from January to December of 2019 and 2021 were used, accessed in July 2023.
Mathematical method: DEA-Malmquist
Efficiency scores for general THs and output parameters for recovery planning were calculated by constructing efficient frontiers, based on data from 2019 and 2021, using DEA models.
The classical DEA model with variable returns to scale (VRS) was chosen due to the differences in scale among the DMUs. The distance between the observed DMU and its feasible projection point on the Pareto-efficient frontier (Russel measure 15 ) was used to calculate the efficiency score of inefficient DMUs and to define parameters of care. It is worth noting that in order to propose these parameters, the efficient frontier incorporated two distinct epidemiological scenarios: before and during the pandemic. The study adopted an output-oriented approach, given that efficiency improvement in Brazilian public health is achieved by increasing output, rather than reducing inputs.
The DEA-Malmquist index 16 assessed the displacement of the efficient frontier between the two different periods – 2019 and 2021 – by calculating the distances between each observed DMU and both frontiers. Index values greater than 1.00 indicate productivity growth; values less than 1.00 indicate a decline.
The DEA-Malmquist index was decomposed to evaluate two distinct sources of productivity variation: change in technical efficiency (catchup) and change in technological efficiency (frontier-shift). The former indicates a change in the relative efficiency of the same DMU over time. The later represents the overall shift of the frontier, indicating either contraction or expansion, i.e., a decline or improvement in the productivity of the units as a whole.
The DEA-Malmquist and Russel measures were constructed in spreadsheets and programmed using the Solver add-in for Microsoft® Excel.
Ethical aspects
The study Implementation of operational research models in hospital planning and management, from which this article was derived, was approved by the Research Ethics Committee of the Instituto de Estudos em Saúde Coletiva da Universidade Federal do Rio de Janeiro, CAAE: 41480720.6.0000.5286, on March 9, 2021.
RESULTS
A total of 213 THs was identified, 64 (30.0%) of which were excluded from the analysis because they were specialized hospitals and maternity hospitals. Among the general THs, 103 (69.1%) hospitals were located in the Southeast and South regions and 131 (87.9%) were large hospitals. Regarding the incidence and specific mortality due to COVID-19, there was a predominance in the South and Midwest regions, followed by the Southeast region (Table 1).
Number of general teaching hospitals, by size, and incidence of COVID-19 cases and deaths, by region of the country, Brazil, 2021
Table 2 presents the inputs and output of Brazil’s general THs in the study period. Of the total number of beds in the country’s general THs (ward and ICU beds combined), the Southeast and South regions accounted for 71.5%, in 2019, and 71.4% in 2021. Between 2019 and 2021, there was a reduction of 604 (-1.4%) ward beds and an increase of 4,614 (51.9%) ICU beds. The supply of hospital admissions for high-complexity procedures, assessed by the specialized services index, increased by 1.5%. On the other hand, there was a decline of 299,547 (-11.6%) in hospitalizations adjusted for complexity and 78,431 (-8.8%) in surgeries performed. The HMR increased by 2.7 percentage points.
Total inputs and output of teaching hospitals before and after the COVID-19 pandemic, by region and legal entity, Brazil, 2019 and 2021
The reduction in ward beds was greater in hospitals in the South (-4.8%) and North (-4.1%) regions, while an increase was observed in the Northeast region (3.4%). The number of ICU beds increased across all regions, ranging from 29.0% in the Northeast region to 79.5% in the Midwest region. As for output, the South and Southeast regions were the most affected by the pandemic, with a reduction in hospitalizations (-17.4% and -10.4%, respectively) and surgeries (-16.3% and -9.6% %, respectively). Only the North region showed an increase in output: 9.9% in hospitalizations and 24.4% in surgeries. It is also worth highlighting that the North region had a high HMR, even before the pandemic (10.2% in 2019), and the impact of the pandemic on HMR in the South region (from 6.1% to 10.1%).
Regarding legal entity, public hospitals under direct administration, corporate hospitals and private non-profit hospitals accounted for 43.0%, 22.8% and 34.2% of the country’s general THs, respectively. There was a reduction in ward beds in corporate hospitals (-3.0%) and in private non-profit hospitals (-3.6%), and an increase in public hospitals under direct administration (1.5%). General THs of all legal entity types increased the number of ICU beds: 54.4% in direct administration public hospitals; 42.5% in corporate hospitals; and 55.1% in private, non-profit hospitals. Regarding output, all general THs showed a reduction in hospitalizations and surgeries, respectively: -10.1% and - 4.5% in direct administration public institutions; -21.1% and -17.2% in corporate hospitals; and -9.1% and -6.5% in private non-profit hospitals. The HMR increased by 2.4%, 1.7%, 3.8% in public, corporate and private non-profit hospitals, respectively, with the highest rates found among private non-profit hospitals (8.4% in 2019 and 12.2% in 2021).
Table 3 shows the efficiency results of general THs, in 2019 and 2021, as well as the frontier shift over the period (Malmquist index). It could be seen an increase in the average relative efficiency scores of general THs in the Midwest, Northeast and North regions. In 2019, general THs in the Northeast region were the most efficient (58.1%); in 2021, The most efficient general THs were in the Northeast and Midwest regions (59.4%). General THs in the North region remained the least efficient in both years analyzed (36.7%, in 2019, and 42.8%, in 2021), despite the increased output during this period. During the pandemic, direct administration general THs were more efficient in the North and South regions; corporate general THs, in the Midwest and Southeast regions; and private non-profit general THs, in the Northeast region. Despite the observed increase in general TH efficiency (catch-up 1.01), the productivity frontier contracted (Malmquist 0.77; frontier-shift 0.76) in all regions (Malmquist ranging from 0.67 to 0.92; frontier-shift from 0.66 to 0.81) and across all legal entity types (Malmquist from 0.62 to 0.88; frontier-shift from 0.58 to 0.88). Only direct administration public hospitals in the Midwest region showed a Malmquist index greater than 1.00 (equal to 1.01), however, with a frontier-shift of 0.85.
Average efficiency of teaching hospitals and Malmquist index, by region of the country and legal entity, Brazil, 2019 and 2021
32 efficient units were identified, as benchmarks for inefficient general THs (Supplementary Material). A total of 15 general THs was efficient in both years analyzed. Among the benchmarks, 24 (75.0%) were large hospitals, 16 (50.0%) were located in the Southeast region and 11 (34.4%) were private, non-profit hospitals.
Table 4 presents the expected output projection for all inefficient general THs to reach the best practice frontier as of 2021. In a recovery scenario, given the current availability of inputs, these hospitals should increase their output by 2,205,856 (96.5%) hospitalizations and 872,264 (107.4%) surgeries. Simultaneously, with the end of the global health emergency due to COVID-19, a decrease in the HMR from 9.2% to 4.0% is expected, particularly in the North region (from 11.6% to 3.5%) and among private non-profit hospitals (from 12.2% to 4.0%).
Expected projection for service output and the estimated mortality rate of teaching hospitals after the COVID-19 pandemic, by region and legal entity, Brazil, 2021
DISCUSSION
In this study, a decline in the productivity of general THs in Brazil across all regions and for all legal entity types was observed, in the period from 2019 to 2021. It is worth highlighting that this decline in productivity was due to the social commitment of general THs rather than a failure in public policy. In other words, the increase in the supply of inputs (ICU beds) and the drop in output (hospitalizations and surgeries) occurred in response to the strategic actions required to combat the pandemic. Similarly, the observed increase in HMR during the pandemic, was an indicator of the frequency of COVID-19 hospitalizations in the units under analysis, not a reflection of the quality of care provided.
The reduction in production, with less attention to other diseases, was a global phenomenon. 17 Taking into consideration the central role of THs, responsible for 35.3% of high-complexity production in the country,2 the decline in production had a significant impact on the performance of more complex procedures. In 2021, the country’s general THs reduced heart surgeries by 14.8%, cancer surgeries by 8.4%, radiotherapy by 96.8% and transplants by 18.6%.2 Estimates indicated a backlog of 60,000 cardiovascular surgeries due to the pandemic, further increasing the surgical waiting list. 18 The reduction in output, at all levels of care, had a widespread effect on the health of the Brazilian population; for example, mortality from cardiovascular diseases increased by 6.9% in the same period.6 In addition, teaching and research activities were also compromised, with the exception of academic work aimed at COVID-19.
In order to restore productivity, the number of hospitalizations and surgeries needs to nearly double nationwide, and managers of each health unit (as well as those from municipalities and states) can plan how much they need to increase output. It is worth highlighting that the activation of 4,814 (51.9%) ICU beds (above the national average of 46.7%) supports the high-complexity production role of general THs as they resume these procedures. In other words, additional inputs to support high-complexity care promote increased output, as long as the units remain at the efficient frontier.
As a limitation of the study, it is worth mentioning the absence of qualitative models to structure the problem before the mathematical modeling. Studies19,20, 21 suggest the use of associated methodologies (multimethodology) to contexts and preference assessment before choosing mathematical models. Regarding the model variables, there was a lack of accurate information on the hiring of human resources, which was important for productivity during the pandemic, and could have been included in the model. Data on teaching and research activities would also have enriched the analysis, since the volume of research, high resident-to-bed ratio (teaching intensity) and low resident-to-physician ratio (teaching dedication) are associated with increased efficiency. 22 Another limitation lies in the heterogeneity of information among Brazilian regions. A study on excess mortality during the pandemic 23 suggests greater diagnostic challenges and underreporting of deaths due to COVID-19 in Northeastern capitals compared to Southeastern capitals. Research using network DEA to study the capacity and structures for addressing COVID-19 showed that the North and Northeast regions were more vulnerable during the pandemic due to a lack of structure (ICU beds) and lower capacity to reallocate resources (doctors and ventilators) to meet the excess demand from people with COVID-19. 24
As a recommendation for further research, the DEA-Malmquist model could be applied in the years following this study, given the introduction of vaccines nationwide (starting in 2021), and the emergence of successive waves and new strains of COVID-19. To improve the study of reference units, qualitative and quantitative research can identify patterns of successful strategies employed during the pandemic. The use of this tool, along with a better characterization of the output of these hospitals, will allow for the estimation and monitoring of the gradual recovery of service delivery in line with the evolving needs and demands of Brazilian society.
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Apêndice
Supplementary Table 1 Efficient General Teaching Hospitals by region, state, legal entity, and size, Brazil, 2019 and 2021| Hospital name | Region | State | Legal entity | Size | Reference (years) |
|---|---|---|---|---|---|
| Fundação Hospital Adriano Jorge | North | AM | Public Administration | Large | 2019 and 2021 |
| Hospital Anchieta | Southeast | SP | Public Administration | Medium | 2019 and 2021 |
| Hospital da Baleia | Southeast | MG | Private Non-Profit Entity | Medium | 2019 and 2021 |
| Hospital da Restauração Governador Paulo Guerra | Northeast | PE | Public Administration | Large | 2019 and 2021 |
| Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto | Southeast | SP | Public Administration | Large | 2019 and 2021 |
| Hospital das Clínicas da Faculdade de Medicina de São Paulo | Southeast | SP | Public Administration | Large | 2019 and 2021 |
| Hospital das Clínicas da Universidade Federal de Pernambuco (EBSERHa) | Northeast | PE | Corporate Entity | Large | 2021 |
| Hospital de Base de São José do Rio Preto | Southeast | SP | Private Non-Profit Entity | Large | 2021 |
| Hospital de Caridade São Vicente de Paulo | Southeast | SP | Private Non-Profit Entity | Large | 2019 and 2021 |
| Hospital do Trabalhador | South | PR | Public Administration | Large | 2019 and 2021 |
| Hospital Estadual de Sumaré Dr. Leandro Franceschini | Southeast | SP | Public Administration | Large | 2021 |
| Hospital Geral do Grajau | Southeast | SP | Public Administration | Large | 2019 |
| Hospital Getúlio Vargas | Northeast | PE | Public Administration | Large | 2019 and 2021 |
| Hospital Municipal da Piedade | Southeast | RJ | Public Administration | Medium | 2019 and 2021 |
| Hospital Municipal Universitário de São Bernardo do Campo | Southeast | SP | Public Administration | Large | 2019 and 2021 |
| Hospital Nossa Senhora do Rócio | South | PR | Corporate Entity | Large | 2019 |
| Hospital Regional do Paranoá | Central-West | DF | Public Administration | Large | 2021 |
| Hospital Santa Lucinda da Pontifícia Universidade Católica de São Paulo | Southeast | SP | Private Non-Profit Entity | Medium | 2021 |
| Hospital Universitário Cajuru da Pontifícia Universidade Católica do Paraná | South | PR | Private Non-Profit Entity | Large | 2019 and 2021 |
| Hospital Universitário Clemente de Faria da Universidade Estadual de Montes Claros | Southeast | MG | Public Administration | Large | 2021 |
| Hospital Universitário da Universidade Federal do Maranhão (EBSERHa) | Northeast | MA | Corporate Entity | Large | 2021 |
| Hospital Universitário da Universidade Federal de Sergipe (EBSERHa) | Northeast | SE | Corporate Entity | Medium | 2019 |
| Hospital Universitário da Universidade Federal de São Carlos (EBSERHa) | Southeast | SP | Corporate Entity | Medium | 2019 and 2021 |
| Hospital Universitário de Lagarto da Universidade Federal de Sergipe (EBSERHa) | Northeast | SE | Corporate Entity | Medium | 2019 |
| Hospital Universitário Evangélico Mackenzie | South | PR | Private Non-Profit Entity | Large | 2021 |
| Hospital Universitário Júlio Muller da Universidade Federal do Mato Grosso (EBSERHa) | Central-West | MT | Corporate Entity | Medium | 2021 |
| Hospital Universitário Walter Cantídio da Universidade Federal do Ceará (EBSERHa) | Northeast | CE | Corporate Entity | Large | 2019 and 2021 |
| Irmandade Nossa Senhora das Mercês de Montes Claros | Southeast | MG | Private Non-Profit Entity | Large | 2019 |
| Santa Casa de Misericórdia de Fortaleza | Northeast | CE | Private Non-Profit Entity | Large | 2019 and 2021 |
| Santa Casa de Misericórdia de Limeira | Southeast | SP | Private Non-Profit Entity | Large | 2019 |
| Santa Casa de Misericórdia do Pará | North | PA | Private Non-Profit Entity | Large | 2021 |
| Santa Casa de Misericórdia de Belo Horizonte | Southeast | MG | Private Non-Profit Entity | Large | 2021 |
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ASSOCIATED ACADEMIC WORK
Article derived from a doctoral thesis to be submitted by Henrique de Castro Rodrigues to the Postgraduate Program in Production Engineering, at the Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia, da Universidade Federal do Rio de Janeiro.
Edited by
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Associate editor:
Elisângela Aparecida da Silva Lizzi
Publication Dates
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Publication in this collection
06 Dec 2024 -
Date of issue
2024
History
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Received
12 Dec 2023 -
Accepted
17 July 2024
