Lobo Maria Stella de Castro, Lins Marcos Pereira Estellita, Rodrigues Henrique de Castro, Soares Gabriel Martins
Universidade Federal do Rio de Janeiro, Instituto de Estudos em Saúde Coletiva, Rio de Janeiro, RJ, Brazil.
Universidade Federal do Estado do Rio de Janeiro, Escola de Engenharia de Produção, Rio de Janeiro, RJ, Brazil.
Epidemiol Serv Saude. 2024 Dec 6;33:e20231176. doi: 10.1590/S2237-96222024v33e20231176.en. eCollection 2024.
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.
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.
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.
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.
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%.
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.
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).
按地区和法律实体分析新冠疫情对巴西综合教学医院生产力的影响，并提出护理参数。
这是一项采用数据包络分析和曼奎斯特指数进行数学建模的观察性研究，使用了疫情前（2019年）和疫情期间（2021年）的投入数据和医疗产出数据。
共分析了149家综合教学医院，其中32家被认为是高效的。所有地区和法律实体的生产力均有所下降。为使所有低效医院达到模型产生的效率前沿，需要增加2,205,856例（96.5%）住院治疗和872,264例（107.4%）手术。
医院生产力下降是由于疫情期间医院的社会承诺以及护理提供模式的改变。所使用的数学模型能够生成参数，以促进公共卫生紧急情况结束后护理服务的高效恢复，并且可应用于医院规划。
新冠疫情导致巴西所有地区和所有法律实体类型的综合教学医院生产力下降。为了恢复，这些医院需要将平均住院治疗增加96.5%，手术增加107.4%。
该研究提出了一种方法，在公共卫生紧急情况结束后的护理恢复场景中，通过定义效率得分和计算参数，可在该国医疗服务管理中进行调整和复制。
后续年份进行的额外定性分析以及数据包络分析 - 曼奎斯特指数的应用将验证动态规划，该规划考虑了多种波动以及影响生产力的新因素和背景（如疫情）的影响。
