ABSTRACT
In the aftermath of the COVID-19 health crisis, hospitals around the world needed to stock up on resources to satisfy the high demand from patients. Peru was one of the countries most affected by the pandemic due to its fragile health system that led it to be the country with the highest mortality rate in the world with 554 deaths per million inhabitants. For this reason, it was imperative to implement measures to mitigate the state of emergency, such as the allocation of health resources to hospitals in all regions of the country. This research aimed to measure the efficiency of the Peruvian regions in the management of hospital resources against COVID-19 at the end of the year 2020. The two stage DEA model, the Data Envelopment Analysis in the first stage was used to estimate the efficiency and the Logit regression model in the second stage to find the incidence of internal and external factors. To this end, the variables number of doctors, number of nurses, oxygen, number of medications, number of COVID tests, number of positive cases, ICU beds available as inputs, and as outputs the mortality rate and recovery rate were selected;then, 8 models were formulated that grouped these variables sequentially to estimate efficiency. Results indicated that 53.33% of the regions were inefficient in all the proposed models, only Amazonas and Huancavelica regions were efficient in 2020, when comparing the average efficiency of each model, it was possible to distinguish that the internal variables of the number of nurses and oxygen causes a significant change in efficiency. Logit regression concluded that the number of nurses and the total population are significant influencing variables, while the external variable GDP per capita had a negative effect on the efficiency scores. Important findings show a notable deficiency in the use of human resources and poor management of investment in health resources to face the COVID-19 pandemic in Peru. © 2022 ACM.