BACKGROUND: The fragility of healthcare systems worldwide had not been exposed by any pandemic until now. The lack of integrated methods for bed capacity planning compromises the effectiveness of public and private hospitals' services. OBJECTIVES: To estimate the impact of the COVID-19 pandemic on the provision of intensive care unit and clinical beds for Brazilian states, using an integrated model. DESIGN AND SETTING: Experimental study applying healthcare informatics to data on COVID-19 cases from the official electronic platform of the Brazilian Ministry of Health. METHODS: A predictive model based on the historical records of Brazilian states was developed to estimate the need for hospital beds during the COVID-19 pandemic. RESULTS: The proposed model projected in advance that there was a lack of 22,771 hospital beds for Brazilian states, of which 38.95% were ICU beds, and 61.05% were clinical beds. CONCLUSIONS: The proposed approach provides valuable information to help hospital managers anticipate actions for improving healthcare system capacity.

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While the estimate of hospital costs concerns the past, its planning focus on the future. However, many public hospitals in low and middle-income countries don’t have robust accounting health systems to evaluate and project their expenses. In Brazil, public hospitals are funded based on government estimates of available hospital infrastructure, historical expenditures and population needs. However, these pieces of information are not always readily available for all hospitals. To solve this challenge, we propose a flexible simulation-based optimisation algorithm that integrates this dual task: estimating and planning hospital costs. The method was applied to a network of 17 public hospitals in Brazil to produce the estimates. Setting the model parameters for population needs and future hospital infrastructure can be used as a cost-projection tool for divestment, maintenance, or investment. Results show that the method can aid health managers in hospitals’ global budgeting and policymakers in improving fairness in hospitals’ financing.

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