The spread of new diseases in the face of growing urbanization can lead to a high peak load on the healthcare system. Predicting the emergence of a new disease and its potential danger for humans is a difficult task. In this regard, it is important to support the adoption of operational decisions on the hospitalization of patients in any new pandemic. A methodology is proposed that combines support for decision-making on hospitalization, based on solving an optimization problem for distributing the load on hospitals, analyzing heterogeneous data to form an array of constraints when solving an optimization problem, and exchanging electronic medical records between the main actors to provide complete information about the patient. The paper analyzes the decision support process during hospitalization to determine the main parameters that should be taken into account in the context of the optimization problem when making a decision on hospitalization.