Lyapin V.A.Novosibirsk Research Hygiene Institute of the Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Novosibirsk Research Hygiene Institute of Rospotrebnadzor), Novosibirsk, Russian Federation v.a.liapin@mail.ru https://orcid.org/0000-0002-9034-1727
Kulikova O.M.Novosibirsk Research Hygiene Institute of the Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Novosibirsk Research Hygiene Institute of Rospotrebnadzor), Novosibirsk, Russian Federation ya.aaaaa11@yandex.ru https://orcid.org/0000-0001-9082-9848
Subject This article deals with the issues of formation of and changes in the flow of patients to the medical organizations of the region. Objectives The article aims to develop a model of patient flow formation in medical organizations of the region, which takes into account the patient's priorities in choosing the method and conditions of medical services, and the disease stage and severity. Methods The initial data of the research are determined on the basis of expert information, patient interviews, and a statistical data and literature sources analysis. A chronic non-communicable disease has been selected for simulation. For the study, we used simulation and Markov processes with discrete time. Results The article presents a developed simulation model, which can help predict the patient flow intensity in time cycles and plan and use resources of medical organizations of the region. Conclusions The flow rate of patients determines the need of medical organizations in financial resources. On the basis of the performed modeling, the problem of improvement of funding of medical organizations can be solved and the workload of various parts of the health care system can be predicted. The developed model contributes to making more precise and effective decisions concerning the planning of resource supply of health care and improving the financial stability of the medical organization.
Keywords: health care resources, medical services, treatment adherence, patient's priorities, agent-based model
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