Experience of a Multidisciplinary Hospital in Implementing Organizational Development Measures Using Artificial Intelligence Technologies

Relevance. Currently, an essential factor in the success of medical organizations is the presence of an effective system of organizational development, which provides for the use of modern technologies in the implementation of various improvement initiatives. From the standpoint of organizational development, there is great potential for the analysis and prevention of various undesirable events in a medical organization, including patient falls. This task is currently one of the most pressing, both in terms of optimizing the treatment and diagnostic process and in the economic aspect. The paper presents the experience of the N.V. Sklifosovsky Research Institute for Emergency Medicine of the Department of Health of Moscow in using artificial intelligence technologies to reduce the risks of undesirable events associated with a patient’s stay in a hospital (in particular, patient falls), and, as a result, to reduce the time of a patient’s stay in a hospital and the costs of his treatment. The paper also considers the impact of the use of artificial intelligence technologies on the possibility of reducing the workload of nursing staff.

Aim of Study. To study the trend in the number of cases of adverse events associated with the stay of patients (in particular, patient falls) when using artificial intelligence technologies for their analysis, and to determine the potential economic efficiency of using artificial intelligence in the management of adverse events for the emergency hospital complex (EHC) of a multidisciplinary hospital.

Material and Methods. An analysis of 34,876 cases of patient stay in the emergency and diagnostic departments of the N. V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow Health for the purpose of identifying cases of adverse events (patient falls). The obtained data were examined in their economic and social aspects using statistical methods.

Results. Based on the results of the analysis of data obtained using artificial intelligence technologies, a trend was identified for the occurrence of adverse events associated with the stay of patients in the hospital (patient falls) on the territory of the emergency hospital complex. As a result of the development and implementation of a number of organizational measures, the identified trend was leveled. A medical and social portrait of a patient at risk of falling was compiled, which, in turn, also contributed to improving the statistics of adverse events.

Conclusions. Artificial intelligence is not only an effective tool in the management of adverse events, but also improves the quality of the treatment and diagnostic process as a whole. 

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