Digitalization as the Basis for the Automation of the Treatment Process

DOI

10.2991/assehr.k.201105.033How to use a DOI?

Keywords

digitalization, management of the treatment process, visualization

Abstract

The public health crisis and the pandemic threat have shown the urgent need to improve the management and implementation of medical care. Digitalization is seen as an effective tool for managing the disease treatment process. The problem is solved on the basis of the influence of factors x on the target indicator Y. The aim of the study is to improve the efficiency of treatment process management based on digital models. To achieve this goal, one needs to solve the following tasks: 1.To develop a methodology for building a strategy for managing the treatment process based on a digital model. 2 Visualize the implementation of a treatment strategy based on a digital model. For modeling, statistical information is used that reflects the values of factors and patient targets, using multivariate correlation and regression analysis. On the example of a digital model of a patient, a systematization of a set of numerical values characterizing the patient’s condition for the corresponding disease has been carried out. It is shown that the visualization of digital information in projections with numerical marks provides the necessary effective communication between the user and the computer and allows you to make unambiguous decisions in determining and implementing the treatment process management strategy. This creates the prerequisites for automating the research process in the treatment of diseases.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - V.S. Borovik
AU  - V.V. Borovik
PY  - 2020
DA  - 2020/11/06
TI  - Digitalization as the Basis for the Automation of the Treatment Process
BT  - Proceedings of the Research Technologies of Pandemic Coronavirus Impact (RTCOV 2020)
PB  - Atlantis Press
SP  - 176
EP  - 181
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201105.033
DO  - 10.2991/assehr.k.201105.033
ID  - Borovik2020
ER  -