Machine Learning for Digital Shadow Design in Health Insurance Sector

The digital transformation process in organizations has accelerated significantly in recent years; the COVID-19 pandemic was a catalyst that highlighted the need for digitalization in all sectors. In the case of the health sector, this process is complex due to the processes inherent in health care...

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Bibliographic Details
Published in:Mobile networks and applications 2024-02, Vol.29 (1), p.221-234
Main Authors: Rodríguez-Aguilar, Román, Marmolejo-Suacedo, José-Antonio, Rodríguez-Aguilar, Miriam, Marmolejo-Saucedo, Liliana
Format: Article
Language:English
Subjects:
Artificial intelligence
Chronic illnesses
Communications Engineering
Computer Communication Networks
COVID-19
Decision making
Diabetes
Digital technology
Digital twins
Digitization
Disease transmission
Electrical Engineering
Engineering
Enterprise resource planning
Health care
Health insurance
Health services
Hypertension
Insurance
Insurance industry
IT in Business
Machine learning
Mortality
Networks
Neural networks
Organizations
Overweight
Pandemics
Patients
Real time operation
Shadows
Supply chains
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Summary:The digital transformation process in organizations has accelerated significantly in recent years; the COVID-19 pandemic was a catalyst that highlighted the need for digitalization in all sectors. In the case of the health sector, this process is complex due to the processes inherent in health care as well as the integration of multiple sectors that allow the provision of health services. A first approach towards the construction of a Digital Twin in health organizations is a Digital Shadow that allows an orderly transition towards digital operation in real time. This paper presents a first approach to the design of a Digital Shadow for the health insurance sector and specifically for the care of patients diagnosed with COVID-19 through the implementation of an analytical intelligence system based on machine learning models to forecast and monitor to patients who represent catastrophic cases for the insurer.
ISSN:1383-469X
1572-8153
DOI:10.1007/s11036-023-02289-2