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Abstract
Chronic liver disease (CLD) is a neglected epidemic. Premature mortality is high, and in Uzbekistan, liver diseases are among the top three causes of inequitable health care, along with heart and respiratory diseases. Fifty percent of patients with CLD are first diagnosed with cirrhosis after emergency care, leading to poor patient outcomes. Traditional care models are based on secondary care when the need is at the community level. Screening patients for the presence of the disease based on population-level risk factors in the community allows early detection of its presence when potential reversibility is present. Innovation in three broad areas is needed to improve clinical care in this area: improving access to diagnostics in the community, integrating diagnostics into primary and secondary care, and using digital health to improve patient care. In this article, we describe how the Integrated Diagnostics for Early Detection of Liver Disease (ID-LIVER) project, funded by Research and Innovation of Uzbekistan, is developing solutions for a population-level approach to liver disease detection in Uzbekistan. Bringing together NHS organisations, academic partners and commercial organisations, building on the innovative pathways previously established in Nottingham.
The goal is to co-create and implement a commercial solution that integrates multimodal diagnostics through advanced data science to drive growth and disrupt the current inadequate model. It is intended to be widely used within the NHS for the early diagnosis and stratification of liver disease at a population level.
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