Intelligent Liver Function Testing: Working Smarter to Improve Patient Outcomes in Liver Disease

Area of Science:

• Hepatology
• Clinical Biochemistry
• Health Informatics

Background:

• Chronic liver disease (CLD) is a global health concern and a leading cause of premature death in Scotland.
• Liver function tests (LFTs) frequently show abnormalities, but investigations often fall short of national guidelines.
• Current diagnostic pathways for liver dysfunction lack efficiency and integration.

Purpose of the Study:

• To introduce and evaluate the intelligent Liver Function Testing (iLFT) pathway, an automated system for early CLD diagnosis.
• To assess the effectiveness and cost-effectiveness of the iLFT pathway in improving liver disease diagnosis and management.
• To integrate laboratory data with clinical information for automated diagnosis and care planning.

Main Methods:

• Development of an automated iLFT pathway triggering reflexive testing upon initial abnormal LFT results.
• Utilization of algorithms to combine LFT results with demographic, clinical, and fibrosis data for diagnosis.
• Implementation and operationalization of the iLFT pathway in NHS Tayside, Scotland.

Main Results:

• The iLFT pathway increased the diagnosis of liver disease in a pilot trial.
• In its first year, iLFT generated over 2000 diagnoses from 1824 patient samples.
• The majority of diagnosed patients were suitable for primary care management, demonstrating cost-effectiveness.

Conclusions:

• The iLFT pathway offers a novel, automated solution for early liver disease diagnosis and management.
• This system maximizes the value of liver blood tests by integrating diverse data streams.
• Algorithm-led testing pathways bridge clinical and laboratory medicine, addressing modern healthcare challenges.