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Intelligent Liver Function Testing: Working Smarter to Improve Patient Outcomes in Liver Disease.
- Source :
-
The journal of applied laboratory medicine [J Appl Lab Med] 2020 Sep 01; Vol. 5 (5), pp. 1090-1100. - Publication Year :
- 2020
-
Abstract
- Chronic liver disease (CLD) is a significant health problem affecting millions of people worldwide. In Scotland, CLD is a major cause of premature mortality. Liver function tests (LFTs) are a panel of frequently requested blood tests which may indicate liver disease. However, LFTs commonly contain at least one abnormal result, and abnormalities are rarely investigated to the extent recommended by national guidelines. The intelligent Liver Function Testing (iLFT) pathway is a novel, automated system designed to improve early diagnosis of liver disease. Initial abnormal LFT results trigger a cascade of reflexive testing to help identify the cause of any liver dysfunction. Algorithms combine these results with demographic and clinical data (such as patient age, body mass index, and alcohol intake) and fibrosis estimates to produce an electronic diagnosis and management plan. The pilot trial demonstrated that iLFT increased diagnosis of liver disease whilst remaining cost-effective. As such, iLFT has been fully operational across our region (NHS Tayside, Scotland) since August 2018. In the first year, iLFT generated over 2000 diagnoses from 1824 patient samples with an abnormality in the initial LFTs. The majority of these patients could be safely managed in primary care. iLFT allows maximal value to be obtained from liver blood tests across biochemistry, virology, immunology, and hematology with only minor changes to working practices. 'Intelligent', algorithm-led testing pathways break down the barrier between clinical and laboratory medicine and offer solutions to many of the challenges experienced in modern healthcare systems.<br /> (© American Association for Clinical Chemistry 2020.)
Details
- Language :
- English
- ISSN :
- 2576-9456
- Volume :
- 5
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- The journal of applied laboratory medicine
- Publication Type :
- Academic Journal
- Accession number :
- 32916711
- Full Text :
- https://doi.org/10.1093/jalm/jfaa109