We firmly believe in an evidence-based approach, and therefore our team regularly publishes new research to provide evidence for the utility of our approach. Read more about research published below.
Hutchison K, et al 2021
Patients with indicators of primary biliary cholangitis were not investigated in line with guidance, and even those with appropriate tests in primary care were frequently not referred.
Kitchin A, et al.
Demonstrated the delay in referral and diagnosis for genetic haemochromatosis – indicating the potential for a ‘case-finding’ approach.
Wesley E, et al.
Demonstrated the potential utility of longitudinal analysis of time trends in laboratory data as a powerful tool for case finding. The ability to perform these searches rapidly is a unique feature of the system developed.
Creamer J, et al.
The system developed can demonstrate clearly that young adults are frequently under investigated for liver disease, and also that they can be individually identified and recalled for treatment.
Owen C, et al. 2023
Demonstrates that the system developed can identify patients at high risk of end stage liver disease – these are patients that have a significant risk of presenting to secondary care with irreversible / fatal liver disease in the next few months to years.
Gormley S, et al. 2023
Demonstrates that the system developed can identify patients at high risk of end stage liver disease – these are patients that have a significant risk of presenting to secondary care with irreversible / fatal liver disease in the next few months to years.
Mohamed A, et al. 2023
This abstract showed that the system can identify patients on the basis of longitudinal data with persistently abnormal liver function tests who have not had a ‘non-invasive liver screen’ – panel of blood tests to identify laboratory definable liver disease.
Jobson T, et al. 2023
This abstract showed that the system can identify patients on the basis of longitudinal data with persistently abnormal liver function tests who have not had a ‘non-invasive liver screen’ – panel of blood tests to identify laboratory definable liver disease.
Mohamed A, et al. 2023
This presentation demonstrated the utility of the case finding approach to target patients in deprived areas with Hepatitis B.
Mohamed A, et al. 2023
This presentation demonstrated the utility of the case finding approach to target patients in deprived areas with Hepatitis B
Creamer J, et al. 2023
This abstract showed that the system can use the Index of Multiple Deprivation to target case-finding in areas of deprivation.
Owen C, et al. 2023
This abstract showed that the system can use the Index of Multiple Deprivation to target case-finding in areas of deprivation.
Jobson T, et al. 2024
This abstract is the first real world data from the Somerset Liver Improvement Project – using the case finding data base to identify patients with PBC.
Owen C, et al. 2024
This abstract is the first real world data from the Somerset Liver Improvement Project – using the case finding data base to identify patients with PBC.
Saunsbury E, et al. 2024
This abstract shows the potential utility of the case finding database for missing Hepatitis C patients
Mohamed A, et al. 2024
This abstract shows the potential utility of the case finding database for missing Hepatitis C patients
Owen C, et al. 2024
This abstract presents the first real world data from the Somerset Liver Improvement Project including new pathways of care.
Owen C, et al. 2024
This abstract presents the first real world data from the Somerset Liver Improvement Project including new pathways of care.
Miller R, et al. 2024
This abstract shows the potential power of hepatoSIGHT in generating cohorts for clinical trials
Miller R, et al. 2024
This abstract shows the potential power of hepatoSIGHT in generating cohorts for clinical trials
Saunsbury E, et al. 2024
This abstract shows how hepatoSIGHT may be used to identify undiagnosed hepatitis D patients in the UK.
Owen C, et al. 2025
This abstract shows the potential power of hepatoSIGHT to allow clinicians to rapidly and easily identify patients for care.