Health Informatics research

About us

The University of Liverpool’s Health Data Science Network brings together more than 50 academics from across three faculties. The Network will develop an ambitious programme of interdisciplinary research to address some of the most challenging issues facing public health and health delivery systems globally.

The vision for the Network is to improve patient care and public health through the development of a Learning Health System. This system will link information systems to innovative health informatics research. The Our Healthcare Data Laboratory, established as part of the Connected Health Cities Programme, will drive the development of the Learning Health System through iterative cycles of data collection, analyses, dissemination, feedback and refinement.

The Healthcare Data Laboratory will work collaboratively with partners across disciplines, institutions and sectors to maximise the use of health, biological, clinical, environmental and social data sources.

The analytical and methodological expertise within the Network will:

  • Harness the power of large and complex datasets
  • Build collective intelligence through interdisciplinary research and collaboration with academic, NHS and industry partners
  • Develop a model for a Learning Health System that is scalable.
  • Improving patient care and outcomes

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    The Healthcare Data Laboratory will use novel informatics approaches to answer important clinical and public health questions; key drivers for which include:

    • Improved health outcomes for patients
    • Enhanced quality of care
    • Reduced costs and inefficiencies.

    A programme of engagement with NHS clinicians across the North West Coast is supporting the iterative co-production of clinically validated analytics and data visualisations for defined patient pathways as part of the Connected Health Cities project.

    Find out more about the Connected Health Cities Programme.

  • Optimising personalised medicines research

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    Linking our health informatics and our clinical pharmacology expertise we will revolutionise our approach to stratified medicine.

    Insights derived from linkage of datasets will provide greater insight and understanding of how a drug works and who is most likely to benefit from it. Key drivers of this work include:

    • More robust methods for the identification of patients suitable for recruitment into stratified medicine research cohorts
    • More effective application of personalised medicines
    • More effective monitoring of the effects of personalised medicines over time.

    Read more about the University’s work in the area of stratified medicine.

  • Controlling infectious disease

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    Already recognised for its international research excellence, our infectious diseases research programme will continue to build on our expanding network of biomedical and health informatics expertise.

    Major programmes including the INTEGRATE, and SAVSNET are already harnessing the power of large and complex datasets in monitoring and predicting patterns of disease outbreak; modelling human risk for factors such as anti-microbial resistance; and predicting the effects of climate change on the spread of disease.

    Key drivers for this work include:

    • Improving predictability of and preparedness for disease outbreak
    • Combatting and preventing antimicrobial resistance
    • Developing effective methods for the evaluation of new vaccines

    Read more about the University’s work in infectious diseases.

  • Reducing health inequalities

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    Public Health is another strong area of research within the University that is spurring the development of innovative analytics.

    Building a robust evidence-base to reduce health inequalities, our teams are deploying novel data resources and expertise to demonstrate the health inequalities effects of public policy decisions. The team are extracting, managing, and linking data from diverse datasets for relevant analytics including consumer, finance and welfare datasets not routinely used in applied public health research.

    Through a collaboration between the NIHR CLAHRC, the ESRC funded Consumer Research Data Centre and 10 local authorities across the North West we have established an Integrated Longitudinal Research Resource (ILRR) of linked neighbourhood datasets. This collaboration will enable the tracking of determinants of health and health outcomes along with novel neighbourhood level contextual indicators.

    The key drivers for this work include:

    • Reducing health disparities and health equity challenges
    • Identifying effective interventions and policies to reduce health inequalities
    • The development and evaluation of novel methodology for the analysis of the “next-generation” of genome-wide association studies (GWAS) of complex human traits.
  • Powering novel analytics, methods and standards

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    The University has one of the largest Departments of Biostatistics in the UK undertaking cutting edge research in areas such as joint modelling of longitudinal and time-to-event data, multivariate analysis, multi-source evidence synthesis, and statistical genetics and pharmacogenetics.

    Working collaboratively with geographers with expertise in spatial statistical analysis and a cross-faculty Analytics Special Interest Group, the Department offers high-level analytical expertise and innovative methods development opportunities.

    The University also hosts the MRC North West Hub for Trials Methodology Research (NWHTMR), the Clinical Trials Research Centre, and the COMET Initiative, producing impressive outputs driven towards:

    • Improved measurement and use of appropriate outcomes in EHR research
    • Robust evaluation of approaches to e-recruitment and data-sharing
    • Development of web and app-based solutions for collecting patient reported outcome data.

    In addition, the Centre for Genomic Research comprises a dedicated team of fifteen bioinformaticians and software engineers working in collaboration with laboratory specialists to enable effective and efficient analysis of large and complex genomic datasets.