A newly designed method, known as INTEGRATE, has been successfully used to demonstrate how anonymised critical care data from a number of data sources can be brought together to give an overview of a patient's journey before and after admission into an Intensive Care Unit (ICU).

The beauty of the INTEGRATE methodology, which is published in the International Journal of Population Data Science (IJPDS), is that it can be easily reproduced by any researcher with appropriate access to critical care data and interest in research into critical care pathways, making it a valuable asset for critical care research studies.

Critical care is a specialist area for severely ill and high-risk patients who may require long periods of recovery after being discharged due to the nature of their condition. ICUs and High Dependency Units collect data on patients during their stay, but this data alone can't provide the insights needed on patients before they are admitted into critical care, or how they recover from their illness when they leave.

Critical care data typically focuses on the critical care admission, which is just one part of a patient's journey. The INTEGRATE methodology enhances the multiple data sources available by capturing any non-acute or emergency hospital admission or primary care interactions immediately before the critical care admission.

Patients who reach critical care will have a 'data trail' from their interactions with various healthcare services. These data are routinely recorded on each visit but often held in separate data sources. Combining and analysing the various data sources on a patient can provide a much broader picture of their journey through the healthcare system before, during and after critical care. Additional data on chronic illness and a patient's health status offers researchers opportunities to study healthcare trajectories and support research into survivorship.

Primarily using two data sources, namely the Intensive Care National Audit & Research Centre data (ICNARC) and the Critical Care Dataset (CCDS), INTEGRATE was developed within the Secure Anonymised Information Linkage (SAIL) Databank, with impressive results.

Rowena Griffiths, Research Officer and Data Scientist, Swansea University Medical School further highlighted the significance of INTEGRATE, saying "The development of this methodology meant we were able to capture healthcare trajectories and care pathways for critical care admissions. This, at an anonymised individual-level, population-scale, provides a key research asset which will be beneficial for critical care research."

Professor Tamas Szakmany MBE, Consultant in Intensive Care and Anaesthesia at Aneurin Bevan University Health Board and Senior Lecturer in Intensive Care at Cardiff University says “It’s important having a methodology, which is reproducible and helps researchers to create reliable cohorts of patients. These are crucial for clinicians to be able to study the effects of different diseases, such as sepsis, major trauma, cardiac arrest across the whole primary and secondary care system.”


You can read the full results of this study here

Rowena Griffiths, Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University

Griffiths, R., Herbert, L., Akbari, A., Bailey, R., Hollinghurst, J., Pugh, R., Szakmany, T., Torabi, F. and Lyons, R. A. (2022) “ linked electronic health records at population scale”., International Journal of Population Data Science, 7(1). doi: 10.23889/ijpds.v7i1.1724.