My name is Matthew Iveson, and I’m a Project Manager on the DATAMIND project and a senior researcher at The University of Edinburgh. My research focuses on using electronic health records to better understand depression and how people respond to treatment.
For nearly 8 years, I’ve been working with big mental health datasets, like hospital records and prescription records. During this time, I’ve witnessed many changes to the mental health data landscape, both in the types of data available to researchers and the processes that govern their use. I’ve been fortunate to be involved in several initiatives, with the most recent being DATAMIND’s work to harmonise mental health data and to promote its discoverability for research.
Harmonising Data
DATAMIND has been active in standardising our definitions of mental health, psychiatric conditions, and treatment. For example, if I want to identify cases of depression in hospital admission records I can now simply search using a tool such as the Health Data Research Phenotype Library (https://phenotypes.healthdatagateway.org/) to find a pre-defined list of clinical codes (International Classification of Diseases codes) and see previous uses in the literature. This standardisation ensures that I am talking about the same ‘depression’ as other researchers, allowing comparisons to be made between studies. Making these standardised definitions freely available for research has made it easier to validate research in other settings and in other countries.
Promoting Data Discoverability
DATAMIND has also been instrumental in the creation of a Catalogue of Mental Health Measures (https://www.cataloguementalhealth.ac.uk/), which lists the ways of measuring mental health used across British cohort and longitudinal studies. For me as a researcher, this makes it easier to identify all the studies in which depression is measured and to combine them together to produce more powerful research. It also helps me understand the context of these different studies, and how I might need to account for their differences (such as the age at which they measure depression).
These are just a few of DATAMIND’s initiatives to support better mental health research. Other efforts include developing new data sources, improving data access procedures, and creating algorithms to make large and complex datasets more useable. These efforts to improve the mental health data landscape lead to better tools for researchers like me to use, allowing for more detailed, more representative research. By working together on common goals, researchers can produce better insights into the causes and mechanisms underlying mental health and treatment that ultimately improve the lives of those at-risk of or living with mental ill-health. I look forward to continuing my work with DATAMIND, making research more accessible for others and encouraging collaboration between mental health researchers.
For more information on DATAMIND and its activities in harmonised and discoverable data, please visit datamind.org.uk or email datamind@swansea.ac.uk
