In April 2023, the DATAMIND Data Science workshop and conference for researchers to share talks and presentations. Emily Ball, Post-doctoral Research Fellow funded by the Wellcome Trust, kindly shares their experiences attending the Data Science workshop and meeting.
Emily, could you introduce yourself to our readers and tell us why data is so important to your work?
I am a Research Fellow at the University of Edinburgh. My research aims to identify environmental risk factors for depression across the life course. I use existing research data that has been linked to up-to-date electronic health records. This allows me to follow-up thousands of individuals to identify whether they have been diagnosed with depression.
There are huge benefits to using clinical data for research, for example, it can enable researchers to address their questions at a population-level. However, as I discuss below, the MQ DATAMIND workshop and meeting highlighted important considerations when working with clinical data for research purposes.
What were the Top Tips you took as a researcher from the sessions?
The workshop began with insightful talks from Matthew Broadbent and Amelia Jewell, who took us behind the scenes of a Clinical Data Linkage Service. This was followed by a practical session led by Risha Govind, where we worked in groups to design a study that uses clinical data to answer a real-world research question.
Here, I have summarised my top tips for using clinical data for research purposes taken from these sessions:
- Researchers need to work with clinicians to understand the primary purpose of clinical data.
- If we want to ensure that we acquire good quality data from electronic health records, we need to focus on how we pull data out (rather than how it is put in).
- When working with clinical data, be flexible and concentrate on the definitions you are using (e.g., clearly defining outcome of interest). “Matching the right definition to the right question has a massive impact on accuracy”.
- Effective communication with patients and the public is required to strike a balance between using clinical data for research, whilst protecting the ethical and legal rights of patients.
What else did you enjoy listening to during the day?
In the afternoon, I enjoyed listening to a talk from Katrina Davis who discussed their experience of using linked data for research. And of particular interest to me, discussed the strengths and limitations of using prescribed drugs data in research. As I work with prescription data in Scotland, I found it interesting to learn about the similarities and differences of working with clinical data in England.
Did you learn anything useful about using existing datasets to aid your research?
It can be time consuming trying to identify existing research datasets that can be used to address your research question. An excellent talk by Louise Arseneault and Elena Triantafillopoulou outlined how researchers can use the Catalogue of Mental Health Measures (https://www.cataloguementalhealth.ac.uk/) to discover existing British cohort and longitudinal studies that have used mental health and wellbeing measures. This looks like a very helpful resource!
Were there any stand out moments of the day for you?
A particular highlight of the Data Science Meeting was hearing from the DATAMIND Research Advisory Group. In this session, the panel was formed of four Research Advisory Group members who have experience with mental health issues, as a patient or as a carer. This session emphasised the importance of incorporating patient and public involvement (PPI) throughout the research process, from designing the research question to disseminating findings. The panel also outlined how researchers can effectively facilitate PPI sessions, for example, by providing patients/public with a glossary of research terms that may be used during the session.
You’re an Early Career Researcher – the future of mental health research! What specific support was available to you in this role?
In both the workshop and meeting there was a special focus on supporting Early Career Researchers. Sixteen Early Career Researchers working in industry or academia presented their work as a poster or short presentation. There were a wide range of research topics ranging from patient and public co-development studies to studies using machine learning methods. I also had the opportunity to discuss my own work with other researchers and had fantastic discussions about the strengths and limitations of using prescribed drugs to identify cases of depression.
How would you sum up the day to someone who wasn’t there?
The MQ DATAMIND workshop and conference focused on learning from each other and exploring opportunities for growth and development within the field of data science. I look forward to the next meeting!
And MQ looks forward to it too! Our thanks to Emily for sharing their experience with us. You can follow Emily on Twitter @DrEmilyBall. And you can learn more about future events like this by visiting our events page and sign up to our research round up newsletter.
