Lauren Lawson and Radin Karimi, NIHR Newcastle PSRC Safet Integrated Health and Social Care Environments & Safer management of polypharmacy in multiple long-term conditions Theme, PhD students.
The NIHR Academy Member’s Conference, held in Leeds on the 19th and 20th of November 2024, brought together researchers at all career stages to network and learn more about work supported by the NIHR. This year’s theme, Research Impact: Beginning with the End, emphasised planning for meaningful communication of research findings to diverse audiences.
A highlight of the event was the skills workshop led by Dr Oli Williams and Dr. Joe Langley on creative methods for impact. Here, we saw examples of creative projects, including evidence-based role-play scenarios given to participants to reconstruct serious incident investigations, encouraging empathy and insight. Another innovative project used a 3D model of a leg to visualise the effects of ‘pyjama paralysis’ on muscle mass in hospitals, and uneven-soled shoes illustrated the impact on balance. These creative approaches showcased how researchers can engage audiences and make their findings resonate, inspiring new ways to share and develop research.
NIHR Newcastle PSRC PhD students Faiza Yaha, Lauren Lawson and Radin Karimi
The second day of the NIHR Academy Member’s Conference kicked off with an exciting and creative networking session using LEGO® SERIOUS PLAY. Attendees worked in groups to answer impact-related questions by building LEGO models, sparking new ideas and fresh perspectives on research challenges. This interactive activity highlighted how stepping outside traditional methods can inspire innovative solutions and stronger collaboration.
A standout moment was the keynote speech by Dr. Raphael Olaiya, known for his role on CBBC’s Operation Ouch! and as a data scientist in the NHS. Dr. Olaiya shared how he combines his expertise in science with engaging communication to reach broad audiences, encouraging researchers to think about how they share their work in meaningful ways.
The conference left attendees motivated to explore creative approaches to making their research more inclusive and impactful. It was a strong reminder of the power of collaboration and innovation in driving research that makes a real-world difference.
Written by Ayesha Sahar, Research Associate, Artificial intelligence and data science in multiple long-term conditions theme.
In recent months, I have focused on developing models to predict patterns and uncover relationships in large datasets, particularly in healthcare. For example, understanding why certain health conditions often co-occur or how long-term medication prescriptions affect patients over time can be challenging. Data modeling provides a solution by analyzing historical data to identify and predict these connections.
One method I have explored is topic modeling, which organizes complex datasets into groups or “topics.” In healthcare, this might mean identifying clusters of related conditions—such as diabetes and hypertension frequently occurring together—or grouping treatments and prescriptions commonly used for managing these conditions. This helps researchers and clinicians make informed decisions, whether tailoring treatment plans for individual patients or designing studies to explore new healthcare solutions.
To enhance these insights, I have also employed Principal Component Analysis (PCA). PCA reduces the complexity of large datasets by identifying clusters of related variables (or components) that explain the most variance in the data. This approach simplifies the relationships between numerous conditions and prescriptions, making the data more manageable and meaningful.
However, raw PCA results can be difficult to interpret. That’s where varimax rotation plays a critical role. This mathematical technique adjusts PCA components to make them more distinct and interpretable. Instead of overlapping clusters, varimax rotation sharpens the focus, clearly linking specific conditions to corresponding treatments.
By combining these methods, my work simplifies the complexity of healthcare data, transforming it into actionable insights. These models not only reveal patterns but also support better clinical decisions, enabling healthcare providers to design effective, patient-centered care pathways. As we refine these approaches, the potential to improve both research and patient outcomes grows exponentially.