Helping healthcare organisations leverage data science to improve decision-making, operational efficiency, and patient outcomes.
Build robust machine learning models tailored to healthcare contexts—from risk stratification and patient outcome prediction to resource forecasting and operational planning. I focus on creating interpretable, clinically-relevant models that integrate seamlessly into existing workflows.
Example outcome: A predictive model that identifies surgical cases likely to overrun, reducing theatre delays by 15% and improving patient throughput.
Extract valuable insights from unstructured clinical narratives, medical records, and free-text documentation. From sentiment analysis and entity extraction to automated summarisation and classification, I help organisations unlock the value hidden in their text data.
Example outcome: An NLP pipeline that automatically categorises patient feedback and extracts key themes, reducing manual review time by 70% while improving response quality.
Design and build intuitive, interactive dashboards using Power BI, Streamlit, or custom web applications. I translate complex datasets into clear visual stories that empower stakeholders to make informed decisions quickly and confidently.
Example outcome: A real-time operational dashboard for theatre managers, visualising case durations, resource utilisation, and bottlenecks—enabling proactive intervention and better scheduling.
Understand your challenge, stakeholders, and data landscape. I work closely with clinical and operational teams to define clear objectives and success metrics.
Explore and clean your data, identify patterns, and prototype solutions. I use rapid experimentation to validate approaches before committing to full development.
Build production-ready models, dashboards, or pipelines. I prioritise interpretability, documentation, and seamless integration with your existing systems.
Provide training, documentation, and ongoing support to ensure your team can maintain and extend the solution independently.