Every day we collect, record and analyse data, and using this data, we can assure quality healthcare within our specialties. Data is the core of physics and engineering work and determines the recommendations we make and indeed the outcomes of patients within our care. This year the Summer School is exploring the topic of “Data-driven Healthcare” by digging deep into examining the data we collect and how we use it.
Catering to all specialities, this school will introduce attendees to the concepts of data analysis, databases and how the information extracted can be used to shape outcomes.
The Summer School will explore how access to larger datasets and more powerful analysis techniques have changed the way that we do work. The focus will be on risk analysis; however, the knowledge gained is applicable across all facets of our work.
Attendees will be able to discuss relevant applications of these concepts as well as gain ideas for improvement initiatives they can implement as part of their TEAP requirements.
In keeping with social distancing guidelines and COVID-19 safety checklists, the Summer School will be held completely virtually via online webinars (details to be announced). There is no registration fee for TEAP registrars.
Draft Workshop Program
The complete program including presenters and associated TEAP modules will be made available shortly.
- Introduction to the importance of data analysis and data visualization
- Data Analysis and Machine Learning - introduction to the new techniques used to analyse data sets
- Being data-driven and how data is driving our daily lives
- Data filtering and flattening and the pitfalls of sampling data
- Data visualization and the pitfalls of presenting data
- Data science in medical physics. How has the power of data analysis changed the way we work?
- Looking closer at epidemiology with data science
- Risk Analysis. An insight into radiation risk assessment and calculation
This day is dedicated to exploring quality management and risk management from a data-driven perspective.
- Extracting data from clinical management systems. Including structuring a database and working with SQL
- Applications of data science and/or machine learning in delivery of healthcare