Data analysis

Is able to use basic descriptive statistics and explain the concepts of probability, predictive modelling and machine learning techniques to discover patterns and knowledge in recorded data and know when to use them to solve health and social care, clinical practice and research problems.

Clinical data

Understands what clinical questions can be addressed with different data sources and working with data custodians and others can understand what data is required and the data analytical methods to be used to address the problem and derive insights.

Data quality

Is able to demonstrate how data quality effects analysis, and resulting clinical and healthcare insights, and how important it is to improve to derive maximum potential from its utilisation.

Data technologies

Understands how technologies (e.g., R, Python, Jupyter notebooks) facilitate the analysis, display of results, and reproducibility of analyses to be able to re-run protocols to verify results and modify for other purposes.

Data techniques

Is aware of the latest techniques (e.g., AI) and their application to healthcare (e.g., imaging and genomics interpretation, clinical diagnostic evaluations, prediction of readmission risk, extracting semantic information from text) and the challenges in deployment and usage of these in health and clinical settings (e.g., population data and algorithmic bias, explainability of results, robust regulation and quality control, metrics vs clinical applicability, ethics and unintended negative consequences).

Data attributes

Demonstrates an understanding of the key attributes of data and information including quality, integrity, accuracy, timeliness and appropriateness and can discuss their limitations within the context of intended use.