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.
CIF Category: Working with Data & Analytical Methods
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.
Data streams and sources
Understands the variety of data streams and sources that contribute to health decision making including those not necessarily primarily collected for health (e.g., mobile, sensors, phenome).
Health data
Discusses the opportunities and challenges with using real-world health data for analysis and to drive decision-making.
Health informatics standard
Applies current best health informatics standards for the recording of health data (e.g., classifications, vocabularies) to increase data quality and utilisation for improving healthcare and clinical practice and research
Data linkage
Understands the importance of data linkage, record linkage methods, and the relevant strengths and limitations, to be able to conduct or review linked data analysis.
Inter-relationships and dependencies
Demonstrates an understanding of the data inter-relationships and dependencies among the various health information systems (e.g., decision support systems, electronic health records, order entry, registries, etc.).
Data storage
Has awareness of the different approaches used to store health data and the pros and cons of using these approaches, and how these effect data accessibility and analyses.
Regulatory guidelines
Explains the ethical, legal and regulatory guidelines to determine the appropriate access, use, disclosure and protection of data to protect patient information and ensure confidentiality and applies them when processing patient data at all times.
Data processes, guidelines, and governance
Demonstrates an understanding of processes, guidelines, and governance structures needed to achieve trustworthy use of methodologies such as Artificial Intelligence and is able to assess these with others to address health care problems.
Privacy enhancing technology
Has some awareness of privacy enhancing technologies (e.g., K-anonymity, homomorphic encryption), and how and what they might be used for.
Visualisation techniques
Demonstrates an understanding of a range of visualisations used to present data analyses and information so as to be able guide others in their usage.
Data quality analysis
Contributes to quality analysis by organizing and transforming data into reliable and meaningful information to support decision making.
Information presentation
Presents information in a way that is effective for users decision making, and that takes into account the variability in the user capability to assess methods and draw appropriate conclusions.