Exploring the Medical School Councils' (MSC) digital agenda.

Core competency

Learning outcomes

1) Data governance and data management

• Describe what constitutes health data.
• Explain the importance of data governance policies in healthcare.
• Demonstrate an understanding of data required for specific functions of electronic health records (e.g., laboratory results, clinical decision support), its location, accessibility, and record manipulation tracking.

2) Health informatics

• Explain the concept of health informatics.
• Describe the role of health data and informatics in supporting health through enhanced data collection, management, sharing, and application in the context of both infectious and chronic diseases.
• Interpret and explain findings generated from health data to patients and the public.

3) Professionalism, ethical, legal, and regulatory considerations in digital health

• Apply best practices for managing digital patient data.
• Describe protected health information.
• Apply prevailing privacy and security rules when handling protected health information.
• Demonstrate compliance with ethical conduct and codes of practice when processing digital patient data.
• Explain how professional, clinical, and research ethics are applied and practiced within digital health space.

4) Digital identity, safety, and security

• Describe common behaviours of doctors which may compromise data security.
• Demonstrate awareness of prevailing rules or regulations on sharing protected health information via cross-platform instant messaging services (e.g., WhatsApp, Telegram).
• Explain the concepts of medical professional, digital identity and digital intelligence.

5) Artificial intelligence in healthcare

• Describe basic principles of artificial intelligence (AI), natural language processing (NLP), speech recognition, machine learning (ML), automated image interpretation and predictive analytics.
• Critique limitations of and barriers to using AI in healthcare.
• Explain the role of big data in healthcare data science and delivery.
• Explain the importance of rigorous real-world clinical validation of AI-based technologies before implementation in patient care.
• Maintain vigilance and validate advice given by machines to avoid automation bias.
• Describe application of automated image interpretation and pattern recognition in radiology, radiotherapy, and retinal photography.
• Discuss how integrating big data from different sources (e.g., data in electronic health records, hospital records, medical records of patients, results of medical examinations; data from devices that are a part of internet of medical things and wearables, data from biomedical research) can yield useful insights into healthcare provision, utilization, optimization, and improvement.

6) Clinical Academic Research

• Describe how advanced data analysis can help identify patterns, trends, risk factors, and correlations within health data, which can lead to new research questions and hypotheses.
• Discuss how data from everyday clinical practice can be used to assess treatment effectiveness, monitor drug safety, and drive advancements in healthcare.
• Discuss how data science can be used to develop models that predict outcomes, such as disease progression, treatment effectiveness, or patient risk.

7) Digital diagnostics algorithms

• Describe how digital diagnostic algorithms can enhance, guide, and record aspects of clinical examination.
• Recognize the limitations of fully automated AI-led diagnostic platforms and traditional methods.

8) Precision medicine

• Describe the concept of precision medicine and its implications in healthcare.
• Discuss how data collected through digital health technologies (e.g., wearable sensors, mobile health apps) can support precision medicine.

9) Digital health literacy

• Describe digital health literacy and its impact on health outcomes and access to care.
• Describe how higher health literacy leads to reduced healthcare costs, hospitalisation rates, readmissions and emergency department visits.

10) Health inequalities

• Explain the need for datasets to be diverse and representative of patient populations, including those from marginalised groups.
• Identify and mitigate potential biases in data and algorithms that can perpetuate existing health disparities.
• Understand the digital divide and its impact on access to healthcare services and information.
• Recognise the heightened risk of data breaches and misuse for vulnerable populations.

11) Personal health records

• Explain both the positive and negative impacts of personal health records on patientcentred healthcare (e.g., patient-provider communication, education and lifestyle changes, health self-management)
• Explain what constitute appropriate use of and access to patient health records.

12) Foundation and principles of health information systems

• Discuss both positive and negative impacts of electronic health records on patient care.