Ethical Considerations in Digital Health Research Training Course
Ethical Considerations in Digital Health Research Training Course is a comprehensive and industry-relevant program designed to equip researchers, health professionals, policymakers, and digital innovators with practical and theoretical knowledge in digital health ethics.
Course Overview
Ethical Considerations in Digital Health Research Training Course
Introduction
Ethical Considerations in Digital Health Research Training Course is a comprehensive and industry-relevant program designed to equip researchers, health professionals, policymakers, and digital innovators with practical and theoretical knowledge in digital health ethics. As digital health tools such as AI, wearables, telehealth, and mobile health apps become more prevalent, navigating the ethical landscape of data use, consent, privacy, equity, and governance is critical. This course explores how ethical principles intersect with cutting-edge health technologies, helping participants make informed and responsible decisions in research design and implementation.
From AI-driven diagnostics to cross-border data sharing, digital health research presents new ethical challenges that demand dynamic solutions. The course emphasizes ethical frameworks, data stewardship, human subject protections, algorithmic fairness, and regulatory compliance. Through case studies, expert insights, and interactive sessions, participants will build competencies to evaluate ethical dilemmas, engage diverse stakeholders, and implement ethical digital health solutions that align with global standards.
Course Objectives
- Understand key ethical principles in digital health research.
- Identify and address data privacy concerns in mobile health technologies.
- Evaluate informed consent strategies in digital platforms.
- Apply ethical guidelines to AI and machine learning in health research.
- Analyze regulatory frameworks such as GDPR and HIPAA.
- Explore bias and fairness in health algorithms.
- Promote ethical inclusivity in research involving vulnerable populations.
- Implement cybersecurity ethics in health data storage and transfer.
- Assess the ethical implications of big data in healthcare.
- Critically examine commercialization and IP ethics in digital health.
- Evaluate cross-border data sharing ethics and governance.
- Interpret case studies of ethical failures and successes in digital health.
- Develop a digital health ethics protocol for research projects.
Target Audiences
- Digital health researchers
- Clinical trial investigators
- Public health professionals
- Ethics board members and IRBs
- Health tech entrepreneurs
- Data scientists in healthcare
- Medical and nursing students
- Health policymakers and regulators
Course Duration: 10 days
Course Modules
Module 1: Foundations of Digital Health Ethics
- Historical evolution of health research ethics
- Bioethical principles (autonomy, justice, beneficence)
- Role of ethics in digital transformation
- Digital health vs. traditional health research ethics
- Current debates and global standards
- Case Study: The Belmont Report in the digital era
Module 2: Data Privacy and Confidentiality
- Types of data in digital health
- Identifiable vs. anonymized data
- Encryption and access controls
- Legal frameworks (HIPAA, GDPR)
- Ethical data breach responses
- Case Study: Health data exposure in contact tracing apps
Module 3: Informed Consent in Digital Contexts
- E-consent platforms and usability
- Challenges in remote consent
- Consent fatigue and user comprehension
- Continuous vs. one-time consent
- Multimedia consent tools
- Case Study: Consent design in wearable health device trials
Module 4: AI and Machine Learning Ethics in Health
- Algorithmic transparency
- Training data ethics
- Explainable AI in health diagnostics
- Risk of bias in ML systems
- Ethical review of AI models
- Case Study: Racial bias in AI health diagnostics
Module 5: Cybersecurity and Ethical Protection of Health Data
- Ethical hacking and penetration testing
- Threat modeling in research systems
- Cyber risk mitigation
- Data minimization strategies
- Third-party vendor ethics
- Case Study: Ransomware attack on hospital research systems
Module 6: Research Ethics in Telemedicine and mHealth
- Mobile app-based clinical research
- Ethical implications of teleconsultations
- Remote monitoring and surveillance ethics
- Culturally appropriate digital tools
- Access and equity in tele-research
- Case Study: Ethics of remote postpartum care via apps
Module 7: Vulnerable Populations and Equity in Digital Health
- Definition and examples of vulnerable groups
- Ethical engagement strategies
- Digital divides and access gaps
- Language, literacy, and inclusivity
- Intersectionality and data equity
- Case Study: Indigenous populations and digital health trials
Module 8: Ethical Implications of Big Data in Health
- Nature of big data in healthcare
- Predictive analytics ethics
- Consent in secondary data use
- Aggregated vs. individual risk
- Risk of surveillance and profiling
- Case Study: Big data misuse in insurance premium predictions
Module 9: Cross-Border Data Ethics and Global Governance
- Jurisdictional and legal complexities
- Data sovereignty and nationalism
- International collaboration protocols
- Conflict of ethical standards
- Repatriation of health data
- Case Study: COVID-19 global data sharing dilemmas
Module 10: Ethical Review and Institutional Oversight
- Role of IRBs and ethics committees
- Digital research review protocols
- Risk-benefit assessment for digital studies
- Research misconduct in digital trials
- Stakeholder engagement in review
- Case Study: Ethics board response to digital health chatbot trial
Module 11: Commercialization, Patents, and IP Ethics
- Tech transfer and innovation ethics
- Conflicts of interest in commercialization
- Licensing digital health tools
- Startups and research partnerships
- Ethical monetization of patient data
- Case Study: Monetizing genetic testing kits and privacy concerns
Module 12: Cultural Competency in Digital Health Research
- Understanding local beliefs and norms
- Community-based participatory research
- Multilingual ethical communication
- Adapting digital tools to cultures
- Inclusive UX research designs
- Case Study: Culturally adapted HIV prevention app in sub-Saharan Africa
Module 13: Algorithmic Bias and Discrimination
- Sources of bias in datasets
- Impact on marginalized communities
- Mitigating discriminatory outputs
- Auditing AI models for fairness
- Role of human oversight
- Case Study: Gender bias in AI mental health apps
Module 14: Digital Health and Environmental Ethics
- Ethical sustainability of tech tools
- E-waste and environmental impact
- Green cloud computing in research
- Responsible device disposal
- Climate justice in digital health
- Case Study: Evaluating carbon footprint in digital clinical trials
Module 15: Future Trends and Emerging Ethical Challenges
- Metaverse and immersive health research
- Blockchain and health data ethics
- Wearables and continuous consent
- Brain-computer interfaces (BCI) ethics
- Ethical innovation frameworks
- Case Study: Virtual reality therapy in PTSD trials
Training Methodology
- Interactive instructor-led lectures
- Real-world case study analysis
- Hands-on ethics protocol design workshops
- Peer-to-peer debates and roundtables
- Group presentations on ethical dilemmas
- Continuous assessment through quizzes and project work
- Bottom of Form
Register as a group from 3 participants for a Discount
Send us an email: [email protected] or call +254724527104
Certification
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.