Course overview
The Digital Health Transformation & AI Applications in Healthcare Training Workshop is a practical professional development programme designed to equip healthcare professionals, health system leaders, digital health specialists and healthcare programme managers with the knowledge and skills to implement digital transformation initiatives and apply artificial intelligence (AI) to improve healthcare delivery, operational efficiency, patient outcomes and evidence-based decision-making.
The workshop explores the technologies, strategies and organisational changes shaping modern healthcare, including electronic health records, health information systems, telemedicine, AI-assisted clinical decision support, predictive analytics, digital patient engagement, healthcare automation, interoperability and responsible health data management.
The Digital Health Transformation & AI Applications in Healthcare Training Workshop equips healthcare professionals, hospital administrators, health informatics specialists and health system leaders with practical knowledge of digital transformation and artificial intelligence in modern healthcare. Participants explore electronic health records, telemedicine, clinical decision support, predictive analytics, generative AI, healthcare automation, interoperability, data governance and cybersecurity. Through practical exercises and implementation planning, participants learn to identify high-value technology applications, evaluate digital health solutions, improve operational workflows and develop sustainable strategies for responsible AI adoption and better healthcare outcomes.
Course objectives
- Explain the principles, technologies and strategic drivers of digital transformation in healthcare systems and healthcare organisations.
- Evaluate ethical, clinical, operational and regulatory risks associated with digital health and AI, applying appropriate safeguards for patient safety, privacy and responsible technology adoption.
- Develop digital health transformation roadmaps incorporating implementation planning, interoperability, data governance, cybersecurity, change management and performance measurement.
- Apply AI-assisted analytics and automation concepts to improve healthcare operations, resource utilisation, reporting and patient service delivery.
- Identify and evaluate practical AI applications in clinical decision support, medical imaging, predictive analytics, patient engagement and healthcare administration.
- Assess digital health infrastructure, electronic health records, telemedicine platforms and health information systems to identify opportunities for service improvement.
Who should attend
Hospital Administrators, Healthcare Executives, Medical Directors, Clinical Officers, Doctors, Nurses, Health Information Managers, Digital Health Specialists, Health Informatics Professionals, Healthcare IT Managers, Public Health Professionals, Health Programme Managers, Monitoring and Evaluation Specialists, Health Data Analysts, Health System Planners, Medical Researchers, Health Insurance Managers, Healthcare Consultants, Pharmaceutical and Medical Technology Professionals, NGO Health Programme Officers, Hospital Quality Improvement Teams, Health Policy Professionals.
Course content
- Hospital Administrators, Healthcare Executives, Medical Directors, Clinical Officers, Doctors, Nurses, Health Information Managers, Digital Health Specialists, Health Informatics Professionals, Healthcare IT Managers, Public Health Professionals, Health P
- Module 1: Foundations of Digital Health Transformation Digital health concepts and terminology, evolution of healthcare delivery models, digital transformation versus digitisation and digitalisation, drivers of digital healthcare adoption, patient-centred
- Module 2: Digital Health Architecture and Healthcare Information Systems Healthcare information system architecture, hospital information management systems, electronic health records and electronic medical records, laboratory information management syste
- Module 3: Electronic Health Records and Digital Clinical Workflows EHR implementation principles, patient identification and digital registration, electronic clinical documentation, medication management and prescribing workflows, laboratory and diagnosti
- Module 4: Artificial Intelligence Fundamentals for Healthcare Professionals AI concepts and terminology, machine learning and predictive analytics, natural language processing, generative AI and large language models, computer vision and medical imaging,
- Module 5: AI-Powered Clinical Decision Support Clinical decision support systems, evidence-based clinical recommendations, patient risk stratification, early warning and deterioration prediction, medication safety and interaction alerts, clinical guidelin
- Module 6: Predictive Analytics and AI for Population Health Healthcare data sources and analytical workflows, disease risk prediction, patient readmission prediction, chronic disease risk stratification, forecasting healthcare demand, patient segmentation
- Module 7: AI in Medical Imaging, Diagnostics and Laboratory Services Introduction to AI-assisted medical imaging, radiology workflow support, image-based abnormality detection, pathology and laboratory analytics, automated image classification concepts, d
- Module 9: Telemedicine, Remote Patient Monitoring and Connected Care Telemedicine models and digital consultations, virtual triage and appointment workflows, remote patient monitoring, wearable devices and connected medical technologies, chronic disease m
- Module 10: Healthcare Data Analytics, Business Intelligence and Dashboards Healthcare data lifecycle and quality, descriptive and diagnostic analytics, operational and clinical performance indicators, patient flow and waiting-time analysis, bed occupancy
- Module 11: Interoperability, Health Data Standards and Digital Health Ecosystems Interoperability principles and system integration, exchange of clinical and administrative information, standardised health data structures, terminology and coding systems,
- Module 12: Data Governance, Cybersecurity and Responsible AI Healthcare data governance frameworks, patient consent and lawful data processing, confidentiality and access control, cybersecurity risks in healthcare, phishing and ransomware awareness, ident
- Module 13: Digital Health Project Management and Change Management Digital health needs assessment, stakeholder analysis and engagement, business case development, requirements gathering, technology procurement and vendor evaluation, implementation planni
- Module 14: Evaluating Digital Health and AI Investments Digital health investment appraisal, total cost of ownership, licensing and infrastructure costs, implementation and maintenance expenditure, expected operational and clinical benefits, cost-effectiv
General notes
- Module 15: Capstone Project—Developing a Digital Health and AI Implementation Roadmap Healthcare organisation problem assessment, digital maturity and readiness review, identification and prioritisation of AI use cases, selection of suitable digital health solutions, stakeholder and workflow analysis, development of a business case, data governance and cybersecurity assessment, implementation timeline and resource planning, risk register and mitigation measures, selection of monitoring indicators, preparation of a digital transformation roadmap, presentation of the proposed solution.
- Recommended Software and Tools: Electronic Health Records (EHR) platforms, Hospital Information Systems (HIS), DHIS2, Microsoft Excel, Microsoft Power BI, Python, SQL, Microsoft PowerPoint, telemedicine platforms, digital patient engagement systems, healthcare workflow automation tools, enterprise AI assistants, healthcare analytics platforms, approved clinical decision support systems, health information exchange platforms, project management software, cybersecurity and data governance tools.
- Tool selection will depend on the institution's available systems, training environment and participants' roles. Clinical AI applications should be demonstrated using approved tools and suitable sample or de-identified data; public AI tools should not receive identifiable patient information without appropriate authorisation and safeguards.
- Training Methodology: The workshop combines instructor-led presentations, practical software demonstrations, guided exercises, healthcare case studies, group discussions, digital health implementation scenarios and a capstone project. Participants will examine how healthcare organisations can move from isolated digital tools towards integrated, data-driven systems. Practical activities will focus on identifying real operational problems, selecting appropriate technologies, evaluating AI use cases, improving workflows, assessing risks and developing implementation plans. The workshop is designed for both technical and non-technical professionals. It emphasises practical application and strategic decision-making rather than requiring participants to become software developers or AI engineers.
- Digital health training workshop, AI in healthcare training, healthcare digital transformation, artificial intelligence in healthcare, health informatics training, digital health strategy, healthcare automation, AI clinical decision support, healthcare predictive analytics, electronic health records training, telemedicine training, healthcare data analytics, responsible AI in healthcare.
Frequently asked questions
1. 1. What is digital health transformation?
Digital health transformation involves redesigning healthcare services, workflows and management processes through digital technologies. It can include electronic health records, telemedicine, connected medical devices, integrated information systems, analytics and AI-supported services.
2. 2. How is artificial intelligence used in healthcare?
AI can support clinical decision-making, medical imaging, patient risk prediction, disease surveillance, healthcare analytics, administrative automation, patient communication and resource planning. Its suitability depends on the use case, available evidence, data quality and required safeguards.
3. 3. Do participants need programming experience?
No. The workshop is designed primarily for healthcare professionals and decision-makers. It introduces AI and analytics concepts using practical demonstrations and accessible tools. Participants interested in developing custom AI models may require additional technical training.
4. 4. Will the workshop cover electronic health records and hospital information systems?
Yes. Participants will examine EHR and HIS functionality, digital clinical workflows, system integration, data governance and opportunities for improving patient record management and healthcare operations.
5. 5. Will participants learn how to use generative AI in healthcare?
Yes. The workshop explores appropriate uses of enterprise AI assistants for administrative documentation, summarisation, patient education content and analytical support. It also covers verification, confidentiality, hallucinations and the need for professional oversight.
6. 7. Will participants develop a practical digital health strategy?
Yes. The capstone project involves developing a digital health transformation roadmap with prioritised use cases, implementation phases, resource considerations, risks, governance measures and performance indicators.
7. What are the expected training outputs?
Expected outputs include a digital maturity assessment, healthcare workflow analysis, AI use-case assessment, sample analytics or dashboard design, data governance checklist, digital health business case and an implementation roadmap.
Schedule & fees
| Dates | Location | Duration | Fee (USD) |
|---|
| 25/01/2027 - 05/02/2027 |
Nairobi,kenya or Virtual |
10 Days |
$2,000 |
| 01/02/2027 - 12/02/2027 |
Mombasa,Kenya |
10 Days |
$2,000 |
| 05/04/2027 - 16/04/2027 |
Naivasha,Kenya |
10 Days |
$2,000 |