Research, Data Management & Analytics · Physical, Online
Healthcare Data Analytics, Business Intelligence & AI Training Workshop
5 Days4 sessions available Certificate on completion
The Healthcare Data Analytics, Business Intelligence & AI Training Workshop equips healthcare professionals, health information managers, data analysts, programme managers and public health practitioners with practical skills to transform healthcare data into actionable insights using data analytics, business intelligence and artificial intelligence.
The course covers healthcare data management, data quality assurance, statistical analysis, performance measurement, interactive dashboard development, predictive analytics and AI-assisted reporting. Participants learn to analyse patient records, hospital operational data, clinical indicators, disease surveillance data, service utilisation, financial performance and healthcare programme outcomes.
: Healthcare Data Analytics Training, Healthcare Business Intelligence Training, AI in Healthcare Analytics, Healthcare Data Management Course, Power BI Healthcare Dashboard Training, Healthcare SQL Training, Healthcare Predictive Analytics, Hospital Data Analysis Training, Health Information Analytics, AI for Healthcare Professionals, Healthcare KPI Reporting, Digital Health Analytics Workshop, Healthcare Performance Monitoring, Healthcare Data Visualisation.
Course objectives
Manage, clean, validate and prepare healthcare datasets for accurate analysis and reporting.
Implement responsible healthcare data analytics practices that protect patient confidentiality, promote data quality and support evidence-based decisions.
Apply AI and predictive analytics to identify trends, forecast service demand, detect unusual patterns and support operational decision-making.
Develop interactive healthcare dashboards and performance reports using business intelligence tools.
Apply statistical and analytical techniques to healthcare utilisation, service delivery, operational performance and public health indicators.
Who should attend
Healthcare data analysts, health information officers, hospital administrators, health records and information management professionals, clinical audit officers, public health professionals, epidemiologists, health programme managers, monitoring and evaluation officers, health informatics specialists, healthcare quality improvement officers, medical researchers, health insurance analysts, healthcare finance managers, business intelligence analysts, digital health professionals, health policy analysts, healthcare consultants and professionals responsible for healthcare data analysis and reporting.
Course content
Module 1: Introduction to Healthcare Data Analytics and Business Intelligence Healthcare data analytics concepts, descriptive and diagnostic analytics, predictive and prescriptive analytics, business intelligence in healthcare, clinical versus operational
Module 2: Healthcare Data Sources and Information Systems Electronic Health Records (EHRs) and Electronic Medical Records (EMRs), Hospital Information Systems (HIS), Laboratory Information Systems (LIS), pharmacy information systems, health insurance and
Module 3: Healthcare Data Management, Quality and Governance Healthcare data collection methods, data dictionaries and metadata, data completeness and consistency, duplicate patient records, missing values and invalid entries, standardisation of clinical
Module 4: Healthcare Data Analysis Using Microsoft Excel Importing healthcare datasets, sorting and filtering records, data cleaning and transformation, conditional formulas, lookup functions, date and time calculations, PivotTables and PivotCharts, patie
Module 5: SQL for Healthcare Data Management and Analysis Relational database concepts, healthcare data tables and relationships, patient and encounter data structures, SELECT queries, filtering and sorting, aggregate functions, GROUP BY and HAVING, joins
Module 6: Healthcare Statistics and Performance Measurement Descriptive statistics, measures of central tendency and dispersion, frequency distributions, proportions and rates, healthcare service utilisation, patient admission and discharge analysis, read
Module 7: Healthcare Dashboards and Business Intelligence Using Power BI Connecting Excel and SQL data sources, importing and transforming healthcare data, Power Query, data modelling, relationships between tables, calculated measures using DAX, healthcar
Module 8: Clinical, Hospital Operations and Resource Analytics Patient flow and waiting-time analysis, outpatient and inpatient workload, bed occupancy and turnover, theatre utilisation, laboratory turnaround time, pharmacy stock availability, staffing an
Module 9: Public Health and Disease Surveillance Analytics Disease incidence and prevalence, surveillance data quality, time-series trends, outbreak signal monitoring, geographical variation in disease burden, age and sex disaggregation, vaccination cover
Module 10: Artificial Intelligence for Healthcare Data Analytics Generative AI concepts and healthcare applications, using ChatGPT and Microsoft Copilot for analytical support, prompt engineering for healthcare datasets, AI-assisted data documentation, ge
Module 11: Predictive Analytics and Machine Learning in Healthcare Introduction to machine learning, defining healthcare prediction problems, selecting appropriate variables, preparing structured datasets, training and testing data, regression and classif
Module 12: AI-Powered Healthcare Reporting and Decision Support Automating routine performance summaries, generating monthly healthcare reports, AI-assisted interpretation of KPI trends, comparing facilities and departments, summarising operational bottle
Module 13: Healthcare Financial and Insurance Analytics Healthcare expenditure analysis, departmental cost allocation, revenue and billing analysis, insurance claims processing metrics, claims rejection and denial analysis, reimbursement trends, budget ve
Module 14: Healthcare Data Privacy, Security and Responsible AI Patient confidentiality, personally identifiable and sensitive health information, data minimisation, de-identification and pseudonymisation, access controls and least privilege, secure data
Module 15: Integrated Healthcare Analytics and AI Capstone Project Defining a healthcare analytics problem, selecting an appropriate dataset, preparing and validating data, calculating healthcare KPIs, analysing service utilisation and operational perform
General notes
The Healthcare Data Analytics, Business Intelligence & AI Training Workshop equips healthcare professionals with practical skills in healthcare data management, statistical analysis, business intelligence and artificial intelligence. Participants learn to clean healthcare datasets, analyse patient and hospital operational data, calculate performance indicators, develop interactive Power BI dashboards and generate evidence-based reports. The course also covers AI-assisted analytics, predictive modelling, disease surveillance, healthcare resource utilisation, financial analytics and responsible data governance. Designed for healthcare data analysts, health information managers, hospital administrators, public health professionals and digital health specialists, the workshop supports improved service monitoring, operational efficiency and data-driven healthcare management.
: Healthcare Data Analytics Training, Healthcare Business Intelligence Training, AI in Healthcare Analytics, Healthcare Data Management Course, Power BI Healthcare Dashboard Training, Healthcare SQL Training, Healthcare Predictive Analytics, Hospital Data Analysis Training, Health Information Analytics, AI for Healthcare Professionals, Healthcare KPI Reporting, Digital Health Analytics Workshop, Healthcare Performance Monitoring, Healthcare Data Visualisation.
Frequently asked questions
It is suitable for healthcare data analysts, health information managers, hospital administrators, public health professionals, health informatics specialists, clinical audit teams, researchers and professionals responsible for healthcare performance reporting.
Yes. Participants learn to develop interactive Power BI dashboards for patient attendance, admissions, bed occupancy, waiting times, disease trends, departmental KPIs and other operational measures.
AI can assist with data preparation, statistical interpretation, trend summaries, forecasting, anomaly detection and report drafting. AI outputs must be checked against source data, and clinical decisions require appropriate professional oversight.