Introduction

In today’s data-driven healthcare environment, Statistical Quality Control (SQC) has become a critical pillar for achieving patient safety, healthcare excellence, operational efficiency, clinical governance, healthcare compliance, and evidence-based decision-making. Hospitals, laboratories, pharmaceutical institutions, public health organizations, and healthcare regulators are increasingly adopting quality improvement systems, healthcare analytics, Six Sigma methodologies, Lean healthcare strategies, risk management frameworks, and performance monitoring tools to minimize medical errors and improve patient outcomes. Statistical Quality Control in Health Training Course equips healthcare professionals with practical knowledge and modern statistical techniques to strengthen healthcare quality assurance systems and support continuous improvement initiatives across healthcare facilities.

The course focuses on modern trends including healthcare data analytics, AI-driven quality monitoring, healthcare accreditation standards, patient-centered care, KPI dashboards, predictive analytics, infection control monitoring, healthcare performance management, ISO standards in healthcare, and digital health transformation. Participants will gain practical skills in applying statistical tools for quality control, analyzing healthcare performance indicators, conducting root cause analysis, monitoring healthcare processes, and implementing corrective actions using internationally recognized quality frameworks. Real-world healthcare case studies and hands-on exercises ensure participants can immediately apply learned techniques in clinical and healthcare management settings.

Programme Curriculum

Statistical Quality Control in Health Training Course

Introduction

Course Duration

5 days

Course Objectives

By the end of this training course, participants will be able to:

Understand the principles of Statistical Quality Control (SQC) in healthcare systems.
Apply healthcare data analytics techniques for performance improvement.
Use control charts and statistical process control (SPC) for monitoring healthcare quality.
Implement Lean Healthcare and Six Sigma methodologies to reduce clinical errors.
Analyze Key Performance Indicators (KPIs) for healthcare quality management.
Conduct root cause analysis and risk assessment in healthcare operations.
Utilize predictive analytics and AI-driven healthcare monitoring tools.
Improve patient safety and healthcare compliance through quality assurance frameworks.
Measure and evaluate healthcare service delivery performance effectively.
Apply evidence-based quality improvement strategies in hospitals and clinics.
Develop continuous quality improvement (CQI) programs for healthcare organizations.
Strengthen infection prevention and healthcare risk management systems using statistical methods.
Design actionable healthcare quality dashboards and reporting systems for decision-making.

Target Audience

Healthcare Quality Assurance Managers
Hospital Administrators and Healthcare Executives
Medical Laboratory Professionals
Public Health Officers and Epidemiologists
Nurses and Clinical Supervisors
Healthcare Data Analysts and Health Information Officers
Pharmaceutical and Biomedical Professionals
Healthcare Accreditation and Compliance Officers

Course Modules

Module 1: Fundamentals of Statistical Quality Control in Healthcare

Introduction to healthcare quality management
Principles of Statistical Quality Control (SQC)
Healthcare quality indicators and standards
Basics of healthcare performance measurement
Continuous Quality Improvement (CQI) concepts
Case Study: Implementation of quality control systems in a tertiary hospital to reduce patient waiting times.

Module 2: Statistical Tools for Healthcare Quality Improvement

Descriptive and inferential statistics
Sampling techniques in healthcare
Pareto analysis and histograms
Cause-and-effect (Fishbone) diagrams
Scatter plots and correlation analysis
Case Study: Using Pareto analysis to identify causes of medication administration errors.

Module 3: Statistical Process Control (SPC) in Healthcare

Introduction to SPC methodologies
Control charts for healthcare monitoring
Process capability analysis
Variation analysis in healthcare services
Monitoring clinical performance indicators
Case Study: Application of control charts to monitor infection rates in intensive care units.

Module 4: Lean Healthcare and Six Sigma Applications

Lean healthcare principles
Six Sigma DMAIC methodology
Waste reduction strategies in healthcare
Process mapping and workflow optimization
Error reduction in clinical operations
Case Study: Reducing laboratory turnaround time using Lean Six Sigma techniques.

Module 5: Healthcare Risk Management and Patient Safety

Patient safety frameworks
Risk assessment methodologies
Root Cause Analysis (RCA)
Failure Mode and Effects Analysis (FMEA)
Healthcare incident reporting systems
Case Study: Root cause investigation of surgical safety incidents in a hospital setting.

Module 6: Healthcare Data Analytics and Digital Quality Systems

Healthcare data management systems
Predictive analytics in healthcare
AI-driven quality monitoring tools
Healthcare dashboards and KPI reporting
Digital transformation in healthcare quality
Case Study: Use of predictive analytics to reduce hospital readmission rates.

Module 7: Quality Assurance Standards and Healthcare Accreditation

ISO standards in healthcare
Healthcare accreditation requirements
Clinical audit methodologies
Compliance monitoring systems
Regulatory quality frameworks
Case Study: Preparing a healthcare facility for international accreditation compliance.

Module 8: Developing Sustainable Quality Improvement Programs

Strategic healthcare quality planning
Performance benchmarking techniques
Developing quality improvement action plans
Change management in healthcare
Building a culture of continuous improvement
Case Study: Designing a sustainable hospital-wide quality improvement program.

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

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 Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Process monitoring Data quality control Performance analysis Error reduction strategies

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Sep 21 2026

21 Sep — 25 Sep 2026

💻 Online / Virtual session

Online $1,000