Minitab for Healthcare Quality Data Analysis Training Course

Course overview

Healthcare quality teams collect large volumes of data on waiting times, infection rates, falls, medication incidents, readmissions, complaints and patient experience, yet many reports stop at monthly averages and traffic-light ratings. This makes it difficult to distinguish common variation from a genuine deterioration, identify where a pathway is failing, or demonstrate whether an improvement intervention has worked. This course enables professionals to use Minitab to turn routinely collected healthcare data into defensible evidence for quality, safety and operational decisions.

Participants learn a practical workflow for importing, cleaning and structuring healthcare datasets; selecting suitable descriptive, graphical and statistical analyses; and presenting findings for clinical and managerial audiences. They use Minitab to build run charts and control charts, analyse Pareto patterns, test differences between groups, assess associations, evaluate process capability against service standards, and investigate causes of variation. The course addresses common healthcare data challenges, including small samples, skewed waiting-time data, proportions, rates, missing values and denominator changes.

Teaching combines instructor demonstration with guided Minitab labs using realistic datasets from emergency care, inpatient safety, outpatient access and community services. Participants apply statistical process control, hypothesis testing and root-cause prioritisation to an end-to-end quality improvement case. They leave with a reusable Minitab project containing analysed data, publication-ready charts, an interpretation guide and an action plan for applying the methods to a live measure in their own service.

The course is suited to healthcare staff who support quality improvement, clinical audit, patient safety, service performance or operational transformation and need to analyse data directly rather than rely solely on analysts.

Course objectives

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

• Import, clean and structure healthcare quality datasets in Minitab using worksheets, data types, calculated columns and filters
• Produce descriptive statistics, histograms, boxplots and stratified summaries for waiting-time, incident and patient-experience data
• Construct and interpret run charts and appropriate control charts for counts, proportions, rates and continuous measures
• Apply Pareto analysis and cause-and-effect matrices to prioritise recurring quality and patient-safety problems
• Test differences between clinical groups, time periods or interventions using t-tests, Mann-Whitney tests, chi-square tests and confidence intervals
• Assess relationships between operational variables using correlation, regression and residual diagnostics in Minitab
• Calculate process capability and performance against healthcare service standards, targets and specification limits
• Create a documented Minitab quality-data analysis project with charts, conclusions and improvement recommendations

Benefits of attending

Target competencies

Who should attend

• Quality Improvement Leads — who need to demonstrate whether improvement work has changed performance
• Patient Safety Managers — who investigate incident patterns and prioritise risk-reduction actions
• Clinical Audit Professionals — who analyse compliance, outcomes and variation against standards
• Healthcare Data Analysts — who support clinical and operational teams with reproducible statistical evidence
• Service Managers — who monitor access, flow, productivity and patient-experience measures
• Clinical Leads and Matrons — who use ward, specialty or pathway data to target local improvement

Requirements and prerequisites

This is a foundation-to-intermediate course and does not require previous Minitab experience or formal statistical qualifications. Participants should be comfortable using a Windows computer, working with spreadsheets and reading basic tables or charts. Familiarity with their organisation's quality measures, such as infection rates, waiting times, falls, complaints or audit compliance, will help them apply the examples. A basic understanding of numerators, denominators and percentages is useful. Participants do not need to know programming, advanced mathematics, regression modelling or Six Sigma terminology before attending; these are introduced where needed.

Training methodology

Each day combines short instructor-led explanations with live Minitab demonstrations and structured hands-on analysis. Participants work with realistic healthcare datasets covering emergency department waits, falls, infection surveillance, medication incidents and compliance audits. Exercises require learners to select chart types, configure Minitab output, interpret results and explain implications for improvement action. Small-group case discussions test how findings should be communicated to clinical and operational stakeholders. The final day includes application planning, where participants define a measure, dataset, analysis sequence and reporting approach for their own workplace.

Course outline

Day 1: Preparing healthcare quality data in Minitab

• Minitab interface, worksheets, projects and session output
• Healthcare quality measures: counts, proportions, rates and continuous data
• Importing CSV and Excel extracts into Minitab
• Data types, date-time fields and categorical coding
• Data cleaning with missing-value checks, recoding and calculated columns
• Descriptive statistics for clinical and operational measures
• Histograms, boxplots and stratified graphical summaries

Workshop: Participants prepare an emergency department waiting-time dataset and produce a validated descriptive analysis pack with summary tables and charts.

Day 2: Understanding variation with statistical process control

• Common-cause and special-cause variation in healthcare processes
• Run charts and median-based shift and trend rules
• Individuals and moving range charts for continuous measures
• P charts for compliance and patient-experience proportions
• U charts for incidents and infections with changing denominators
• C charts for event counts with stable opportunity
• Control-chart interpretation, annotations and escalation decisions

Workshop: Participants build and interpret control charts for falls, hand-hygiene compliance and weekly access performance, producing an escalation narrative.

Day 3: Prioritising problems and testing improvement results

• Pareto charts for incident categories, complaint themes and defect types
• Cross-tabulation and chi-square tests for categorical healthcare data
• Two-sample t-tests for before-and-after comparisons
• Mann-Whitney tests for skewed waiting-time and length-of-stay data
• Paired tests for repeated audit or matched patient measures
• Confidence intervals and practical versus statistical significance
• Selecting tests using outcome type, distribution and study design

Workshop: Participants analyse a medication-safety intervention dataset and produce a conclusion on whether observed changes warrant wider implementation.

Day 4: Explaining drivers and assessing process performance

• Scatterplots and correlation for service-demand and performance measures
• Simple linear regression for operational drivers
• Multiple regression concepts and model interpretation
• Residual plots, outliers and assumptions checking
• Cause-and-effect matrices for prioritising potential contributors
• Process capability analysis for time-based service standards
• Capability indices and performance gaps against specification limits

Workshop: Participants model contributors to outpatient delay and complete a capability assessment against a referral-to-treatment service standard.

Day 5: Reporting evidence and applying Minitab at work

• Selecting charts for board, clinical governance and team audiences
• Exporting Minitab graphs and tables for reports and presentations
• Writing statistical interpretations in plain clinical language
• Documenting data definitions, assumptions and analysis decisions
• Minitab project organisation for reproducible quality analysis
• IHI Model for Improvement links to measurement and PDSA cycles
• Workplace analysis planning for a live quality measure

Workshop: Participants complete a capstone quality-improvement case and produce a Minitab project, management-ready findings summary and 90-day application plan.

Tools & standards covered

Minitab Statistical Software, Minitab Workspace, Microsoft Excel, IHI Model for Improvement

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions