Donabedian Model for Healthcare Quality Evaluation Training Course

Course overview

Healthcare quality teams are often asked to explain why a service is underperforming, whether an improvement programme has worked, and which investment will improve patient outcomes. Raw performance data alone rarely answers these questions. The Donabedian Model provides a disciplined way to connect structures such as staffing, equipment, governance and care environments; processes such as assessment, medication reconciliation and discharge planning; and outcomes such as harm, readmission, functional status and experience. This course helps participants move from fragmented indicators to an evidence-based account of how care quality is produced and where intervention is justified.

Participants learn to design Donabedian-based evaluation frameworks for hospitals, primary care, community services, residential care and integrated pathways. They define measurable structure, process and outcome indicators; build logic chains and indicator dictionaries; set numerator, denominator, inclusion and exclusion rules; assess data quality; and interpret associations without making unsupported causal claims. The course also addresses risk adjustment, equity stratification, balancing measures, benchmark selection, dashboard design and the use of AHRQ and OECD indicator sets.

Teaching combines expert-led model instruction with realistic quality cases, spreadsheet-based indicator analysis, dashboard critique and facilitated peer review. Participants work through a service-evaluation brief from problem definition to reporting recommendation. They leave with a completed Donabedian Quality Evaluation Pack: a service-specific framework, indicator specification sheets, data-collection plan, interpretation guide and a 90-day implementation plan suitable for discussion with clinical leaders and executives.

The course is designed for experienced healthcare and social care professionals who already work with service performance, safety, audit, accreditation, commissioning or improvement and need a rigorous evaluation method that stands up to operational and governance scrutiny.

Course objectives

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

• Construct a Donabedian structure-process-outcome framework for a defined healthcare or social care service
• Translate quality aims into measurable indicators with explicit numerators, denominators, inclusion criteria and exclusions
• Develop an indicator dictionary covering source systems, collection frequency, ownership, targets and reporting rules
• Assess indicator validity, reliability, completeness, timeliness and susceptibility to gaming
• Apply risk adjustment and equity stratification when comparing outcome performance across patient populations
• Use Excel and Power BI to analyse, visualise and interpret linked structure, process and outcome measures
• Critique quality dashboards and evaluation reports for unsupported causal claims, weak benchmarks and misleading charts
• Produce a Donabedian Quality Evaluation Pack with recommendations and a 90-day measurement implementation plan

Benefits of attending

Target competencies

Who should attend

• Healthcare Quality Managers — who need to design defensible service evaluations and quality reporting frameworks
• Clinical Governance Leads — who must connect safety, audit and assurance evidence to board-level decisions
• Quality Improvement Leads — who need to test whether process changes are producing meaningful patient outcomes
• Hospital and Service Managers — who must prioritise staffing, capacity and operational investments using quality evidence
• Commissioning and Contracting Managers — who evaluate provider performance and specify measurable quality requirements
• Social Care Quality Leads — who need to assess care environments, practice reliability and outcomes for people using services

Requirements and prerequisites

Participants should have practical experience of healthcare or social care delivery, quality improvement, clinical audit, governance, performance management or service evaluation. They should understand basic healthcare metrics such as rates, percentages, targets, trends and denominators, and be comfortable reading tables and charts. Familiarity with Excel is useful because exercises include indicator calculations and data checks; basic Power BI awareness is helpful but not essential. Participants should bring a laptop if attending live online or if they want to work on their own files. No statistical programming, advanced epidemiology, clinical qualification or prior formal training in the Donabedian Model is required.

Training methodology

The instructor introduces each Donabedian concept through a healthcare or social care scenario, then participants apply it to structured data and service documents. Exercises include mapping a deteriorating discharge pathway, writing indicator definitions, checking missing and inconsistent records in Excel, and reviewing a Power BI quality dashboard for interpretation errors. Small groups compare alternative measures and defend their choices against clinical, operational and equity considerations. Each day ends with feedback on the evolving evaluation pack, culminating in a peer-reviewed implementation plan for a participant-selected service.

Course outline

Day 1: Positioning the Donabedian Model in quality evaluation

• Origins, purpose and limits of the Donabedian Model
• Structure, process and outcome definitions in healthcare and social care
• Quality domains: safety, effectiveness, experience, equity, timeliness and efficiency
• Distinguishing audit, quality improvement, assurance and formal evaluation
• Service boundary definition and pathway mapping
• Problem statements, evaluation questions and stakeholder requirements
• Logic chains linking structural conditions to care outcomes

Workshop: Participants map a selected care pathway and produce a first-pass structure-process-outcome logic chain for a defined quality problem.

Day 2: Designing measurable quality indicators

• Indicator selection criteria: relevance, actionability and feasibility
• Structure indicator design for workforce, capability, equipment and governance
• Process indicator design for reliability, adherence and timeliness
• Outcome indicator design for harm, recovery, experience and utilisation
• Numerators, denominators, populations and observation periods
• Inclusion, exclusion and attribution rules
• Indicator dictionaries and measurement ownership

Workshop: Participants create indicator specification sheets for three linked measures, including definitions, sources, targets and accountable owners.

Day 3: Data quality, comparison and interpretation

• Healthcare data sources: EHRs, incident systems, surveys, audits and administrative datasets
• Data completeness, accuracy, consistency, timeliness and lineage checks
• Missing data, small numbers and denominator instability
• Baseline construction, run charts and control-chart interpretation
• Risk adjustment concepts and case-mix considerations
• Equity stratification by demographic, geographic and deprivation factors
• Benchmarking with AHRQ Quality Indicators and OECD HCQI measures

Workshop: Using a supplied dataset, participants audit data quality and produce an interpreted trend analysis with risk and equity caveats.

Day 4: Analysing linked measures and communicating findings

• Testing plausible links between structures, processes and outcomes
• Correlation, confounding and limits of causal inference
• Balancing measures and unintended consequences
• Excel pivot tables, formulas and validation checks for quality indicators
• Power BI dashboard structure for executive and service-level audiences
• Chart selection, annotation and statistical storytelling
• Escalation thresholds, exception reporting and governance narratives

Workshop: Participants rebuild a flawed quality dashboard and produce a concise evidence narrative identifying what action is and is not justified.

Day 5: Implementing a Donabedian evaluation framework

• Prioritising improvement opportunities from evaluation evidence
• Measurement plans, sampling approaches and reporting cadence
• Roles, RACI assignments and data-governance controls
• Clinical engagement and co-design of indicator use
• Evaluation reporting for boards, regulators and commissioning meetings
• Ninety-day implementation roadmaps and review checkpoints
• Peer review against a Donabedian evaluation quality checklist

Workshop: Participants finalise and present their Donabedian Quality Evaluation Pack, receiving peer and instructor feedback on its implementation plan.

Tools & standards covered

Microsoft Excel, Microsoft Power BI, AHRQ Quality Indicators, OECD Health Care Quality and Outcomes Indicators

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