Health care organizations in Georgia have increasingly recognized the need for structured, actionable quality of care (QoC) indicators to support patient safety, clinical governance, and continuous quality improvement at the facility level. In a context where hospitals are strengthening internal accountability mechanisms and aligning with international accreditation standards, there has been a growing emphasis on developing indicators that are both methodologically sound and operationally feasible.

Rather than focusing on a single indicator, the Georgian experience centers on the development and use of a set of facility-level QoC indicators aligned with hospital strategic priorities, patient safety risks, and high-impact clinical processes. Indicators span both process and outcome domains, covering areas such as diagnostic turnaround times, adherence to clinical protocols, patient identification, incident reporting, and perioperative safety.

The indicators are designed primarily for facility-level use, supporting departmental performance reviews, clinical governance discussions, and targeted quality improvement initiatives. While not currently part of a national reporting framework, the indicators are structured to allow future alignment with national requirements if needed.

Stakeholder engagement was embedded throughout the indicator development process and followed a collaborative, bottom-up model. Executive leadership provided strategic direction by approving key performance priorities, while clinical departments played a central role in translating these priorities into measurable indicators.

The development process followed a structured and participatory methodology. It began with defining Key Behavior Objectives (KBOs) that reflected hospital strategic goals, patient safety priorities, accreditation standards, and areas of high clinical risk. These objectives served as the organizing framework for indicator selection.

Departmental teams then collaboratively translated KBOs into specific indicators by analyzing workflows, identifying risk points, and defining numerators, denominators, data sources, and reporting frequency. Indicators were assessed against core criteria, including relevance to patient outcomes and safety, measurability using available data, clarity of definition, and actionability for quality improvement.

The Quality Department reviewed all proposed indicators to ensure consistency, standardization, and alignment with hospital-wide monitoring systems. Each indicator was assigned to a designated indicator owner, typically a department head, unit manager, or clinical lead. Indicator owners were responsible for convening departmental teams, engaging frontline staff, and ensuring that proposed measures reflected clinical realities and existing workflows. The Quality Department maintained continuous engagement with departments through regular meetings, feedback sessions, and joint reviews, supporting the clarification of definitions, feasibility assessments, and the refinement of indicator specifications.

Once finalized, indicators were integrated into routine management cycles, including monthly and quarterly departmental reviews, quality committees, and performance dashboards.

This approach fostered strong ownership, ensured alignment with day-to-day practice, and reduced resistance to data collection and use. Engagement focused not only on defining indicators but also on building a shared understanding of how indicator results would be interpreted and used for improvement rather than punitive monitoring.

Formal validation was an integral component of the implementation process. Validation activities included field testing in real clinical settings, end-user feedback, and data verification exercises to confirm that indicators were being applied consistently across departments. Regular audits were conducted to compare reported values with source data and identify discrepancies.

Targeted training sessions were delivered to clinical staff, indicator owners, and quality personnel on indicator definitions, data collection methods, and use of Power BI dashboards. Ongoing mentorship was provided by the Quality Department, including one-on-one support to departments facing data or implementation challenges.

Standardized tools, such as indicator definition sheets, data collection templates, and reporting guides, were developed to promote consistency and reduce ambiguity. These efforts strengthened local capacity to manage indicators independently and supported integration of measurement into routine practice.

Validated indicators were fully integrated into the hospital’s digital monitoring infrastructure via Power BI dashboards, enabling real-time or near-real-time data visualization, trend analysis, and cross-departmental comparison. This integration supported transparency, accountability, and timely decision-making.

Indicator data were actively used to inform service delivery improvements and policy adjustments within hospitals. For example, analysis of turnaround time indicators for laboratory and radiology services led to workflow redesign, clearer prioritization protocols, and improved coordination with clinical units, resulting in faster diagnostic reporting and more timely clinical decision-making.

Similarly, indicators highlighting gaps in protocol adherence prompted revision of clinical pathways for conditions such as sepsis, stroke, and postoperative care. Patient safety indicators, including those related to surgical checklists and patient identification, informed updates to policies, staff retraining, and the introduction of additional safeguards, such as barcode scanning.

Key challenges included inconsistent data capture across departments, the absence of structured data sources for some indicators, and initial resistance to transparency in performance reporting. These challenges were addressed through iterative refinement, close collaboration with indicator owners, and sustained communication between departments and the Quality Department.

Validation challenges related to data availability and analytical capacity were mitigated through targeted training, technical support, and gradual strengthening of data systems. The iterative nature of the process allowed indicators to be refined over time as workflows and data quality improved.