The Emergency Department represents the main entry point to the hospital and a key setting for the management of urgent healthcare needs in the population. To date, the assessment of care quality has mainly focused on organizational aspects, with limited structured tools to systematically measure clinical and care processes. Overcrowding and limited resources make dedicated data collection unsustainable; therefore, it is necessary to rely on data already available from routine healthcare information systems. In this context, the study aims to assess the feasibility of using these data to construct quality indicators, as well as to evaluate their availability and reliability across participating centers. The study will also analyze variability in indicators among participating Emergency Departments, with the goal of identifying differences in care processes and potential areas for improvement.
The Emergency Department (ED) is the primary gateway to the hospital system, yet its quality has traditionally been measured only by organizational efficiency rather than clinical care. This study introduces a program to monitor the quality of assistance through standardized indicators, shifting the focus toward clinical and care appropriateness. Because Italian EDs face chronic overcrowding and resource shortages, the study is designed to be sustainable by using data already collected during routine clinical practice (current data flows) instead of requiring new, active data collection by staff. The main goal of this pilot study is to build a reliable monitoring system. First, it verifies if the necessary data are actually available and accurate within existing hospital databases. Second, it measures the quality of care provided by participating centers to identify significant variations in clinical processes. Finally, it aims to provide centers with detailed reports to help them interpret these findings and identify areas for improvement. This is a multicenter, retrospective, observational cohort study involving at least 10 EDs. The study analyzes all patient visits over a three-year period, from January 2023 to December 2025, to account for seasonal trends and the evolution of care over time. A total of 13 indicators are evaluated: * Mandatory Indicators: These focus on critical issues like "boarding" (the time spent waiting for a hospital bed), overall overcrowding, wait times for clinical visits, and the management of time-dependent conditions like sepsis. * Optional Indicators: These explore aspects such as the re-evaluation of patients in the waiting room, the use of "fast-track" assignments, and safety metrics like the frequency of falls among elderly patients. Data are extracted from hospital information systems and transferred via secure protocols for analysis. All information is pseudonymized at the source to protect patient privacy. The analysis assesses the reliability of the data and uses statistical tests to compare performance between different centers and over time. As a pilot project, this study serves to identify which indicators are robust enough to be included in a permanent, large-scale quality improvement framework.
Study Type
OBSERVATIONAL
Enrollment
650,000
Ospedale Civile SS.Antonio e Biagio
Alessandria, Italy
To verify the availability and reliability of data for the construction of indicators on the quality of care in Emergency Departments (EDs) in the hospital databases of the participating centers.
Data availability for calculating quality-of-care indicators in emergency departments will be assessed without statistical analyses. Data reliability will be evaluated by a multidisciplinary working group (statisticians, emergency physicians, nurses, and hospital information/management staff), considering clinical relevance, missing or incomplete data, and internal consistency. This approach will document potential limitations in data quality across participating hospital databases.
Time frame: July 2026 - November 2026
Rate of adherence to quality-of-care indicators per participating emergency department
Quality-of-care indicators will be calculated and, for each of them, 95% confidence intervals will be reported to quantify estimation uncertainty. Differences across participating emergency departments will be assessed using descriptive statistics (means, standard deviations, medians, interquartile ranges, boxplots, and histograms) and inferential tests appropriate to the indicator type: ANOVA and Kruskal-Wallis tests for quantitative indicators, and proportion difference tests for indicators expressed as percentages.
Time frame: April 2027 - July 2027
Change in quality-of-care indicator rates over the three-year study period
Temporal trends in quality-of-care indicators will be analyzed across the three-year data collection period (2023-2025) to quantify variations observed over time within and across participating emergency departments.
Time frame: December 2026 - March 2027
Proportion of patients managed in accordance with predefined quality-of-care indicators per participating emergency department
Quality-of-care indicators will be calculated according to predefined criteria, with 95% confidence intervals to quantify estimation uncertainty. Variability across participating emergency departments will be assessed using descriptive methods (means, standard deviations, medians, interquartile ranges, boxplots, and histograms) and inferential statistics, including ANOVA, Kruskal-Wallis tests, and proportion tests as appropriate. Temporal trends over the three-year study period will be analyzed. A retrospective application of the methodology will be performed to evaluate potential improvement signals under continuous monitoring conditions.
Time frame: December 2026 - March 2027
Number of improvement signals per quality-of-care indicator under simulated continuous monitoring
A retrospective application of the continuous monitoring methodology will be performed to evaluate the number of improvement signals that would have been detected had the monitoring system been continuously active across participating emergency departments during the study period.
Time frame: December 2026 - March 2027
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