This study, titled "Automated Indicator Feedback for Improving the Quality of Discharge Letters: A Cluster-Randomized Controlled Trial" (FIAQ-LS), aims to evaluate whether continuous real-time feedback to hospital teams can improve the quality of discharge letters. Discharge letters are critical for ensuring continuity of care and reducing adverse events by providing detailed information about a patient's hospital stay to both the patient and their primary care physician.

The study will be conducted at Grenoble Alpes University Hospital and involve 40 hospital services across three campuses. The trial design includes two parallel arms: an intervention group receiving monthly performance feedback through automated dashboards and a control group with no additional intervention. Services are randomized into these groups using a stratified cluster approach.

The primary objective is to assess whether this intervention increases the proportion of discharge letters validated on the day of discharge compared to usual care. Secondary objectives include evaluating patient satisfaction, rates of unplanned 30-day readmissions, and completeness of discharge letter content.

The study will include data from approximately 132,000 patient stays over two phases: a pre-implementation observational period (12 months) and an intervention phase (12 months). All data will be collected and analyzed anonymously, with findings expected to inform the broader implementation of quality improvement strategies in French hospitals.

Detailed Description Effective communication at hospital discharge is vital for continuity of care and patient safety. Discharge letters summarize the hospital stay, outlining diagnoses, treatments, and follow-up care. Despite national guidelines mandating that discharge letters be validated and provided to patients on the day of discharge, compliance remains suboptimal in France, with average performance scores well below targets.

This study seeks to address this gap through an automated feedback mechanism. Using the hospital's electronic health record (EHR) system, the study will generate monthly dashboards for each participating service in the intervention group. These dashboards will provide a real-time view of performance metrics, including the proportion of discharge letters validated on the day of discharge and the completeness of required content fields.

The trial employs a cluster-randomized controlled design with 40 hospital services as the unit of randomization. Services are stratified by activity type (medicine, surgery/obstetrics) and baseline performance. The study is divided into two phases:

Pre-implementation Phase (January 2024 - January 2025): A 12-month observational period to collect baseline data and stratify services for randomization.

Implementation Phase (February 2025 - February 2026): Intervention services receive monthly performance feedback, while control services continue with standard care practices.

The primary endpoint is the proportion of hospital stays where discharge letters are validated on the day of discharge. Secondary outcomes include:

Patient satisfaction, measured through the national "e-Satis" survey. Rates of unplanned readmissions within 30 days of discharge. Completeness of discharge letters, evaluated across mandated content fields (e.g., patient identification, discharge summary, follow-up plan).

This study will enroll all eligible patient stays within the 40 participating services, excluding stays of less than 24 hours or cases where the patient died during hospitalization. The anticipated sample size is 132,000 stays.

Data collection will rely on routine administrative data from the EHR system, anonymized at the patient level. Statistical analyses will adopt a "difference-in-differences" approach, comparing changes in outcomes between the intervention and control groups over time. A mixed-effects logistic regression model will account for intra-cluster correlations.

The results of this study aim to demonstrate the effectiveness of automated feedback in driving quality improvements in hospital discharge processes. If successful, the approach could be scaled across other hospitals in France, contributing to better continuity of care and patient outcomes.

Inclusion Criteria:

• Patients hospitalized for at least 24 hours in participating services.
• Patients discharged alive directly from participating services.

Exclusion Criteria:

• Patients hospitalized for less than 24 hours.
• Patients who died during hospitalization.
• Stays in services not meeting inclusion criteria (e.g., psychiatry, long-term care, emergency services with rare direct discharges, or critical care units).

The study plans to share de-identified individual participant data (IPD) with other researchers upon request, after the publication of primary results. The shared data will include:

Anonymized patient-level data for all outcomes (e.g., discharge letter generation and validation times, patient satisfaction scores, readmission rates).

Aggregated service-level performance data. Access to IPD will be granted under a data-sharing agreement to ensure proper use and compliance with ethical guidelines. Requests for data must be submitted to the corresponding investigator and approved by the study's oversight committee.

IPD and supporting information will be made available starting 6 months after publication of the primary study results and will remain accessible for 5 years.

Access to the IPD and supporting documentation will be granted to researchers upon request. Requests must include a detailed research proposal outlining the intended use of the data, which will be reviewed by the study's oversight committee. Approved researchers will sign a data-sharing agreement to ensure compliance with ethical and data protection standards. Access will be provided via a secure platform managed by the CHU Grenoble Alpes.

The statistician in charge of the data analyses will be blinded to the allocation of hospital services to the intervention or control group to prevent bias in the statistical evaluation of outcomes. This includes the primary outcome (proportion of discharge letters validated on the day of discharge) and secondary outcomes.

Outcomes assessors and the statistician will work with anonymized datasets without group allocation information. However, participants (hospital services), care providers, and investigators managing the intervention are not masked due to the need to deliver feedback in real-time and monitor its implementation.