NCT06835153

Automatic Feedback Indicator to Enhance the Hospital Discharge Communication Between Acute Care and Primary Care.

Automatic Feedback Indicator to Enhance the Hospital Discharge Communication Between Acute Care and Primary Care: a Randomized Controlled Cluster Trial

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 132,000 (estimated)
• Sponsor: University Hospital, Grenoble · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

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

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.

Conditions

• Continuity of Care
• Patient Safety
• Hospital Discharge Communication Processes
• Communication
• Quality Indicators, Health Care
• Electronic Health Records

Interventions

Type	Name	Description
OTHER	Monthly Performance Feedback with Dashboards (Automated Audit and Feedback)	Hospital services in the intervention group will receive monthly automated dashboards that provide detailed performance metrics. These include: The proportion of patients with a discharge letter generated on the day of discharge, The proportion of discharge letters validated on the day of discharge, Median delays for generating discharge letters, Median delays for validating discharge letters. The dashboards are shared with all physicians, nurse managers, and secretarial staff in each service. A designated quality improvement officer is available to assist teams in interpreting the data and implementing organizational changes based on the feedback. The intervention uses real-time data extraction from the hospital's electronic health record system to generate these insights.

Timeline

• Start date: 2025-03-01
• Primary completion: 2026-02-01
• Completion: 2026-02-01
• First posted: 2025-02-19
• Last updated: 2025-02-19

Locations

1 site across 1 country: France