Clinical Research Directory

Quality Indicators in Obstetrics

The study examines whether midwives and doctors are familiar with quality indicators in clinical obstetrics and whether these are used as a tool for continuous improvement process.

Training and Support Programme on Data-driven Quality Development for Swiss Long-Term Care Facilities (NIP-Q-UPGRADE Subaim 2.6)

Since 2019, long-term care facilities (LTCFs) in Switzerland have been required by the Federal Insurance Law (KVG, Art. 59a) to report data for the calculation and public reporting of medical quality indicators (MQIs) in four clinical domains: polypharmacy, pain, malnutrition, and physical restraints. This data serves both for monitoring care quality at the national level through public reporting and for internal quality development. Contextual analysis showed that various quality development methods are already known and used in Swiss LTCFs. However, significant challenges remain: limited resources, time constraints, and restricted access to MQI data hinder effective use. Facilities reported a greater need for support in using MQI data. They also expressed interest in peer networking, structured support for applying quality methods (such as Plan Do Check Act cycles (PDCA)), and practical tools such as training, best-practice examples, and additional resources. Residents and relatives also expressed a strong interest in being more involved in decision-making and care quality discussions. The overall aim of the current study is to test a quality development training programme that supports LTCFs in using MQI data for continuous data-driven care quality development. The study is structured into three thematic areas: 1. MQI Results Literacy - Supporting LTCFs in interpreting MQI reports and benchmarks. 2. Impactful Actions - Supporting LTCFs to translate MQI results into concrete quality development actions using PDCA cycles. 3. Sparking Culture - Integrating data-driven quality development into everyday practice and fostering a culture of continuous development, with a strong emphasis on strengthening the involvement of residents, relatives, and leadership. The study follows a train-the-trainer strategy. Trainers instructed by the NIP-Q-UPGRADE research team provide structured training and coaching to Quality Leaders and management representatives of LTCFs. Quality Leaders then support their co-workers in quality development. The training programme consists of online and in-person trainings, training materials, practical tools, a website, guided tasks for facilities, and an email helpdesk for ongoing support. Study outcomes: This sub-study of the NIP-Q-UPGRADE programme aims to assess the acceptability, feasibility, fidelity, and costs related to the training programme, both at the facility level and at the trainer level.

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

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.

Artificial Intelligence in Colonoscopy

Colorectal cancer is the second most common malignancy in the countries of the European Union. Colonoscopy is the primary method for detecting and preventing the development of colorectal cancer is endoscopic examination. This study aims to evaluate the impact of artificial intelligence on the detection rate of polyps and early stages of colorectal cancer.