Achieving Diagnostic Excellence Through Prevention and Teamwork — ADEPT  

Diagnostic Errors Clinical Trial

Official title: Achieving Diagnostic Excellence Through Prevention and Teamwork (ADEPT)

Status Not yet recruiting

Clinical Trial Summary

This study seeks to link a group of hospitals to measure and share the rates of diagnostic errors, to understand underlying causes of diagnostic errors, and develop ways that hospitals, clinicians, and patients can work together to avoid diagnostic errors and harms due to those errors. The investigators will test how data sharing and collaboration improve diagnostic processes and develop approaches which can be sustained into the future. The approach represents a novel application of rigorous outcome adjudication to the problem of inpatient diagnostic errors using a learning health system model.

Clinical Trial Description

Many factors contribute to diagnostic errors, but key among them are foundational issues in healthcare: complex and fragmented care systems, the limited time available to providers trying to ascertain a firm diagnosis, and the work systems and cultures that support or impede improvements in diagnostic performance. While approaches to identifying diagnostic errors exist, few studies have linked identification of underlying systemic and structural causes of errors to existing quality improvement programs in hospitals. Even fewer have applied resilience theories or positive deviance approaches to characterize the features of cases where the diagnostic process is optimal and then use those findings to frame health system improvement. This application builds directly on the investigators' currently funded study - Utility of Predictive Systems in Diagnostic Errors (UPSIDE) - which is defining risk factors, underlying causes, and prevalence of diagnostic errors among patients admitted to hospitals participating in a 55-hospital research collaborative, the Hospital Medicine Reengineering Network (HOMERuN). UPSIDE has developed reference standard approaches to adjudication of diagnostic errors, defined factors associated with errors, and created collaborations with participating sites and national organizations, providing a uniquely powerful opportunity to transform how diagnostic process evaluation programs can be used to improve patient safety. The overall goal of this Center is to turn the investigators' highly successful multicenter network into a diagnostic error learning health system that will integrate diagnostic error assessments into existing quality and safety programs, provide support and expertise needed to reduce diagnostic errors, and catalyze scientific, personnel, and infrastructure changes which will last beyond the duration of this grant. To achieve the study's overall goals, the investigators will: 1) Implement a case review infrastructure which can accurately identify diagnostic errors and characterize diagnostic processes among patients suffering inpatient deaths, ICU transfers, or rapid-response team calls taking place at hospitals associated the Hospital Medicine Reengineering Network; 2) Develop site-level audit and feedback and group-wide benchmarking reports of error rates, diagnostic process faults, diagnostic process resilience features and use these data to frame collaboration between existing safety and quality programs at participating sites; 3) Use the data and collaborative model to develop and pilot test interventions based on highest priority findings; and 4) Develop understanding of the program's reach, adoption, implementation, and maintenance, as well feasibility and initial experience with pilot interventions. This project will establish a learning health system which can achieve excellence in diagnosis as an ongoing part of care, a system which can be a model for others as well. ;

Related Conditions & MeSH terms

• Diagnostic Errors

• NCT number: NCT05747755
• Study type: Interventional
• Source: Brigham and Women's Hospital
• Contact: Tiffany Lee
• Phone: 415-476-6949
• Email: tiffany.lee@ucsf.edu
• Status: Not yet recruiting
• Phase: N/A
• Start date: March 2023
• Completion date: September 2026