Achieving Diagnostic Excellence through Prevention and Teamwork (ADEPT)

PROJECT SUMMARY/ABSTRACT
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 our 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 our 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 our 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 our 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 our overall goals, we 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) To 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 our sites; 3) To use our data and collaborative model to develop
and pilot test interventions based on highest priority findings; and 4) Develop understanding of our 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.

Grant Number: 5R18HS029366-04
NIH Institute/Center: AHRQ
Principal Investigator: ANDREW AUERBACH