Achieving Diagnostic Excellence Through Prevention and Teamwork

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Achieving Diagnostic Excellence Through Prevention and Teamwork (ADEPT)

Conditions
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Diagnostic Errors

Intervention / Treatment
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* Behavioral: ADEPT Program


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Adult patients admitted to general medicine services at one of the participating hospitals and who either died during the hospitalization, were transferred to the ICU >= 48 hours after admission, or had a rapid response. Exclusion Criteria: Admitted for a non-medical reason Patients coded in the field who are moribund on arrival to the hospital

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Non-Randomized
Intervention Model: Single Group Assignment
Interventional Model Description: Interrupted time series
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| No Intervention: Pre-intervention (usual care)<br>Patients admitted to study hospitals in the 12 months prior to the start of the intervention | |
| Experimental: Intervention<br>Patients admitted to study hospitals during the 36 months of the intervention | Behavioral: ADEPT Program<br>* Integration of surveillance for diagnostic errors into usual care, benchmarking and sharing of results across hospitals, expert mentoring of quality and safety personnel in change management, pilot testing and refinement of Safety I and Safety II interventions to reduce systemic causes of diagnostic errors and to increase resilience, thus promoting diagnostic excellence.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Diagnostic Errors | Proportion of patients in each trigger category (death, ICU transfer, rapid response) with an adjudicated diagnostic error. | Through hospital discharge, an average of 10 days | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Harmful Diagnostic Errors | Proportion of patients in each trigger category with diagnostic errors contributing to death, permanent harm, or requiring life-sustaining treatment using NCC-MERP criteria. | Through hospital discharge, an average of 10 days | 
| Diagnostic process faults | Number of diagnostic process faults per patient, as determined by the DEER taxonomy during adjudication | Through hospital discharge, an average of 10 days |