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Supported by the

September 2022 – September 2026

DECODE focuses on decreasing diagnostic errors in medical imaging by implementing and evaluating a highly resilient system for care planning and coordination, as well as a peer learning system for clinical providers.

In addition, we seek consensus on evidence-based recommendations to enhance diagnosis of four targeted diseases – lung, prostate, pancreas and adrenal cancer.

Combined, these initiatives aim to help reduce delayed, wrong, or missed diagnoses that contribute to poor patient outcomes.

Project Aims

Supporting AHRQ’s research to address failures in the diagnostic process.

Multidisciplinary Team
A multidisciplinary diagnostic excellence center to address diagnostic errors.

Reliability & Resilience
Design enhancements to a pilot implementation of a highly reliable and resilient system, accelerate its implementation in a large healthcare system, and evaluate its impact on diagnostic errors using a mixed-methods analysis.

Diagnostic Precision and Consensus
Improve diagnostic precision and management by building consensus using available evidence.

Disseminate all DECODE methods and tools, including specifications for information technology systems and workflow processes.

Diagnostic Safety Centers of Excellence

In fiscal year 2022, Congress authorized funding to support AHRQ’s research to address failures in the diagnostic process, which may include the establishment of Research Centers of Diagnostic Excellence to develop systems, measures, and new technology solutions to improve diagnostic safety and quality. In response, DECODE was awarded 1 of 10 grants to establish it’s Diagnostic Safety Centers of Excellence.

DECODE: Diagnostic Excellence Center on Diagnostic Error
Ramin Khorasani, Brigham and Women’s Hospital
Ronilda Lacson, Brigham and Women’s Hospital

Diagnostic Accuracy Through Advancing EHR displaY, Education, and Surveillance (DATA-EYES)
Jeffrey A Gold, Oregon Health and Science University
David W. Bates, Brigham and Women’s Hospital
Raj M. Ratwani, MedStar Health Research Institute 

Achieving Better Cancer Diagnosis (ABCD): Identifying, Supporting, and Learning From Marginalized Patients Who Experience Delayed Cancer Diagnosis
Gordon David Schiff, Brigham and Women’s Hospital
Thomas Henry Gallagher, University of Washington Medical Center

The Patient-Partnered Diagnostic Center of Excellence
Kristen Elizabeth Miller, MedStar Health Research Institute
Traber L. Giardina, Baylor College of Medicine
Kelly Michelle Smith, University of Toronto

Center To Improve Clinical Diagnosis
Goutham Rao, University Hospitals of Cleveland
Mary Dolanksy, Case Western Reserve University
Sarah J. Koopman Gonzalez, Aspire Indiana
Marlene Rosemary Miller, Johns Hopkins University
Peter J. Pronovost, Johns Hopkins Hospital

Safety-II Together: Coupling Teaming Science With Patient Engagement and Health Information Transparency To Coproduce Diagnostic Excellence
Eric J Thomas, University of Texas Health Science Center, Houston
Sigall Bell, Beth Israel Deaconess Medical Center

Achieving Diagnostic Excellence through Prevention and Teamwork (ADEPT)
Andrew D. Auerbach, University of California, San Francisco
Jeffrey Lawrence Schnipper, Brigham and Women’s Hospital

Re-Engineering Patient and Family Communication To Improve Diagnostic Safety Resilience
Kathleen Elizabeth Walsh, Boston Children’s Hospital
Christopher Paul Landrigan, Boston Children’s Hospital


Closed-Loop Communication Tool

Impact of an Automated Closed-Loop Communication and Tracking Tool on the Rate of Recommendations for Additional Imaging in Thoracic Radiology Reports.

DeSimone AK, Kapoor N, Lacson R, Budiawan E, Hammer MM, Desai SP, Eappen S, Khorasani R.J Am Coll Radiol. 2023 Aug;20(8):781-788. doi: 10.1016/j.jacr.2023.05.004. Epub 2023 Jun 10.PMID: 37307897

Predictors of Completion of Clinically Necessary Radiologist-Recommended Follow-Up Imaging: Assessment Using an Automated Closed-Loop Communication and Tracking Tool.

Kapoor N, Lynch EA, Lacson R, Flash MJE, Guenette JP, Desai SP, Eappen S, Khorasani R.AJR Am J Roentgenol. 2023 Mar;220(3):429-440. doi: 10.2214/AJR.22.28378. Epub 2022 Oct 26.PMID: 36287625

Artificial Intelligence Tool for Information Extraction and Identification

Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging.

Abbasi N, Lacson R, Kapoor N, Licaros A, Guenette JP, Burk KS, Hammer M, Desai S, Eappen S, Saini S, Khorasani R.AJR Am J Roentgenol. 2023 Sep;221(3):377-385. doi: 10.2214/AJR.23.29120. Epub 2023 Apr 19.PMID: 37073901

Machine Learning Model Drift: Predicting Diagnostic Imaging Follow-Up as a Case Example.

Lacson R, Eskian M, Licaros A, Kapoor N, Khorasani R.J Am Coll Radiol. 2022 Oct;19(10):1162-1169. doi: 10.1016/j.jacr.2022.05.030. Epub 2022 Aug 16.PMID: 35981636

Factors Associated with Diagnostic Imaging Errors

Improving Report Clarity

Development and Assessment of an Information Technology Intervention to Improve the Clarity of Radiologist Follow-up Recommendations.

Guenette JP, Kapoor N, Lacson R, Lynch E, Abbasi N, Desai SP, Eappen S, Khorasani R.JAMA Netw Open. 2023 Mar 1;6(3):e236178. doi: 10.1001/jamanetworkopen.2023.6178.PMID: 37000450 Free PMC article.

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