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Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network.

ABSTRACT: BACKGROUND:Resources within the Undiagnosed Diseases Network (UDN), such as genome sequencing (GS) and model organisms aid in diagnosis and identification of new disease genes, but are currently difficult to access by clinical providers. While these resources do contribute to diagnoses in many cases, they are not always necessary to reach diagnostic resolution. The UDN experience has been that participants can also receive diagnoses through the thoughtful and customized application of approaches and resources that are readily available in clinical settings. METHODS:The UDN Genetic Counseling and Testing Working Group collected case vignettes that illustrated how clinically available methods resulted in diagnoses. The case vignettes were classified into three themes; phenotypic considerations, selection of genetic testing, and evaluating exome/GS variants and data. RESULTS:We present 12 participants that illustrate how clinical practices such as phenotype-driven genomic investigations, consideration of variable expressivity, selecting the relevant tissue of interest for testing, utilizing updated testing platforms, and recognition of alternate transcript nomenclature resulted in diagnoses. CONCLUSION:These examples demonstrate that when a diagnosis is elusive, an iterative patient-specific approach utilizing assessment options available to clinical providers may solve a portion of cases. However, this does require increased provider time commitment, a particular challenge in the current practice of genomics.

SUBMITTER: Cope H

PROVIDER: S-EPMC7549585 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network.

Cope Heidi H Spillmann Rebecca R Rosenfeld Jill A JA Brokamp Elly E Signer Rebecca R Schoch Kelly K Glanton Emily E Sullivan Jennifer A JA Macnamara Ellen E Lincoln Sharyn S Golden-Grant Katie K Orengo James P JP Clark Gary G Burrage Lindsay C LC Posey Jennifer E JE Punetha Jaya J Robertson Amy A Cogan Joy J Phillips John A JA Martinez-Agosto Julian J Shashi Vandana V

Molecular genetics & genomic medicine 20200730 10

<h4>Background</h4>Resources within the Undiagnosed Diseases Network (UDN), such as genome sequencing (GS) and model organisms aid in diagnosis and identification of new disease genes, but are currently difficult to access by clinical providers. While these resources do contribute to diagnoses in many cases, they are not always necessary to reach diagnostic resolution. The UDN experience has been that participants can also receive diagnoses through the thoughtful and customized application of ap ...[more]

PMID: 32730690

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Project description:ObjectivesTo evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs.MethodsA retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs and radiologists' authorised reports using a deep learning and natural language processing algorithm, respectively. The AI-detected discrepant findings between images and reports were assessed for clinical relevance by an external radiologist, as part of the commercial service provided by the AI vendor. The selected missed findings were subsequently returned to the institution's radiologist for final review.ResultsIn total, 25,104 chest radiographs of 21,039 patients (mean age 61.1 years ± 16.2 [SD]; 10,436 men) were included. The AI software detected discrepancies between imaging and reports in 21.1% (5289 of 25,104). After review by the external radiologist, 0.9% (47 of 5289) of cases were deemed to contain clinically relevant missed findings. The institution's radiologists confirmed 35 of 47 missed findings (74.5%) as clinically relevant (0.1% of all cases). Missed findings consisted of lung nodules (71.4%, 25 of 35), pneumothoraces (17.1%, 6 of 35) and consolidations (11.4%, 4 of 35).ConclusionThe AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low.Clinical relevance statementThe AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography.Key points• A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions. • Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations. • Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist's reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevance.

Dataset Information

Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network.

Publications

Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network.

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