Evidence map›Paper›PMID 38696319›Full record

ArticleDiagnosis (Berlin, Germany)2024

Computable phenotype for diagnostic error: developing the data schema for application of symptom-disease pair analysis of diagnostic error (SPADE).

Ahmed Hassoon, Charles Ng, Harold Lehmann, Hetal Rupani, Susan Peterson, Michael A Horberg, Ava L Liberman, Adam L Sharp, Michelle C Johansen, Kathy McDonald and 2 more

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In one paragraph

Article in Diagnosis (Berlin, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Ahmed HassoonDepartment of Epidemiology, 25802 Johns Hopkins University Bloomberg School of Public Health , Baltimore, MD, USA.
Charles Ng5331 IQVIA Inc , San Francisco, CA, USA.
Harold Lehmann1500 The Johns Hopkins University School of Medicine , Baltimore, MD, USA.
Hetal Rupani1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
Susan PetersonEmergency Medicine, 1500 Johns Hopkins University School of Medicine , Baltimore, MD, USA.
Michael A HorbergMid-Atlantic Permanente Medical Group, 51637 Mid-Atlantic Permanente Research Institute , Rockville, MD, USA.
Ava L LibermanNeurology, 12295 Weill Cornell Medicine , New York, NY, USA.
Adam L SharpDepartment of Research & Evaluation, 82579 Kaiser Permanente Southern California , Pasadena, CA, USA.ORCID 0000-0002-6736-6184
Michelle C JohansenDepartment of Neurology and the Armstrong Institute Center for Diagnostic Excellence, 1500 Johns Hopkins University School of Medicine , Baltimore, MD, USA.
Kathy McDonaldJohns Hopkins University School of Nursing 15851 , Baltimore, MD, USA.
J Mathrew AustinDepartment of Anesthesia and Critical Care Medicine and the Armstrong Institute Center for Diagnostic Excellence, 1500 Johns Hopkins University School of Medicine , Baltimore, MD, USA.
David E Newman-TokerNeurology, 1501 Johns Hopkins Medicine , Baltimore, MD, USA.

Funding

Armstrong Institute Center for Diagnostic Excellence-Pursuing Scalable System-Level Diagnostic Quality, Value and Equity by Applying Safety Science to Emergency Department DiagnosisR18HS029350 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI MCDONALD, KATHRYN MACK, NEWMAN-TOKER, DAVID · 2022 to 2025
$4.0M
Towards a National Diagnostic Excellence Dashboard - Partnering with Stakeholders to Construct Evidence-Based Operational Measures of Misdiagnosis-Related HarmsR01HS027614 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI NEWMAN-TOKER, DAVID · 2020 to 2023
$1.5M
AHRQ HHS R01 HS027614AHRQ HHS R18 HS029350
6 · The paper itself

Abstract

objectivesDiagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts.

methodsWe created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility.

resultsWe mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms.

conclusionsData for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.

Indexed as

Diagnostic ErrorsElectronic Health RecordsPhenotypeHumansdiagnostic errormisdiagnosissymptom-disease pair analysis of diagnostic error (SPADE)

Identifiers

PMID38696319
PMCPMC11392038

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