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; J. Mathrew Austin; David E. Newman-Toker

doi:10.1515/dx-2023-0138

Article

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 , J. Mathrew Austin and David E. Newman-Toker

Published/Copyright: May 3, 2024

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From the journal Diagnosis Volume 11 Issue 3

Abstract

Objectives

Diagnostic 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.

Methods

We 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.

Results

We 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.

Conclusions

Data 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.

Keywords: symptom-disease pair analysis of diagnostic error (SPADE); diagnostic error; misdiagnosis

Corresponding author: Ahmed Hassoon, Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, E6035 21205-2103, Baltimore, MD, USA, E-mail: ahassoo1@jhu.edu

Acknowledgments

The author would like to thank Jamal Badr for assisting in manuscript formatting.

Research ethics: This study was approved by the JHU IRB for Quality Improvement. Each case study was approved by their respective research IRB at Johns Hopkins Medicine, Kaiser Permanente Southern California, and Kaiser Permanente Mid-Atlantic States.
Informed consent: Not applicable.
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors state no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

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Received: 2023-10-05

Accepted: 2024-04-01

Published Online: 2024-05-03

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https://doi.org/10.1515/dx-2023-0138

Keywords for this article

symptom-disease pair analysis of diagnostic error (SPADE); diagnostic error; misdiagnosis