The Burden of Diagnostic Error in Dentistry: A Study on Periodontal Disease Misclassification.

clinical decision support diagnostic error diagnostic excellence patient safety trigger tool

Journal

Journal of dentistry
ISSN: 1879-176X
Titre abrégé: J Dent
Pays: England
ID NLM: 0354422

Informations de publication

Date de publication:
01 Jul 2024
Historique:
received: 10 05 2024
revised: 26 06 2024
accepted: 28 06 2024
medline: 4 7 2024
pubmed: 4 7 2024
entrez: 3 7 2024
Statut: aheadofprint

Résumé

Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case. An EHR-based trigger tool (a retrospective record review instrument that uses a list of triggers (or clues), i.e., data elements within the health record, to alert reviewers to the potential presence of a wrong diagnosis) was developed, tested and run against the EHR at the two participating sites to flag all cases having a potential misdiagnosis. All cases flagged as potentially misdiagnosed underwent extensive manual reviews by two calibrated domain experts. A subset of the non-flagged cases was also manually reviewed. A total of 2,262 patient charts met the study's inclusion criteria. Of these, the algorithm flagged 1,124 cases as potentially misclassified and 1,138 cases as potentially correctly diagnosed. When the algorithm identified a case as potentially misclassified, compared to the diagnosis assigned by the gold standard, the kappa statistic was 0.01. However, for cases the algorithm marked as potentially correctly diagnosed, the review against the gold standard showed a kappa statistic of 0.9, indicating near perfect agreement. The observed proportion of diagnostic misclassification was 32%. There was no significant difference by clinic or provider characteristics. Our work revealed that about a third of periodontal cases are misclassified. Diagnostic errors have been reported to happen more frequently than other types of errors, and to be more preventable. Benchmarking diagnostic quality is a first step. Subsequent research endeavor will delve into comprehending the factors that contribute to diagnostic errors in dentistry and instituting measures to prevent them. This study sheds light on the significance of diagnostic excellence in the delivery of dental care, and highlights the potential role of technology in aiding diagnostic decision-making at the point of care.

Sections du résumé

BACKGROUND BACKGROUND
Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case.
METHODS METHODS
An EHR-based trigger tool (a retrospective record review instrument that uses a list of triggers (or clues), i.e., data elements within the health record, to alert reviewers to the potential presence of a wrong diagnosis) was developed, tested and run against the EHR at the two participating sites to flag all cases having a potential misdiagnosis. All cases flagged as potentially misdiagnosed underwent extensive manual reviews by two calibrated domain experts. A subset of the non-flagged cases was also manually reviewed.
RESULTS RESULTS
A total of 2,262 patient charts met the study's inclusion criteria. Of these, the algorithm flagged 1,124 cases as potentially misclassified and 1,138 cases as potentially correctly diagnosed. When the algorithm identified a case as potentially misclassified, compared to the diagnosis assigned by the gold standard, the kappa statistic was 0.01. However, for cases the algorithm marked as potentially correctly diagnosed, the review against the gold standard showed a kappa statistic of 0.9, indicating near perfect agreement. The observed proportion of diagnostic misclassification was 32%. There was no significant difference by clinic or provider characteristics.
CONCLUSION CONCLUSIONS
Our work revealed that about a third of periodontal cases are misclassified. Diagnostic errors have been reported to happen more frequently than other types of errors, and to be more preventable. Benchmarking diagnostic quality is a first step. Subsequent research endeavor will delve into comprehending the factors that contribute to diagnostic errors in dentistry and instituting measures to prevent them.
CLINICAL SIGNIFICANCE CONCLUSIONS
This study sheds light on the significance of diagnostic excellence in the delivery of dental care, and highlights the potential role of technology in aiding diagnostic decision-making at the point of care.

Identifiants

DOI: 10.1016/j.jdent.2024.105221 PMID: 38960000
pubmed: 38960000
pii: S0300-5712(24)00390-7
doi: 10.1016/j.jdent.2024.105221
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105221

Informations de copyright

Copyright © 2024. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Bunmi Tokede (B)

Department of Diagnostic and Biomedical Sciences, University of Texas at Houston, Health Science Center, Houston, Texas, USA. Electronic address: oluwabunmi.tokede@uth.tmc.edu.

Alfa Yansane (A)

Preventive and Restorative Dental Sciences, University of California, San Francisco/ UCSF School of Dentistry, San Francisco, CA, USA, 3333 California Street, Ste. 495, San Francisco, CA 94118. Electronic address: Alfa-Ibrahim.Yansane@ucsf.edu.

Ryan Brandon (R)

Willamette Dental Group and Skourtes Institute, Hillsboro, OR, USA. Electronic address: rbrandon@willamettedental.com.

Guo-Hao Lin (GH)

Postgraduate Periodontics Program, School of Dentistry, University of California, San Francisco, 707 Parnassus Avenue, D-3015, San Francisco, CA 94143. Electronic address: Guo-Hao.Lin@ucsf.edu.

Chun-Teh Lee (CT)

Department of Periodontics & Dental Hygiene, The University of Texas Health Science Center at Houston School of Dentistry, 7500 Cambridge Street, Suite 6470. Electronic address: chun-teh.lee@uth.tmc.edu.

Joel White (J)

Preventive and Restorative Dental Sciences, University of California, San Francisco/ UCSF School of Dentistry, 707 Parnassus Avenue, D-3248, Box 0758, San Francisco, CA 94143. Electronic address: joel.white@ucsf.edu.

Xiaoqian Jiang (X)

UTHealth School of Biomedical informatics, 7000 Fannin St Suite 600, Houston, Texas 77030. Electronic address: Xiaoqian.Jiang@uth.tmc.edu.

Eric Lee (E)

Department of Orofacial Sciences, University of California San Francisco. Electronic address: ericd.lee@ucsf.edu.

Alaa Alsaffar (A)

Department of Periodontics and Dental Hygiene, University of Texas Health Science Center at Houston, School of Dentistry, Houston, TX, USA. Electronic address: alaa.alsaffar@uth.tmc.edu.

Muhammad Walji (M)

Diagnostic and Biomedical Sciences Department, University of Texas Health Science Center at Houston, School of Dentistry, Houston, 7500 Cambridge, SOD 4184, Houston, TX 77054. Electronic address: Muhammad.f.walji@uth.tmc.edu.

Elsbeth Kalenderian (E)

Marquette School of Dentistry, Surgical Sciences, 1801 West Wisconsin Avenue, PO Box 1881, Milwaukee, WI, USA. Electronic address: elsbeth.kalenderian@marquette.edu.

Classifications MeSH