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Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data.
Tokede, Bunmi; Brandon, Ryan; Lee, Chun-Teh; Lin, Guo-Hao; White, Joel; Yansane, Alfa; Jiang, Xiaoqian; Kalenderian, Elsbeth; Walji, Muhammad.
Afiliación
  • Tokede B; Department of Diagnostic and Biomedical Sciences, University of Texas at Houston, Health Science Center, Houston, Texas, USA.
  • Brandon R; Willamette Dental Group and Skourtes Institute, Hillsboro, Oregon, USA.
  • Lee CT; Department of Periodontics & Dental Hygiene, The University of Texas Health Science Center at Houston, School of Dentistry, Houston, Texas, USA.
  • Lin GH; Postgraduate Periodontics Program, School of Dentistry, University of California, San Francisco, California, USA.
  • White J; Preventive and Restorative Dental Sciences, University of California, San Francisco/ UCSF School of Dentistry, San Francisco, California, USA.
  • Yansane A; Preventive and Restorative Dental Sciences, University of California, San Francisco/ UCSF School of Dentistry, San Francisco, California, USA.
  • Jiang X; Department of Health Data Science and AI, UTHealth School of Biomedical Informatics, Houston, Texas, USA.
  • Kalenderian E; Preventive and Restorative Dental Sciences, University of California, San Francisco/ UCSF School of Dentistry, San Francisco, California, USA.
  • Walji M; Department of Diagnostic and Biomedical Sciences, University of Texas at Houston, Health Science Center, Houston, Texas, USA.
J Clin Periodontol ; 51(5): 547-557, 2024 05.
Article en En | MEDLINE | ID: mdl-38212876
ABSTRACT

AIM:

To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions. MATERIALS AND

METHODS:

Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility. Chart reviews were conducted to confirm the algorithm's ability to accurately extract clinical data from the EHR, and then to test its ability to suggest an accurate diagnosis. Subsequently, refinements were made to address limitations of the data and specific clinical situations. Each refinement was evaluated through chart reviews by expert periodontists at the study sites.

RESULTS:

Three-hundred and twenty-three charts were manually reviewed, and a periodontal diagnosis (healthy, gingivitis or periodontitis including stage and grade) was made by expert periodontists for each case. After developing the initial version of the algorithm using the unmodified 2017 World Workshop criteria, accuracy was 71.8% for stage alone and 64.7% for stage and grade. Subsequently, 16 modifications to the algorithm were proposed and 14 were accepted. This refined version of the algorithm had 79.6% accuracy for stage alone and 68.8% for stage and grade together.

CONCLUSIONS:

Our findings suggest that a rule-based algorithm for suggesting a periodontal diagnosis using EHR data can be implemented with moderate accuracy in support of chairside clinical diagnostic decision making, especially for inexperienced clinicians. Grey-zone cases still exist, where clinical judgement will be required. Future applications of similar algorithms with improved performance will depend upon the quality (completeness/accuracy) of EHR data.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades Periodontales / Periodontitis / Gingivitis Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Clin Periodontol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades Periodontales / Periodontitis / Gingivitis Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Clin Periodontol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos