ArticleJournal of clinical periodontology2024
Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data.
Article in Journal of clinical periodontology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating the Performance and Implementation of the 2018 Classification of Periodontal Diseases: A Systematic Review and Survey.Journal of clinical periodontology · 2025Pooled it
- Periodontitis Prediction Model Using Linked Electronic Health and Dental Records.JDR clinical and translational research · 2026Article
- Assessment of periodontal condition using basic periodontal examination scores: A retrospective clinical study.Bioinformation · 2026Article
- Artificial intelligence for detection and classification of furcation defects using radiographic imaging: A systematic review.Imaging science in dentistry · 2025Review
- Article
- The oral microbiome and atherosclerosis: current evidence on association, mechanisms, and clinical implications.Frontiers in immunology · 2025Review
- Digital health interventions use cases: Classification and taxonomy development, a scoping review.Digital healthArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
aimTo 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
methodsUsing 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.
resultsThree-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.
conclusionsOur 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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