Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data.
clinical decision support
diagnostic classification
periodontitis
Journal
Journal of clinical periodontology
ISSN: 1600-051X
Titre abrégé: J Clin Periodontol
Pays: United States
ID NLM: 0425123
Informations de publication
Date de publication:
11 Jan 2024
11 Jan 2024
Historique:
revised:
14
11
2023
received:
29
11
2022
accepted:
07
12
2023
medline:
12
1
2024
pubmed:
12
1
2024
entrez:
11
1
2024
Statut:
aheadofprint
Résumé
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. 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. 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. 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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Agency for Healthcare Research and Quality
ID : R01HS027938
Informations de copyright
© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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