Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records

• Published in: Methods of information in medicine Vol. 61; no. S 02; pp. e125 - e133
• Main Authors: Patel, Jay Sureshbhai, Brandon, Ryan, Tellez, Marisol, Albandar, Jasim M., Rao, Rishi, Krois, Joachim, Wu, Huanmei
• Format: Journal Article
• Language: English
• Published: Rüdigerstraße 14, 70469 Stuttgart, Germany Georg Thieme Verlag KG 01.12.2022
• Subjects: Algorithms; Computers; Dental Records; Humans; Original Article; Periodontal Diseases - diagnosis; Phenotype; Retrospective Studies; phenotype; data quality; electronic dental record; periodontal disease; automated algorithms
• ISSN: 0026-1270; 2511-705X; 2511-705X
• DOI: 10.1055/s-0042-1757880

Abstract Objective  Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms. Methods  We conducted a retrospective study using EDR data of patients ( n  = 27,138) who received care at Temple University Maurice H. Kornberg School of Dentistry from January 1, 2017 to August 31, 2021. We determined the completeness of patient demographics, periodontal charting, and PD diagnoses information in the EDR. Next, we developed two automated computer algorithms to automatically diagnose patients' PD statuses from clinical notes and periodontal charting data. Last, we phenotyped PD diagnoses using automated computer algorithms and reported the improved completeness of diagnosis. Results  The completeness of PD diagnosis from the EDR was as follows: periodontal diagnosis codes 36% ( n  = 9,834), diagnoses in clinical notes 18% ( n  = 4,867), and charting information 80% ( n  = 21,710). After phenotyping, the completeness of PD diagnoses improved to 100%. Eleven percent of patients had healthy periodontium, 43% were with gingivitis, 3% with stage I, 36% with stage II, and 7% with stage III/IV periodontitis. Conclusions  We successfully developed, tested, and deployed two automated algorithms on big EDR datasets to improve the completeness of PD diagnoses. After phenotyping, EDR provided 100% completeness of PD diagnoses of 27,138 unique patients for research purposes. This approach is recommended for use in other large databases for the evaluation of their EDR data quality and for phenotyping PD diagnoses and other relevant variables.