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CC BY-NC-ND 4.0 · Methods Inf Med 2022; 61(S 02): e125-e133
DOI: 10.1055/s-0042-1757880
Original Article

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

Jay Sureshbhai Patel
1   Health Informatics, Department of Health Services Administrations and Policy, Temple University College of Public Health, Philadelphia, Pennsylvania, United States
,
Ryan Brandon
2   Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, Pennsylvania, United States
,
Marisol Tellez
2   Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, Pennsylvania, United States
,
Jasim M. Albandar
3   Department of Periodontology and Oral Implantology, Temple University Kornberg School of Dentistry, Philadelphia, Pennsylvania, United States
,
Rishi Rao
1   Health Informatics, Department of Health Services Administrations and Policy, Temple University College of Public Health, Philadelphia, Pennsylvania, United States
,
Joachim Krois
4   Department of Oral Diagnostics, Digital Health and Health Services Research Charité – Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
,
Huanmei Wu
1   Health Informatics, Department of Health Services Administrations and Policy, Temple University College of Public Health, Philadelphia, Pennsylvania, United States
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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.

Keywords

periodontal disease - data quality - automated algorithms - electronic dental record - phenotype

Protection of Human Subjects and Animals in Research

This study was reviewed and approved by our institutional review board (IRB: 28321) granted authorization. In this retrospective study, we used deidentified patient datasets from the patients' EDR records; therefore, informed consents were not required to obtain.




Publikationsverlauf

Eingereicht: 26. Februar 2022

Angenommen: 30. August 2022

Artikel online veröffentlicht:
22. November 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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