Patel, J.S.; Kumar, K.; Zai, A.; Shin, D.; Willis, L.; Thyvalikakath, T.P. Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records. Diagnostics2023, 13, 1028.
Patel, J.S.; Kumar, K.; Zai, A.; Shin, D.; Willis, L.; Thyvalikakath, T.P. Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records. Diagnostics 2023, 13, 1028.
Patel, J.S.; Kumar, K.; Zai, A.; Shin, D.; Willis, L.; Thyvalikakath, T.P. Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records. Diagnostics2023, 13, 1028.
Patel, J.S.; Kumar, K.; Zai, A.; Shin, D.; Willis, L.; Thyvalikakath, T.P. Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records. Diagnostics 2023, 13, 1028.
Abstract
Objective: Determine the feasibility of utilizing longitudinal electronic dental record (EDR) data to track change over time in patient periodontal disease (PD) and to generate three patient cohorts: 1) patients whose disease did not change over time, 2) patients whose PD progressed, and 3) patients whose disease improved over time using informatics approaches. Methods: We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between January 1, 2009, and December 31, 2014, at the Indiana University School of Dentistry (IUSD) clinics. We developed and tested three automated computer applications to: 1) diagnose periodontitis cases from periodontal charting, 2) retrieve clinician-documented diagnoses from clinical notes, and 3) track disease change over time. We also evaluated the density of longi-tudinal EDR data for the following follow-up times: 1) none, 2) up to 5 years, 3) >5 and <=10 years, and 4) >10 and <=15 years Results: Thirty-four percent (n=9,954) of the study cohort had up to five years of follow-up visits with an average of 2.78 visits with periodontal charting information. An average of three patient visits per year that contained periodontal charts (63,552) were utilized to obtain a diagnosis, which is considered excellent. For clinician-documented diagnoses from clinical notes, 42% of patients (n=5,562) had at least two PD diagnoses to determine their disease change. In this cohort with cli-nician-documented diagnoses, 72% percent of patients (n=3,919) did not have a disease status change between their first and last visits, 669 (13%) patients' disease status progressed, and 589 (11%) patients’ disease improved. Conclusions: This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We found excellent longitudinal data when diagnoses generated from periodontal charting were considered (three visits per pa-tient). This information can be now utilized for studying the clinical course of periodontitis.
Keywords
Periodontal Disease; Electronic Dental Record; Longitudinal Data; Data Quality; Dental Infor-matics; Clinical Course of Periodontal Disease; Periodontal Cohort Generation
Subject
Medicine and Pharmacology, Dentistry and Oral Surgery
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
