1
|
Patel JS, Shin D, Willis L, Zai A, Kumar K, Thyvalikakath TP. Comparing gingivitis diagnoses by bleeding on probing (BOP) exclusively versus BOP combined with visual signs using large electronic dental records. Sci Rep 2023; 13:17065. [PMID: 37816902 PMCID: PMC10564949 DOI: 10.1038/s41598-023-44307-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 10/06/2023] [Indexed: 10/12/2023] Open
Abstract
The major significance of the 2018 gingivitis classification criteria is utilizing a simple, objective, and reliable clinical sign, bleeding on probing score (BOP%), to diagnose gingivitis. However, studies report variations in gingivitis diagnoses with the potential to under- or over-estimating disease occurrence. This study determined the agreement between gingivitis diagnoses generated using the 2018 criteria (BOP%) versus diagnoses using BOP% and other gingival visual assessments. We conducted a retrospective study of 28,908 patients' electronic dental records (EDR) from January-2009 to December-2014, at the Indiana University School of Dentistry. Computational and natural language processing (NLP) approaches were developed to diagnose gingivitis cases from BOP% and retrieve diagnoses from clinical notes. Subsequently, we determined the agreement between BOP%-generated diagnoses and clinician-recorded diagnoses. A thirty-four percent agreement was present between BOP%-generated diagnoses and clinician-recorded diagnoses for disease status (no gingivitis/gingivitis) and a 9% agreement for the disease extent (localized/generalized gingivitis). The computational program and NLP performed excellently with 99.5% and 98% f-1 measures, respectively. Sixty-six percent of patients diagnosed with gingivitis were reclassified as having healthy gingiva based on the 2018 diagnostic classification. The results indicate potential challenges with clinicians adopting the new diagnostic criterion as they transition to using the BOP% alone and not considering the visual signs of inflammation. Periodic training and calibration could facilitate clinicians' and researchers' adoption of the 2018 diagnostic system. The informatics approaches developed could be utilized to automate diagnostic findings from EDR charting and clinical notes.
Collapse
Affiliation(s)
- Jay S Patel
- Division of Dental Informatics, Department of Dental Public Health and Dental Informatics, Indiana University School of Dentistry (IUSD), Indianapolis, IN, USA.
- Department of Health Services Administration and Policy, College of Public Health, Temple University, Philadelphia, PA, USA.
- Bio-Health Informatics, Indiana University School of Informatics and Computing, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.
| | - Daniel Shin
- Department of Periodontology, IUSD, Indianapolis, IN, USA
| | - Lisa Willis
- Division of Dental Informatics, Department of Dental Public Health and Dental Informatics, Indiana University School of Dentistry (IUSD), Indianapolis, IN, USA
| | - Ahad Zai
- Division of Dental Informatics, Department of Dental Public Health and Dental Informatics, Indiana University School of Dentistry (IUSD), Indianapolis, IN, USA
| | - Krishna Kumar
- Division of Dental Informatics, Department of Dental Public Health and Dental Informatics, Indiana University School of Dentistry (IUSD), Indianapolis, IN, USA
| | - Thankam P Thyvalikakath
- Division of Dental Informatics, Department of Dental Public Health and Dental Informatics, Indiana University School of Dentistry (IUSD), Indianapolis, IN, USA.
- Bio-Health Informatics, Indiana University School of Informatics and Computing, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, USA.
| |
Collapse
|
2
|
Patel JS, Kumar K, Zai A, Shin D, Willis L, Thyvalikakath TP. Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records. Diagnostics (Basel) 2023; 13:diagnostics13061028. [PMID: 36980336 PMCID: PMC10047444 DOI: 10.3390/diagnostics13061028] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/11/2023] [Accepted: 02/27/2023] [Indexed: 03/30/2023] Open
Abstract
OBJECTIVE To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). METHODS We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal 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 = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) 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 provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.
Collapse
Affiliation(s)
- Jay S Patel
- Dental Informatics, Department of Cariology Operative Dentistry and Dental Public Health, Indiana Univesity School of Dentistry, Indianapolis, IN 46202, USA
- Health Informatics, Department of Health Services Administrations and Policy, Temple University College of Public Health, Philadelphia, PA 19122, USA
- Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, PA 19140, USA
| | - Krishna Kumar
- Dental Informatics, Department of Cariology Operative Dentistry and Dental Public Health, Indiana Univesity School of Dentistry, Indianapolis, IN 46202, USA
| | - Ahad Zai
- Dental Informatics, Department of Cariology Operative Dentistry and Dental Public Health, Indiana Univesity School of Dentistry, Indianapolis, IN 46202, USA
- Dental Informatics Program, Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46202, USA
| | - Daniel Shin
- Dental Informatics, Department of Cariology Operative Dentistry and Dental Public Health, Indiana Univesity School of Dentistry, Indianapolis, IN 46202, USA
| | - Lisa Willis
- Dental Informatics, Department of Cariology Operative Dentistry and Dental Public Health, Indiana Univesity School of Dentistry, Indianapolis, IN 46202, USA
| | - Thankam P Thyvalikakath
- Dental Informatics, Department of Cariology Operative Dentistry and Dental Public Health, Indiana Univesity School of Dentistry, Indianapolis, IN 46202, USA
- Dental Informatics Program, Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46202, USA
| |
Collapse
|
3
|
Kallio J, Heikkinen AM, Lehtovuori T, Raina M, Suominen L, Kauppila T. Comparing the effectiveness of competition as a method of reminding primary oral health care dentists to record diagnoses with two alternative methods used to enhance the recording of diagnoses in primary health care. Int J Circumpolar Health 2022; 81:2125067. [PMID: 36131386 PMCID: PMC9518279 DOI: 10.1080/22423982.2022.2125067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
The purpose of this study was to investigate whether competition is an effective method to remind primary oral health care dentists to record diagnoses (RRD). The effectiveness of competition was examined in comparison with financial group bonuses (FGBs) and electronic reminders (ERs) of the electronic health record, together with superior-subordinate or development discussions. Putative differences in the diagnosis recording cultures of Finnish public health care physicians and dentists were studied. This was a retrospective quasi-experimental observational study in which the effects of the interventions on the rate of recording diagnoses were identified using a general linear regression model and proportions of visits with recorded diagnoses. The rate of increase in the recording of diagnoses in dentists was 0.995 ± 0.273%/month (mean ± SEM) after the implementation of RRDs and this did not differ from that obtained after starting FGBs (0.919 ± 0.130%/month) or ERs with superior-subordinate or development discussions (1.562 ± 0.277%/month) in physicians. As the rates of increase did not differ none of the applied methods seemed to be more effective than the others when trying to influence the behaviour of primary health care clinicians. Altogether, public primary health care physicians were more active than respective primary oral health care dentists to record diagnoses.
Collapse
Affiliation(s)
- Jouko Kallio
- Department of Public Health, University of Helsinki, Helsinki, Finland.,Social and health bureau, Espoo, Finland
| | - Anna Maria Heikkinen
- Department of Oral and Maxillofacial Diseases, Head and Neck Center, University of Helsinki, Helsinki, Finland.,Department of Oral and Maxillofacial Diseases, Helsingin yliopisto, Tampere, Finland
| | | | | | | | - Timo Kauppila
- Department of Public Health, University of Helsinki, Helsinki, Finland
| |
Collapse
|
4
|
Patel JS, Brandon R, Tellez M, Albandar JM, Rao R, Krois J, Wu H. Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records. Methods Inf Med 2022; 61:e125-e133. [PMID: 36413995 PMCID: PMC9788909 DOI: 10.1055/s-0042-1757880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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.
Collapse
Affiliation(s)
- Jay Sureshbhai Patel
- Health Informatics, Department of Health Services Administrations and Policy, Temple University College of Public Health, Philadelphia, Pennsylvania, United States,Address for correspondence Jay Patel, BDS, MS, PhD Department of Health Services Administration and Policy, Temple University, College of Public Health, Temple University School of DentistryRitter Annex, 1301 Cecil B. Moore Ave. Rm 534, Philadelphia, PA 19122United States
| | - Ryan Brandon
- Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, Pennsylvania, United States
| | - Marisol Tellez
- Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, Pennsylvania, United States
| | - Jasim M. Albandar
- Department of Periodontology and Oral Implantology, Temple University Kornberg School of Dentistry, Philadelphia, Pennsylvania, United States
| | - Rishi Rao
- Health Informatics, Department of Health Services Administrations and Policy, Temple University College of Public Health, Philadelphia, Pennsylvania, United States
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research Charité – Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Huanmei Wu
- Health Informatics, Department of Health Services Administrations and Policy, Temple University College of Public Health, Philadelphia, Pennsylvania, United States
| |
Collapse
|
5
|
Tokede B, Yansane A, White J, Bangar S, Mullins J, Brandon R, Gantela S, Kookal K, Rindal D, Lee CT, Lin GH, Spallek H, Kalenderian E, Walji M. Translating periodontal data to knowledge in a learning health system. J Am Dent Assoc 2022; 153:996-1004. [PMID: 35970673 PMCID: PMC9830777 DOI: 10.1016/j.adaj.2022.06.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/07/2022] [Accepted: 06/14/2022] [Indexed: 01/12/2023]
Abstract
BACKGROUND A learning health system (LHS) is a health system in which patients and clinicians work together to choose care on the basis of best evidence and to drive discovery as a natural outgrowth of every clinical encounter to ensure the right care at the right time. An LHS for dentistry is now feasible, as an increased number of oral health care encounters are captured in electronic health records (EHRs). METHODS The authors used EHRs data to track periodontal health outcomes at 3 large dental institutions. The 2 outcomes of interest were a new periodontitis case (for patients who had not received a diagnosis of periodontitis previously) and tooth loss due to progression of periodontal disease. RESULTS The authors assessed a total of 494,272 examinations (new periodontitis outcome: n = 168,442; new tooth loss outcome: n = 325,830), representing a total of 194,984 patients. Dynamic dashboards displaying performance on both measures over time allow users to compare demographic and risk factors for patients. The incidence of new periodontitis and tooth loss was 4.3% and 1.2%, respectively. CONCLUSIONS Periodontal disease, diagnosis, prevention, and treatment are particularly well suited for an LHS model. The results showed the feasibility of automated extraction and interpretation of critical data elements from the EHRs. The 2 outcome measures are being implemented as part of a dental LHS. The authors are using this knowledge to target the main drivers of poorer periodontal outcomes in a specific patient population, and they continue to use clinical health data for the purpose of learning and improvement. PRACTICAL IMPLICATIONS Dental institutions of any size can conduct contemporaneous self-evaluation and immediately implement targeted strategies to improve oral health outcomes.
Collapse
Affiliation(s)
- Bunmi Tokede
- Department of Diagnostic and Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX
| | - Alfa Yansane
- Preventative and Restorative Dental Sciences, School of Dentistry, University of California, San Francisco, San Francisco, CA
| | - Joel White
- Preventative and Restorative Dental Sciences, School of Dentistry, University of California, San Francisco, San Francisco, CA
| | - Suhasini Bangar
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX
| | | | - Ryan Brandon
- Willamette Dental Group and Skourtes Institute, Hillsboro, OR
| | - Swaroop Gantela
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX
| | - Krishna Kookal
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX
| | - Donald Rindal
- HealthPartners Institute, Minneapolis, MN, and an associate dental director for research, HealthPartners Dental Group, Minneapolis, MN
| | - Chun-Teh Lee
- Department of Periodontics and Dental Hygiene, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX
| | - Guo-Hao Lin
- School of Dentistry, University of California, San Francisco, CA
| | - Heiko Spallek
- The University of Sydney, Sydney, New South Wales, Australia
| | - Elsbeth Kalenderian
- professor, Department of Preventive and Restorative Dental Sciences, School of Dentistry, University of California, San Francisco, San Francisco, CA; a professor, Academic Centre for Dentistry, Amsterdam, The Netherlands; senior lecturer, Harvard School of Dental Medicine, Boston, MA; and an Extraordinary Professor, University of Pretoria School of Dentistry, Pretoria, South Africa
| | - Muhammad Walji
- Diagnostic and Biomedical Sciences Department, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX
| |
Collapse
|