Completeness of Electronic Dental Records in a Student Clinic: Retrospective Analysis

Abstract Background The Swedish Quality Registry for caries and periodontal disease (SKaPa) automatically collects data on caries and periodontitis from patients’ electronic dental records. Provided the data entries are reliable and accurate, the registry has potential value as a data source for registry-based research. The aim of this study was to evaluate the reliability and accuracy of the SKaPa registry information on dental caries in 6- and 12-year-old children. Method This diagnostic accuracy study compared dental caries data registered at an examination with dental health status registered in the patient’s electronic dental records, and with corresponding data retrieved from the SKaPa registry. Clinical examinations of 170 6- and 12-year-old children were undertaken by one of the researchers in conjunction with the children’s regular annual dental examinations where the number of teeth were registered, and dental caries was diagnosed using ICDAS II. Teeth with fillings were defined as filled and were added to the ICDAS II score and subsequently dft/DFT was calculated for each individual. Cohen’s Kappa, the intraclass correlation coefficient (ICC), and sensitivity and specificity were calculated to test the agreement of the ‘decayed and filled teeth’ in deciduous and permanent teeth (dft/DFT) from the three sources. Results Cohen’s Kappa of the dft/DFT-values was calculated to 0.79 between the researcher and the patient record, to 0.95 between patient dental record and SKaPa, and to 0.76 between the researcher and SKaPa. Intraclass correlation coefficient (ICC) was calculated to 0.96 between the researcher and the patient journal, to 0.99 between the patient dental record vs. SKaPa, and to 0.95 between the researcher and SKaPa. Conclusion The SKaPa registry information demonstrated satisfactory reliability and accuracy on dental caries in 6- and 12-year-old children and is a reliable source for registry-based research. Trial registration The study was registered in Clinical Trials (www.ClinicalTrials.gov, NCT03039010)

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A Survey of the Implementation and Usage of Electronic Dental Records in Private Dental Practices in Mississippi

Healthcare Review ◽

10.47285/hr.v1i1.18 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1-11

Author(s):

Barbara Brent ◽

Amy Sullivan ◽

Angelia Garner

Keyword(s):

Response Rate ◽

Advanced Technology ◽

Data Collection Process ◽

Study Results ◽

Dental Practitioners ◽

Dental Practices ◽

Research Questions ◽

Dental Records ◽

Dental Offices ◽

Electronic Dental Records

Implementation of electronic health records by the Health Information Technology for Economic and Clinical Health has led to the implementation of electronic dental records in dental offices. The study was conducted to determine the state of implementation and usage of electronic dental records by the private general and pediatric dental practices in Mississippi as well as reasons why the dental practices are not moving forward with the advanced technology. A survey consisting of six research questions was emailed via SurveyMonkey to 712 private general and pediatric dental practices in Mississippi with an invitation to participate in the study: 116 responded (16% response rate) and 104 consented to participate (89.66%). The data collection process transpired over a six-week period (September 18 – October 29, 2017). Results of the survey indicated dental practices in Mississippi using electronic dental records were 46.07%, electronic dental records with paper records were 42.70%, and only paper records were 11.24%. Dissemination of the study results among medical and dental practitioners may raise awareness and thus encourage more dentists to embrace EDRs. The response rate was affected by the number of dental practices that chose not to participate or did not open the survey email as well as the number of emails that were undeliverable. A second limitation was the lack of certainty of collecting all email addresses through the collection method. Third, there was no certainty that the person who responded knew the correct answers.

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Evaluation of students′ perception in using electronic dental records

Saudi Journal of Oral Sciences ◽

10.4103/1658-6816.150585 ◽

2015 ◽

Vol 2 (1) ◽

pp. 14 ◽

Cited By ~ 1

Author(s):

AmmarA Abu Mostafa ◽

FarisS Almasari ◽

SalahM Abduljabbar ◽

KhaledW Sadek ◽

RawadH Alshehri ◽

...

Keyword(s):

Dental Records ◽

Electronic Dental Records

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Using electronic dental records to establish a surveillance system for dental decay in rural Western Alaska

Journal of Public Health Dentistry ◽

10.1111/jphd.12435 ◽

2020 ◽

Author(s):

Timothy K Thomas ◽

Dane Lenaker ◽

Gretchen M Day ◽

Jennifer C Wilson ◽

Peter Holck ◽

...

Keyword(s):

Surveillance System ◽

Dental Decay ◽

Dental Records ◽

Electronic Dental Records

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Structuring electronic dental records through deep learning for a clinical decision support system

Health Informatics Journal ◽

10.1177/1460458220980036 ◽

2021 ◽

Vol 27 (1) ◽

pp. 146045822098003

Author(s):

Qingxiao Chen ◽

Xuesi Zhou ◽

Ji Wu ◽

Yongsheng Zhou

Keyword(s):

Deep Learning ◽

Decision Support ◽

Clinical Decision Support ◽

Language Processing ◽

Hybrid Method ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Selection Strategy ◽

Dental Records ◽

Electronic Dental Records

Extracting information from unstructured clinical text is a fundamental and challenging task in medical informatics. Our study aims to construct a natural language processing (NLP) workflow to extract information from Chinese electronic dental records (EDRs) for clinical decision support systems (CDSSs). We extracted attributes, attribute values, and tooth positions based on an existing ontology from EDRs. A workflow integrating deep learning with keywords was constructed, in which vectors representing texts were unsupervised learned. Specifically, we implemented Sentence2vec to learn sentence vectors and Word2vec to learn word vectors. For attribute recognition, we calculated similarity values among sentence vectors and extracted attributes based on our selection strategy. For attribute value recognition, we expanded the keyword database by calculating similarity values among word vectors to select keywords. Performance of our workflow with the hybrid method was evaluated and compared with keyword-based method and deep learning method. In both attribute and value recognition, the hybrid method outperforms the other two methods in achieving high precision (0.94, 0.94), recall (0.74, 0.82), and F score (0.83, 0.88). Our NLP workflow can efficiently structure narrative text from EDRs, providing accurate input information and a solid foundation for further data-based CDSSs.

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