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

Barriers and facilitators for the implementation of electronic dental record systems: Perspectives from a developing country

INTRODUCTION

The need for dental data in healthcare services and the inadequacy of paper records due to their inherent limitations have led to a shift towards electronic dental record systems (EDR). Implementing EDR comes with numerous barriers and challenges. Therefore, this research was conducted to identify the implementation barriers and facilitators for EDRs.

METHODS

This descriptive-analytical cross-sectional study was conducted on dentists working in public and private clinics in Tehran, Iran. A questionnaire consisting of three sections was designed to collect data on the demographic information of dentists, the barriers in five categories including financial barriers (6 questions), organizational barriers (11 questions), technical barriers (5 questions), personal barriers (3 questions), and ethical and legal barriers (6 questions), as well as facilitators for the implementation of EDR (15 questions) based on the literature, using a five-point Likert scale. 130 dentists from 60 dental clinics participated in the study. The data were analyzed using descriptive statistics (calculating frequency distribution, mean, and standard deviation). The mean scores were classified into four categories based on quartiles from very low importance to very high importance including very low importance (mean ≤ 1.25), low importance (1.25 ≥ mean < 2.5), important (2.5 ≥ mean < 3.75), and very high importance (mean ≥ 3.75). Finally, each of the barriers and facilitators among user dentists and non-user dentists was compared using the Mann-Whitney U test. The data were analyzed using SPSS software.

RESULTS

The findings indicate that dentists consider all barriers and challenges in implementing EDR to be important, and all the proposed facilitators for addressing these challenges to be very important. Among these important barriers are the rapid turnover of managers and policymakers at higher levels (3.69 out of 5) as a personal barrier, legal issues related to electronic records (3.65 out of 5) as an ethical-legal barrier, the lack of necessary standards for data exchange between different systems (3.64 out of 5) as a technical barrier, dentists' limited awareness of the benefits of this system (3.63 out of 5) as a personal barrier, and the lack of suitable legal infrastructure for EDR implementation (3.62 out of 5) as an ethical-legal barrier. Additionally, among the very important facilitators, training dentists and staff on EDR (4.31 out of 5) is noteworthy.

CONCLUSION

To address the important barriers to EDR implementation, including legal-ethical barriers, legal institutions, and regulators must establish relevant laws and regulations to overcome these obstacles. Furthermore, if system users learn about the features, goals, benefits, and positive impact of EDR on their work and gain the necessary awareness, their resistance to changes will decrease, and their interest and readiness to accept EDR will increase.

Natural language processing for clinical notes in dentistry: A systematic review

OBJECTIVE

To identify and synthesise research on applications of natural language processing (NLP) for information extraction and retrieval from clinical notes in dentistry.

MATERIALS AND METHODS

A predefined search strategy was applied in EMBASE, CINAHL and Medline. Studies eligible for inclusion were those that that described, evaluated, or applied NLP to clinical notes containing either human or simulated patient information. Quality of the study design and reporting was independently assessed based on a set of questions derived from relevant tools including CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). A narrative synthesis was conducted to present the results.

RESULTS

Of the 17 included studies, 10 developed and evaluated NLP methods and 7 described applications of NLP-based information retrieval methods in dental records. Studies were published between 2015 and 2021, most were missing key details needed for reproducibility, and there was no consistency in design or reporting. The 10 studies developing or evaluating NLP methods used document classification or entity extraction, and 4 compared NLP methods to non-NLP methods. The quality of reporting on NLP studies in dentistry has modestly improved over time.

CONCLUSIONS

Study design heterogeneity and incomplete reporting of studies currently limits our ability to synthesise NLP applications in dental records. Standardisation of reporting and improved connections between NLP methods and applied NLP in dentistry may improve how we can make use of clinical notes from dentistry in population health or decision support systems.

PROTOCOL REGISTRATION

PROSPERO CRD42021227823.

Use of medication data alone to identify diagnoses and related contraindications: application of algorithms to close a common documentation gap

AIMS

Automated checks for medication-related problems have become a cornerstone of medication safety. In many clinical settings medication checks remain confined to drug-drug interactions because only medication data are available in an adequately coded form, leaving possible contraindicated drug-disease combinations unaccounted for. Therefore, we devised algorithms that identify frequently contraindicated diagnoses based on medication patterns related to these diagnoses.

METHODS

We identified drugs that can identify diseases constituting common contraindications based on their exclusive use for these conditions (such as allopurinol for gout or salbutamol for bronchial obstruction). Expert-based and machine learning algorithms were developed to identify diagnoses based on highly specific medication patterns. The applicability, sensitivity and specificity of the approach were assessed by using an anonymized real-life sample of medication and diagnosis data excerpts from 3,506 discharge records of geriatric patients.

RESULTS

Depending on the algorithm, the desired focus (i.e. sensitivity vs. specificity) and the disease, we were able to identify the diagnoses gout, epilepsy, coronary artery disease, congestive heart failure and bronchial obstruction with a specificity of 44.0%-99.8% (95% CI 41.7%-100.0%) and a sensitivity of 3.8%-83.1% (95% CI 1.0%-86.1%). Using only medication data we were able to identify 123 (51.3%) of 240 contraindications identified by experts with access to medication data and diagnoses.

CONCLUSION

This study provides a proof of principle that some key diagnosis-related contraindications can be identified based on a patient's medication data alone, while others cannot be identified. This approach offers new opportunities to analyse drug-disease contraindications in community pharmacy or clinical routine data.

Dental Recordkeeping: Practice in Federation of Bosnia and Herzegovina

Background

Dental documentation which includes main information about a patient and dental treatment provided is a very important asset of each dental office.

Objective

This research aims to analyze the way of fulfilling and keeping mandatory dental records and periodic reporting forms by doctors of dental medicine in the Federation of Bosnia and Herzegovina (FB&H).

Methods

The study was observational with a cross-sectional design using a questionnaire as a study tool. The questionnaire was distributed electronically to the participants working in public health care facilities and private practice.

Results

A total of 426 Doctors of Dental Medicine (DDM) participated in the study, of whom 58.7% of respondents were employed in dental offices in the public health sector and 41.3% in dental offices in the private health sector. Dental records are filled out only manually by 53.5% of respondents, while 9.4% fill out the records only electronically, while 37.1% of respondents fill out records both manually and electronically. The manner of keeping dental documentation between respondents employed in dental offices in the public health sector and dental offices in the private health sector differs significantly (p<0.05). Almost all respondents understand the purpose and significance of keeping dental records.

Conclusion

This paper points out that good dental records are of great importance as they allow monitoring the quality of services provided to patients for a longer period.

Leveraging Electronic Dental Record Data for Clinical Research in the National Dental PBRN Practices

OBJECTIVES

The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR).

METHODS

Ninety-nine network general dentistry practices that used Dentrix or EagleSoft EDR shared de-identified data of patients who received PCR and/or RCT on permanent teeth through October 31, 2015. We evaluated the data completeness and correctness, summarized practice, and patient characteristics and summarized the two treatments by tooth type and arch location.

RESULTS

Eighty-two percent of practitioners were male, with a mean age of 49 and 22.4 years of clinical experience. The final dataset comprised 217,887 patients and 11,289,594 observations, with the observation period ranging from 0 to 37 years. Most patients (73%) were 18 to 64 years old; 56% were female. The data were nearly 100% complete. Eight percent of observations had incorrect data, such as incorrect tooth number or surface, primary teeth, supernumerary teeth, and tooth ranges, indicating multitooth procedures instead of PCR or RCT. Seventy-three percent of patients had dental insurance information; 27% lacked any insurance information. While gender was documented for all patients, race/ethnicity was missing in the dataset.

CONCLUSION

This study established the feasibility of using EDR data integrated from multiple distinct solo and small-group network practices for longitudinal studies to assess treatment outcomes. The results laid the groundwork for a learning health system that enables practitioners to learn about their patients' outcomes by using data from their own practice.

Using big data to promote precision oral health in the context of a learning healthcare system

There has been a call for evidence-based oral healthcare guidelines, to improve precision dentistry and oral healthcare delivery. The main challenges to this goal are the current lack of up-to-date evidence, the limited integrative analytical data sets, and the slow translations to routine care delivery. Overcoming these issues requires knowledge discovery pipelines based on big data and health analytics, intelligent integrative informatics approaches, and learning health systems. This article examines how this can be accomplished by utilizing big data. These data can be gathered from four major streams: patients, clinical data, biological data, and normative data sets. All these must then be uniformly combined for analysis and modelling and the meaningful findings can be implemented clinically. By executing data capture cycles and integrating the subsequent findings, practitioners are able to improve public oral health and care delivery.

Implant Failure Prediction Using Discriminant Analysis

Electronic dental records (EDR) provide access to a vast repository of clinical information which may be used for analyzing dental care delivery. The goal of this study was identification of determinants of implant survival and development of implant failure prediction model using large data set of intact and failed implant parameters extracted from EDR. A retrospective analysis of 19 variables reflecting patient, surgeon and dental treatment characteristics of 800 dental implants was performed using discriminant analysis to develop a predictive model identifying potential implant failure based on characteristics routinely available in a clinical care setting. The intact and failed implant characteristics were compared using the Goodman and Kruskal's lambda test, the point-biserial test, the chi-square test, and ANOVA test. A stepwise discriminant analysis reduced model dimensionality from 19 to 4 features. The final discriminant analysis model included the following variables: non-temporary implant, pre-op antibiotics, immunocompromised status, and gender. Overall, 72% of implant failure cases and 62% of intact implants were correctly identified by the resulting discriminant function. As the final predictive feature set is readily available in EDR, the resulting algorithm may be implemented as a clinical decision support module embedded into EDR to promote personalized approach in dental implant patients.