Clinical Decision Support Systems: Perspectives in Dentistry

Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as 'presence of caries' and 13,928 surfaces are determined as 'absence of caries' for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.

Machine learning techniques for periodontitis and dental caries detection: A narrative review

OBJECTIVES

In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing.

METHODS

An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect.

RESULTS

The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture.

CONCLUSION

While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction.

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.

Do clinical decision support tools have a role to play in dealing with paediatric dental trauma?

Design Randomised controlled trial.Case selection In order to assess baseline knowledge of dental trauma, paediatric dentists and medical students were administered a pre-test, subsequent to which a random assignment was carried out to one of three learning groups: mobile app clinical decision support tools (CDST), print CDST and no CDST, for the purposes of a post-test.Data analysis The correct answers for the pre-test and post-test, time to completion and answers to the demographic survey were subjected to descriptive statistical analyses using IBM SPSS v25.0 for Windows (SPSS, IBM Corp, Armonk, NY). In order to compare the pre- and post-tests for each group, a paired-sample t test was conducted. An independent-sample t test and Pearson χ2 test were utilised to assess for significant differences between paediatric dentists and medical students. Least significant post-hoc and one-way analysis of variance (ANOVA) tests were conducted among the three groups. The level of significance was set at p <0.05.Results Paediatric dentists obtained significantly higher scores on both the pre- and post-tests when compared to medical students (8.57 ± 0.96 vs 4.20 ± 1.58; p <0.001 and 8.37 ± 1.09 vs 4.96 ± 1.99; p <0.001, respectively).No significant difference was noted pertaining to the time taken to complete the tests among both the groups. In both the groups, the highest scores were obtained by those who used the mobile app version of the CDST; these subjects, however, recorded the longest time to complete the post-test (p <0.001).Conclusions When compared to the absence of an aid, both the mobile app and print versions of the CDST improved the diagnosis and management of injuries to the primary dentition. With medical students demonstrating significant improvement in primary dental trauma management with CDST usage, these tools are recommended to enhance the diagnosis and treatment for such patients.

Current state of dental informatics in the field of health information systems: a scoping review

Background

Over the past 50 years, dental informatics has developed significantly in the field of health information systems. Accordingly, several studies have been conducted on standardized clinical coding systems, data capture, and clinical data reuse in dentistry.

Methods

Based on the definition of health information systems, the literature search was divided into three specific sub-searches: “standardized clinical coding systems,” “data capture,” and “reuse of routine patient care data.” PubMed and Web of Science were searched for peer-reviewed articles. The review was conducted following the PRISMA-ScR protocol.

Results

A total of 44 articles were identified for inclusion in the review. Of these, 15 were related to “standardized clinical coding systems,” 15 to “data capture,” and 14 to “reuse of routine patient care data.” Articles related to standardized clinical coding systems focused on the design and/or development of proposed systems, on their evaluation and validation, on their adoption in academic settings, and on user perception. Articles related to data capture addressed the issue of data completeness, evaluated user interfaces and workflow integration, and proposed technical solutions. Finally, articles related to reuse of routine patient care data focused on clinical decision support systems centered on patient care, institutional or population-based health monitoring support systems, and clinical research.

Conclusions

While the development of health information systems, and especially standardized clinical coding systems, has led to significant progress in research and quality measures, most reviewed articles were published in the US. Clinical decision support systems that reuse EDR data have been little studied. Likewise, few studies have examined the working environment of dental practitioners or the pedagogical value of using health information systems in dentistry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02163-9.

Comparison of Different Clinical Decision Support Tools in Aiding Dental and Medical Professionals in Managing Primary Dentition Traumatic Injuries

BACKGROUND

Many patients are taken to the emergency room for dental trauma treatment, but studies reveal that medical professionals do not feel confident in diagnosing and treating children with traumatic dental injuries. The purpose of this study was to determine if a clinical decision support tool (CDST) would improve dental trauma knowledge of primary teeth in medical students and pediatric dentists. Another purpose was assessing effectiveness of print and mobile app CDSTs.

METHODS

Medical students (n = 100) and pediatric dentists (n = 49) were given a pretest to assess baseline dental trauma knowledge. All subjects were randomly assigned to 1 of 3 groups for the posttest: no CDST, print CDST, and mobile app CDST. Test scores and total time spent on each test were recorded and analyzed.

RESULTS

Compared with medical students, pediatric dentists scored significantly higher in both pretest (8.57 ± 0.96 vs 4.20 ± 1.58; P < 0.001) and posttest (8.37 ± 1.09 vs 4.96 ± 1.99; P < 0.001). There was no significant difference in time spent to complete the 2 tests between both groups. Medical students and pediatric dentists who utilized the mobile app CDST had scored highest (P = 0.028) but took the longest time (P < 0.001) on the posttest.

CONCLUSIONS

Both print and mobile app CDSTs improved diagnosing and managing traumatic dental injuries in primary dentition significantly compared with those without aid. Medical students with CDSTs showed significant improvement in managing primary dental trauma; therefore, it is recommended for better, more accurate diagnosis and treatment in patients.

Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.

Present and future of artificial intelligence in dentistry

The last decennary has marked as the breakthrough in the advancement of technology with evolution of artificial intelligence, which is rapidly gaining the attention of researchers across the globe. Every field opted artificial intelligence with huge enthusiasm and so the field of dental science is no exception. With huge increases in patient documented information and data this is the need of the hour to use intelligent software to compile and save this data. From the basic step of taking a patient's history to data processing and then to extract the information from the data for diagnosis, artificial intelligence has many applications in dental and medical science. While in no case artificial intelligence can replace the role of a dental surgeon but it is important to be acquainted with the scope to amalgamate this advancement of technology in future for betterment of dental practice.

Developments, application, and performance of artificial intelligence in dentistry - A systematic review

Background/purpose

Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment.

Materials and methods

The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000–March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2.

Results

AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination.

Conclusion

These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.

Automatic drawing of customized removable partial denture diagrams based on textual design for the clinical decision support system

Qualified diagrams of removable partial denture (RPD) designs created by dentists provide technicians with clear and dynamic information. Generating RPD design in the clinical decision support system (CDSS) can be achieved by producing the RPD design in a textual format and then transferring the design onto diagrams. The drawing of RPD diagrams automatically and efficiently for the given textual designs is still under investigation. A new workflow consisting of three major steps is developed to produce and visualize two-dimensional RPD design diagrams. Annotations and orientations of teeth are established from the base maps in the first step, and built-in rules are then incorporated to describe the variations caused by the interactions of the RPD components. Finally, the software draws each component using a series of curve functions. To validate the performance of the software, 112 RPD clinical design plans are randomly selected as inputs for the software, and the outputs are independently verified by experienced clinicians. The proposed methods are proven to be efficient and accurate and thus can be used to improve clinical quality.

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.

Dental caries diagnosis in digital radiographs using back-propagation neural network

Purpose

An algorithm for diagnostic system with neural network is developed for diagnosis of dental caries in digital radiographs. The diagnostic performance of the designed system is evaluated.

Methods

The diagnostic system comprises of Laplacian filtering, window based adaptive threshold, morphological operations, statistical feature extraction and back-propagation neural network. The back propagation neural network used to classify a tooth surface as normal or having dental caries. The 105 images derived from intra-oral digital radiography, are used to train an artificial neural network with 10-fold cross validation. The caries in these dental radiographs are annotated by a dentist. The performance of the diagnostic algorithm is evaluated and compared with baseline methods.

Results

The system gives an accuracy of 97.1%, false positive (FP) rate of 2.8%, receiver operating characteristic (ROC) area of 0.987 and precision recall curve (PRC) area of 0.987 with learning rate of 0.4, momentum of 0.2 and 500 iterations with single hidden layer with 9 nodes.

Conclusions

This study suggests that dental caries can be predicted more accurately with back-propagation neural network. There is a need for improving the system for classification of caries depth. More improved algorithms and high quantity and high quality datasets may give still better tooth decay detection in clinical dental practice.

Developing a new intelligent system for the diagnosis of oral medicine with case-based reasoning approach

OBJECTIVE

Since the clinical manifestations of many oral diseases can be quite similar despite the wide variety in etiology and pathology, the differential diagnosis of oral diseases is a complex and challenging process. Intelligent system for differential diagnosis of oral medicine using the artificial intelligence (AI) capabilities helps specialists in achieving differential diagnosis in a wide range of oral diseases.

MATERIALS AND METHODS

First, the essential data elements to design and develop an intelligent system were identified in a cross-sectional descriptive study. The case-based reasoning method was selected to design and implement the system, which consists of three stages: collect the clinical data, construct the cases database, and case-based reasoning cycle. The problem is solved by CBR method in a cycle consisting of four main stages of retrieval, reuse, review, and retention. The evaluation process was conducted in a pilot-based way through the evaluation of the system's performance in the clinical setting and also using the usability assessment questionnaire.

RESULTS

The output of the present project is a web-based intelligent information system, which is developed using the Visual Studio 2015 software. The database of this system is the Microsoft SQL Server version 2012, which has been programmed based on Net framework (version 4.5 or higher) using Visual Basic language. The results of the system evaluation by specialists in clinical settings showed that the system's diagnosis power in different aspects of the disease is influenced by their prevalence and incidence.

CONCLUSIONS

System development using the artificial intelligence capabilities and through the clinical data analysis has potential to help specialists to determine the best diagnostic strategy to achieve a differential diagnosis of a wide range of oral diseases. The results of evaluation present the potential of the system to improve the quality and efficiency of patient care.

Evidence-based dentistry for planning restorative treatments: barriers and potential solutions

INTRODUCTION

Evidence-based dentistry (EBD) can help provide the best treatment option for every patient, however, its implementation in restorative dentistry is very limited.

OBJECTIVE

This study aimed at assessing the barriers preventing the implementation of EBD among dental undergraduate and graduate students in Montreal, and explore possible solutions to overcome these barriers.

MATERIALS AND METHODS

A cross-sectional survey was conducted by means of a paper format self-administrated questionnaire distributed among dental students. The survey assessed the barriers and potential solutions for implementation of an evidence-based practice.

RESULTS

Sixty-one students completed the questionnaire. Forty-one percent of respondents found evidence-based literature to be the most reliable source of information for restorative treatment planning, however, only 16% used it. They considered that finding reliable information was difficult and they sometimes encountered conflicting information when consulting different sources. Dental students had positive attitudes towards the need for better access to evidence-based literature to assist learning and decision making in restorative treatment planning and to improve treatment outcomes. Even for dentists trained in EBD, online searching takes too much time, and even though it can provide information of better quality than personal intuition, it might not be enough to identify the best available evidence.

CONCLUSIONS

Even though dental students are aware of the importance of EBD in restorative dentistry they rarely apply the concept, mainly due to time constraints. For this reason, implementation of EBD would probably require faster access to evidence-based knowledge.

An ontology-driven, case-based clinical decision support model for removable partial denture design

We present the initial work toward developing a clinical decision support model for specific design of removable partial dentures (RPDs) in dentistry. We developed an ontological paradigm to represent knowledge of a patient’s oral conditions and denture component parts. During the case-based reasoning process, a cosine similarity algorithm was applied to calculate similarity values between input patients and standard ontology cases. A group of designs from the most similar cases were output as the final results. To evaluate this model, the output designs of RPDs for 104 randomly selected patients were compared with those selected by professionals. An area under the curve of the receiver operating characteristic (AUC-ROC) was created by plotting true-positive rates against the false-positive rate at various threshold settings. The precision at position 5 of the retrieved cases was 0.67 and at the top of the curve it was 0.96, both of which are very high. The mean average of precision (MAP) was 0.61 and the normalized discounted cumulative gain (NDCG) was 0.74 both of which confirmed the efficient performance of our model. All the metrics demonstrated the efficiency of our model. This methodology merits further research development to match clinical applications for designing RPDs. This paper is organized as follows. After the introduction and description of the basis for the paper, the evaluation and results are presented in Section 2. Section 3 provides a discussion of the methodology and results. Section 4 describes the details of the ontology, similarity algorithm, and application.

Toward Implementing Primary Care at Chairside: Developing a Clinical Decision Support System for Dental Hygienists

INTRODUCTION

The goal of this project was to use the Consolidated Framework for Implementation Research (CFIR) as the theoretical foundation for developing a web-based clinical decision support system (CDSS) for primary care screening and care coordination by dental hygienists at chairside.

METHODS

First, we appraised New York State education and scope of practice requirements for dental hygienists with input from health experts who constituted a Senior Advisory Board for the project, and reviewed current professional guidelines and best practices for tobacco use, hypertension and diabetes screening, and nutrition counseling at chairside. Second, we created algorithms for these four health issues (tobacco, hypertension, diabetes, and nutrition) using evidence-based guidelines endorsed by authoritative professional bodies. Third, an information technology specialist incorporated the algorithms into a tool using an iterative process to refine the CDSS, with input from dental hygienists, dentists, Senior Advisory Board members and research staff.

RESULTS

An evidence-based CDSS for use by dental hygienists at chairside for tobacco use, hypertension and diabetes screening, and nutrition counseling was developed with the active participation of the individuals involved in the implementation process.

CONCLUSIONS

CDSS technology may potentially be leveraged to enhance primary care screening and coordination by dental hygienists at chairside, leading to improved patient care. Using the CFIR as a pragmatic structure for implementing this intervention across multiple settings, the developed CDSS is available for downloading and adaptation to diverse dental settings and other primary care sensitive conditions.

Health-oriented electronic oral health record: development and evaluation

This study aims to develop and evaluate a new Health-oriented Electronic Oral Health Record that implements the health-oriented status and intervention index. The index takes the principles of holistic oral healthcare and applies them to the design and implementation of the Health-oriented Electronic Oral Health Record. We designed an experiment using focus groups and a consensus (Delphi process) method to develop a new health-oriented status and intervention index and graphical user interface. A comparative intervention study with qualitative and quantitative methods was used to compare an existing Electronic Oral Health Record to the Health-oriented Electronic Oral Health Record, focusing on dentist satisfaction, accuracy, and completeness of oral health status recording. The study was conducted by the dental staff of the Inter-country Center for Oral Health collaborative hospitals in Thailand. Overall, the user satisfaction questionnaire had a positive response to the Health-oriented Electronic Oral Health Record. The dentists found it easy to use and were generally satisfied with the impact on their work, oral health services, and surveillance. The dentists were significantly satisfied with the Health-oriented Electronic Oral Health Record compared to the existing Electronic Oral Health Record (p < 0.001). The accuracy and completeness values of the oral health information recorded using the Health-oriented Electronic Oral Health Record were 97.15 and 93.74 percent, respectively. This research concludes that the Health-oriented Electronic Oral Health Record satisfied many dentists, provided benefits to holistic oral healthcare, and facilitated the planning, managing, and evaluation of the healthcare delivery system.

Expanding the foundation for personalized medicine: implications and challenges for dentistry

Personalized medicine aims to individualize care based on a person's unique genetic, environmental, and clinical profile. Dentists and physicians have long recognized variations between and among patients, and have customized care based on each individual's health history, environment, and behavior. However, the sequencing of the human genome in 2003 and breakthroughs in regenerative medicine, imaging, and computer science redefined "personalized medicine" as clinical care that takes advantage of new molecular tools to facilitate highly precise health care based on an individual's unique genomic and molecular characteristics. Major investments in science bring a new urgency toward realizing the promise of personalized medicine; yet, many challenges stand in the way. In this article, we present an overview of the opportunities and challenges that influence the oral health community's full participation in personalized medicine. We highlight selected research advances that are solidifying the foundation of personalized oral health care, elaborate on their impact on dentistry, and explore obstacles toward their adoption into practice. It is our view that now is the time for oral health professionals, educators, students, researchers, and patients to engage fully in preparations for the arrival of personalized medicine as a means to provide quality, customized, and effective oral health care for all.

How information systems should support the information needs of general dentists in clinical settings: suggestions from a qualitative study

BACKGROUND

A major challenge in designing useful clinical information systems in dentistry is to incorporate clinical evidence based on dentists' information needs and then integrate the system seamlessly into the complex clinical workflow. However, little is known about the actual information needs of dentists during treatment sessions. The purpose of this study is to identify general dentists' information needs and the information sources they use to meet those needs in clinical settings so as to inform the design of dental information systems.

METHODS

A semi-structured interview was conducted with a convenience sample of 18 general dentists in the Pittsburgh area during clinical hours. One hundred and five patient cases were reported by these dentists. Interview transcripts were coded and analyzed using thematic analysis with a constant comparative method to identify categories and themes regarding information needs and information source use patterns.

RESULTS

Two top-level categories of information needs were identified: foreground and background information needs. To meet these needs, dentists used four types of information sources: clinical information/tasks, administrative tasks, patient education and professional development. Major themes of dentists' unmet information needs include: (1) timely access to information on various subjects; (2) better visual representations of dental problems; (3) access to patient-specific evidence-based information; and (4) accurate, complete and consistent documentation of patient records. Resource use patterns include: (1) dentists' information needs matched information source use; (2) little use of electronic sources took place during treatment; (3) source use depended on the nature and complexity of the dental problems; and (4) dentists routinely practiced cross-referencing to verify patient information.

CONCLUSIONS

Dentists have various information needs at the point of care. Among them, the needs for better visual representation and patient-specific evidence-based information are mostly unmet. While patient records and support staff remain the most used information sources, electronic sources other than electronic dental records (EDR) are rarely utilized during patient visits. For future development of dental information or clinical decision-support systems, developers should consider integrating high-quality, up-to-date clinical evidence into comprehensive and easily accessible EDRs as well as supporting dentists' resource use patterns as identified in the study.

Using quality report cards for reshaping dentist practice patterns: a pre-play communication approach

Rationale, aims and objectives Understanding how information disclosure influences dentists' patterns of practice change is important in developing quality-improvement policies and cost containment. Thus, using quality report cards is a promising strategy for investigating whether dentists will reshape their patterns of practice because of the influence of peer comparison. Methods Based on the coordination game, a data warehouse decision support system (DWDSS) was used as a pre-play communication instrument, along with the disclosure of quality report cards, which allow dentists to search their own service rates of dental restoration and restoration replacement as well as compare those results with others. Results and conclusions The group using the DWDSS had a greater decrease in two indicators (i.e. service rates of dental restoration and restoration replacement) than the dentists who did not use it, which implies that the DWDSS is a useful facility for helping dentists filter and evaluate information for establishing the maximum utility in their practice management. The disclosure of information makes significant contributions to solving managerial problems associated with dentists' deviation of practice patterns.

Clinical Decision Support Complements Evidence-Based Decision Making in Dental Practice

Dental professionals as well as consumers of dental health care are driving the demand for access to reliable information so they can make more informed decisions. Clinical decision support (CDS) includes a variety of printed and electronic tools, systems, products, and services that make knowledge and information available to the user. CDS is the main way people will be able to access important facts, ideas, concepts, and the latest thinking about personal and population-based health subjects. CDS has its greatest potential at the point of care where it can facilitate good-quality evidence-based decision-making.

Clinical computing in general dentistry

OBJECTIVE

Measure the adoption and utilization of, opinions about, and attitudes toward clinical computing among general dentists in the United States.

DESIGN

Telephone survey of a random sample of 256 general dentists in active practice in the United States.

MEASUREMENTS

A 39-item telephone interview measuring practice characteristics and information technology infrastructure; clinical information storage; data entry and access; attitudes toward and opinions about clinical computing (features of practice management systems, barriers, advantages, disadvantages, and potential improvements); clinical Internet use; and attitudes toward the National Health Information Infrastructure.

RESULTS

The authors successfully screened 1,039 of 1,159 randomly sampled U.S. general dentists in active practice (89.6% response rate). Two hundred fifty-six (24.6%) respondents had computers at chairside and thus were eligible for this study. The authors successfully interviewed 102 respondents (39.8%). Clinical information associated with administration and billing, such as appointments and treatment plans, was stored predominantly on the computer; other information, such as the medical history and progress notes, primarily resided on paper. Nineteen respondents, or 1.8% of all general dentists, were completely paperless. Auxiliary personnel, such as dental assistants and hygienists, entered most data. Respondents adopted clinical computing to improve office efficiency and operations, support diagnosis and treatment, and enhance patient communication and perception. Barriers included insufficient operational reliability, program limitations, a steep learning curve, cost, and infection control issues.

CONCLUSION

Clinical computing is being increasingly adopted in general dentistry. However, future research must address usefulness and ease of use, workflow support, infection control, integration, and implementation issues.