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Background Dentists strive to provide safe and effective oral healthcare. However, some patients may encounter an adverse event (AE) defined as "unnecessary harm due to dental treatment." In this research, we propose and evaluate two systems for categorizing the type and severity of AEs encountered at the dental office. Methods Several existing medical AE type and severity classification systems were reviewed and adapted for dentistry. Using data collected in previous work, two initial dental AE type and severity classification systems were developed. Eight independent reviewers performed focused chart reviews, and AEs identified were used to evaluate and modify these newly developed classifications. Results A total of 958 charts were independently reviewed. Among the reviewed charts, 118 prospective AEs were found and 101 (85.6%) were verified as AEs through a consensus process. At the end of the study, a final AE type classification comprising 12 categories, and an AE severity classification comprising 7 categories emerged. Pain and infection were the most common AE types representing 73% of the cases reviewed (56% and 17%, respectively) and 88% were found to cause temporary, moderate to severe harm to the patient. Conclusions Adverse events found during the chart review process were successfully classified using the novel dental AE type and severity classifications. Understanding the type of AEs and their severity are important steps if we are to learn from and prevent patient harm in the dental office.

Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disease. However, the limited field-of-view of bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy.Retrospective purposive sampling of cases with healthy periodontium and diseased periodontium with bitewing and periapical radiographs and clinician recorded CAL were utilized. Data utilized was from July 1, 2016 through January 30, 2020. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were mean bias error (MBE), mean absolute error (MAE) and Dunn's pairwise test comparing CAL at p=0.05.Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with MAE of 1.04mm and 1.50mm respectively. The Dunn's pairwise test indicated statistically significant improvement in CAL prediction accuracy between inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn's pairwise value of -63.89.This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing and periapical images.Artificial intelligence was developed and utilized to predict clinical attachment level compared to clinical measurements. A generative adversarial inpainting network with partial convolutions was developed, tested and validated to predict clinical attachment level. The inpainting approach was found to be superior to non-inpainted methods and within the 1mm clinician-determined measurement standard.

Caries indices, the basis of epidemiologic caries measures, are not easily obtained in clinical settings. This study’s objective was to design, test, and validate an automated program (Valid Electronic Health Record Dental Caries Indices Calculator Tool [VERDICT]) to calculate caries indices from an electronic health record (EHR). Synthetic use case scenarios and actual patient cases of primary, mixed, and permanent dentition, including decayed, missing, and filled teeth (DMFT/dmft) and tooth surfaces (DMFS/dmfs) were entered into the EHR. VERDICT measures were compared to a previously validated clinical electronic data capture (EDC) system and statistical program to calculate caries indices. Four university clinician-researchers abstracted EHR caries exam data for 45 synthetic use cases into the EDC and post-processed with SAS software creating a gold standard to compare the ­VERDICT-derived caries indices. Then, 2 senior researchers abstracted EHR caries exam data and calculated caries indices for 24 patients, allowing further comparisons to VERDICT indices. Agreement statistics were computed among abstractors, and discrepancies were resolved by consensus. Agreement statistics between the 2 final-phase abstractors and the VERDICT measures showed extremely high concordance: Lin’s concordance coefficients (LCCs) >0.99 for dmfs, dmft, DS, ds, DT, dt, ms, mt, FS, fs, FT, and ft; LCCs >0.95 for DMFS and DMFT; and LCCs of 0.92–0.93 for MS and MT. Caries indices, essential to developing primary health outcome measures for research, can be reliably derived from an EHR using VERDICT. Using these indices will enable population oral health management approaches and inform quality improvement efforts.

Objective: To comparatively evaluate the effectiveness of three different methods involving end-users for detecting usability problems in an EHR: user testing, semi-structured interviews and surveys. Materials and methods: Data were collected at two major urban dental schools from faculty, residents and dental students to assess the usability of a dental EHR for developing a treatment plan. These included user testing (N= 32), semi-structured interviews (N= 36), and surveys (N= 35). Results: The three methods together identified a total of 187 usability violations: 54% via user testing, 28% via the semi-structured interview and 18% from the survey method, with modest overlap. These usability problems were classified into 24 problem themes in 3 broad categories. User testing covered the broadest range of themes (83%), followed by the interview (63%) and survey (29%) methods. Discussion: Multiple evaluation methods provide a comprehensive approach to identifying EHR usability challenges and specific problems. The three methods were found to be complementary, and thus each can provide unique insights for software enhancement. Interview and survey methods were found not to be sufficient by themselves, but when used in conjunction with the user testing method, they provided a comprehensive evaluation of the EHR. Conclusion: We recommend using a multi-method approach when testing the usability of health information technology because it provides a more comprehensive picture of usability challenges.

BackgroundPoor usability is one of the major barriers for optimally using electronic health records (EHRs). Dentists are increasingly adopting EHRs, and are using structured data entry interfaces to enter data such that the data can be easily retrieved and exchanged. Until recently, dentists have lacked a standardized terminology to consistently represent oral health diagnoses.ObjectivesIn this study we evaluated the usability of a widely used EHR interface that allow the entry of diagnostic terms, using multi-faceted methods to identify problems and work with the vendor to correct them using an iterative design method.MethodsFieldwork was undertaken at two clinical sites, and dental providers as subjects participated in user testing (n = 32), interviews (n = 36) and observations (n = 24).ResultsUser testing revealed that only 22-41% of users were able to successfully complete a simple task of entering one diagnosis, while no user was able to complete a more complex task. We identified and characterized 24 high-level usability problems reducing efficiency and causing user errors. Interface-related problems included unexpected approaches for displaying diagnosis, lack of visibility, and inconsistent use of UI widgets. Terminology related issues included missing and mis-categorized concepts. Work domain issues involved both absent and superfluous functions. In collaboration with the vendor, each usability problem was prioritized and a timeline set to resolve the concerns.DiscussionMixed methods evaluations identified a number of critical usability issues relating to the user interface, underlying terminology of the work domain. The usability challenges were found to prevent most users from successfully completing the tasks. Our further work we will determine if changes to the interface, terminology and work domain do result in improved usability.

Although standardized terminologies such as the International Classification of Diseases have been in use in medicine for over a century, efforts in the dental profession to standardize dental diagnostic terms have not achieved widespread acceptance. To address this gap, a standardized dental diagnostic terminology, the EZCodes, was developed in 2009. Fifteen dental education institutions in the United States and Europe have implemented the EZCodes dental diagnostic terminology. This article reports on the utilization and valid entry of the EZCodes at three of the dental schools that have adopted this standardized dental diagnostic terminology. Electronic data on the use of procedure codes with diagnostic terms from the three schools over a period from July 2010 to June 2011 were aggregated. The diagnostic term and procedure code pairs were adjudicated by three calibrated dentists. Analyses were conducted to gain insight into the utilization and valid entry of the EZCodes diagnostic terminology in the one-year period. Error proportions in the entry of diagnostic term (and by diagnostic category) were also computed. In the twelve-month period, 29,965 diagnostic terms and 249,411 procedure codes were entered at the three institutions resulting in a utilization proportion of 12 percent. Caries and periodontics were the most frequently used categories. More than 1,000 of the available 1,321 diagnostic terms were never used. Overall, 60.5 percent of the EZCodes entries were found to be valid. The results demonstrate low utilization of EZCodes in an electronic health record and raise the need for specific training of dental providers on the importance of using dental diagnostic terminology and specifically how to use the terms in the electronic record. These findings will serve to increase the use/correct use of the EZCodes dental diagnostic terminology and ultimately create a reliable platform for undertaking clinical, outcomes, and quality improvement-related research.

BACKGROUND: Patients with diabetes are at increased risk of developing oral complications, and annual dental examinations are an endorsed preventive strategy. The authors evaluated the feasibility and validity of implementing an automated electronic health record (EHR)-based dental quality measure to determine whether patients with diabetes received such evaluations. METHODS: The authors selected a Dental Quality Alliance measure developed for claims data and adapted the specifications for EHRs. Automated queries identified patients with diabetes across 4 dental institutions, and the authors manually reviewed a subsample of charts to evaluate query performance. After assessing the initial EHR measure, the authors defined and tested a revised EHR measure to capture better the oral care received by patients with diabetes. RESULTS: In the initial and revised measures, the authors used EHR automated queries to identify 12,960 and 13,221 patients with diabetes, respectively, in the reporting year. Variations in the measure scores across sites were greater with the initial measure (range, 36.4–71.3%) than with the revised measure (range, 78.8–88.1%). The automated query performed well (93% or higher) for sensitivity, specificity, and positive and negative predictive values for both measures. CONCLUSIONS: The results suggest that an automated EHR-based query can be used successfully to measure the quality of oral health care delivered to patients with diabetes. The authors also found that using the rich data available in EHRs may help estimate the quality of care better than can relying on claims data. PRACTICAL IMPLICATIONS: Detailed clinical patient-level data in dental EHRs may be useful to dentists in evaluating the quality of dental care provided to patients with diabetes.

Background Although some patients experience adverse events (AEs) resulting in harm caused by treatments in dentistry, few published reports have detailed how dental providers describe these events. Understanding how dental treatment professionals view AEs is essential to building a safer environment in dental practice. Methods The authors interviewed dental professionals and domain experts through focus groups and in-depth interviews and asked them to identify the types of AEs that may occur in dental settings. Results The initial interview and focus group findings yielded 1,514 items that included both causes and AEs. In total, 632 causes were coded into 1 of the 8 categories of the Eindhoven classification, and 882 AEs were coded into 12 categories of a newly developed dental AE classification. Interrater reliability was moderate among coders. The list was reanalyzed, and duplicate items were removed leaving a total of 747 unique AEs and 540 causes. The most frequently identified AE types were "aspiration and ingestion" at 14% (n = 142), "wrong-site, wrong-procedure, wrong-patient errors" at 13%, "hard-tissue damage" at 13%, and "soft-tissue damage" at 12%. Conclusions Dental providers identified a large and diverse list of AEs. These events ranged from "death due to cardiac arrest" to "jaw fatigue from lengthy procedures." Practical Implications Identifying threats to patient safety is a key element of improving dental patient safety. An inventory of dental AEs underpins efforts to track, prevent, and mitigate these events.

Background and objectivesCurrent clinical methods for diagnosing secondary caries are unreliable for identifying the early stages of decay around restorative materials. The objective of this study was to access the integrity of restoration margins in natural teeth using near-infrared (NIR) reflectance and transillumination images at wavelengths between 1300 and 1700-nm and to determine the optimal NIR wavelengths for discriminating composite materials from dental hard tissues.Materials and methodsTwelve composite margins (n=12) consisting of class I, II and V restorations were chosen from ten extracted teeth. The samples were imaged in vitro using NIR transillumination and reflectance, polarization sensitive optical coherence tomography (PS-OCT) and a high-magnification digital microscope. Samples were serially sectioned into 200-μm slices for histological analysis using polarized light microscopy (PLM) and transverse microradiography (TMR). Two independent examiners evaluated the presence of demineralization at the sample margin using visible detection with 10× magnification and NIR images presented digitally. Composite restorations were placed in sixteen sound teeth (n=16) and imaged at multiple NIR wavelengths ranging from λ=1300 to 1700-nm using NIR transillumination. The image contrast was calculated between the composite and sound tooth structure.ResultsIntensity changes in NIR images at wavelengths ranging from 1300 to 1700-nm correlate with increased mineral loss measured using TMR. NIR reflectance and transillumination at wavelengths coincident with increased water absorption yielded significantly higher (P

As dental schools continue to seek the most effective ways to provide clinical education for students, it is important to track the effects innovations have on students' clinical experience to allow for quantitative comparisons of various curricula. The aim of this study was to compare the impact of three successive clinical curricula on students' experience at one U.S. dental school. The three were a discipline-based curriculum (DBC), a comprehensive care curriculum (CCC), and a procedural requirement curriculum plus externships (PRCE). Students' clinic experience data from 1992 to 2013 were analyzed for total experience and in five discipline areas. Clinic experience metrics analyzed were patient visits (PVs), relative value units (RVUs), and equivalent amounts (EQAs). A minimum experience threshold (MET) and a high experience threshold (HET) were set at one standard deviation above and below the mean for the DBC years. Students below the MET were designated as low achievers; students above the HET were designated as high achievers. The results showed significant differences among the three curricula in almost all areas of comparison: total PVs, total EQAs, total RVUs, RVUs by discipline, and number of high and low achievers in total clinical experience and by discipline. The comprehensive care approach to clinical education did not negatively impact students' clinical experience and in many cases enhanced it. The addition of externships also enhanced student total clinical experience although more study is needed to determine their effectiveness. The insights provided by this study suggest that the methodology used including the metrics of PVs, EQAs, and RVUs may be helpful for other dental schools in assessing students' clinical experience.

Objective: Secondary data are a significant resource for in‐depth epidemiologic and public health research. It also allows for effective quality control and clinical outcomes measurement. To illustrate the value of structured diagnostic entry, a use case was developed to quantify adherence to current practice guidelines for managing chronic moderate periodontitis (CMP).Methods: Six dental schools using the same electronic health record (EHR) contribute data to a dental data repository (BigMouth) based on the i2b2 data‐warehousing platform. Participating institutions are able to query across the full repository without being able to back trace specific data to its originating institution. At each of the three sites whose data are included in this analysis, the Dental Diagnostic System (DDS) terminology was used to document diagnoses in the clinics. We ran multiple queries against this multi‐institutional database, and the output was validated by manually reviewing a subset of patient charts.Results: Over the period under study, 1,866 patients were diagnosed with CMP. Of these, 15 percent received only periodontal prophylaxis treatment, 20 percent received only periodontal maintenance treatment, and only 41 percent received periodontal maintenance treatment in combination with other AAP guideline treatments.Conclusions: Our results showed that most patients with CMP were not treated according to the AAP guidelines. On the basis of this use case, we conclude that the availability and habitual use of a structured diagnosis in an EHR allow for the aggregation and secondary analyses of clinical data to support downstream analyses for quality improvement and epidemiological assessments.

BackgroundThe authors conducted a study to determine the frequency and type of adverse events (AEs) associated with dental devices reported to the Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) database.MethodsThe authors downloaded and reviewed the dental device-related AEs reported to MAUDE from January 1, 1996, through December 31,2011.ResultsMAUDE received a total of 1,978,056 reports between January 1, 1996, and December 31, 2011. Among these reports, 28,046 (1.4%) AE reports were associated with dental devices. Within the dental AE reports that had event type information, 17,261 reported injuries, 7,777 reported device malfunctions, and 66 reported deaths. Among the 66 entries classified as death reports, 52 reported a death in the description; the remaining were either misclassified or lacked sufficient information in the report to determine whether a death had occurred. Of the dental device-associated AEs, 53.5% pertained to endosseous implants.ConclusionsA plethora of devices are used in dental care. To achieve Element 1 of Agency for Healthcare Research and Quality's Patient Safety Initiative, clinicians and researchers must be able to monitor the safety of dental devices. Although MAUDE was identified by the authors as essentially the sole source of this valuable information on adverse events, their investigations led them to conclude that MAUDE had substantial limitations that prevent it from being the broad-based patient safety sentinel the profession requires.Practical implicationsAs potential contributors to MAUDE, dental care teams play a key role in improving the profession's access to information about the safety of dental devices.

The objective of this study was to examine the feasibility of sintering dental ceramic (e.g., zirconia) in a microwave oven, and compare the mechanical properties of these with similar materials sintered in a conventional furnace. Zirconia cylinders were heated to 1100°C, 1300°C, 1350°C, 1400 and 1450°C in microwave and conventional furnaces and sintered at varying dwell times. Our results showed that the mechanical and microstructural properties of samples sintered by microwave were comparable to those of the conventionally sintered samples. Indentation hardness and fracture toughness were found to be 1256 ± 7 and 6.4 ± 0.4 Mpa(m)0.5 respectively. The microwave samples were sintered in significantly less time had less voids and more uniform grain structure. Our results suggest that microwave sintering can produce rapid and reliable processing of complex dental ceramics with better microstructural properties and energy savings.

Fourteen dental copings (zirconia stabilized with 3 mol% yttria) were layered with Noritake dental glass-ceramic and sintered in the microwave furnace with various sintering temperatures. Sintered zirconia rectangular beams measuring 50 mm in length, 4 mm in width and 0.75 mm in height were coated with the dental glass-ceramic and sintered in the microwave furnace. These were subjected to a four-point bend test to calculate modulus of rupture. Sintering temperature of 800°C in the microwave furnace with ramp rates in excess of 100°C per minute was sufficient to attain good sintered crowns. Indentation hardness with 200g loads and 500g loads for the microwave-sintered teeth resulted in hardness values of 0.685 ± 0.0245 GPa and 6.56 ± 0.4 GPa. The indentation fracture toughness values with 200g and 500g were calculated to be 2.26 ± 0.8 MPa(m)0.5 and 0.97 ± 0.1 MPa(m)0.5 respectively and agreed well with the published values [1]. The failure load for the layered beams in the bend test was 81.8 ± 17.7 N and the resulting modulus of rupture was 632 ± 105 MPa.

Background Patients with diabetes are at increased risk of developing oral complications, and annual dental examinations are an endorsed preventive strategy. The authors evaluated the feasibility and validity of implementing an automated electronic health record (EHR)–based dental quality measure to determine whether patients with diabetes received such evaluations. Methods The authors selected a Dental Quality Alliance measure developed for claims data and adapted the specifications for EHRs. Automated queries identified patients with diabetes across 4 dental institutions, and the authors manually reviewed a subsample of charts to evaluate query performance. After assessing the initial EHR measure, the authors defined and tested a revised EHR measure to capture better the oral care received by patients with diabetes. Results In the initial and revised measures, the authors used EHR automated queries to identify 12,960 and 13,221 patients with diabetes, respectively, in the reporting year. Variations in the measure scores across sites were greater with the initial measure (range, 36.4-71.3%) than with the revised measure (range, 78.8-88.1%). The automated query performed well (93% or higher) for sensitivity, specificity, and positive and negative predictive values for both measures. Conclusions The results suggest that an automated EHR-based query can be used successfully to measure the quality of oral health care delivered to patients with diabetes. The authors also found that using the rich data available in EHRs may help estimate the quality of care better than can relying on claims data. Practical Implications Detailed clinical patient-level data in dental EHRs may be useful to dentists in evaluating the quality of dental care provided to patients with diabetes.

Although standardized terminologies such as the International Classification of Diseases have been in use in medicine for over a century, efforts in the dental profession to standardize dental diagnostic terms have not achieved widespread acceptance. To address this gap, a standardized dental diagnostic terminology, the EZCodes, was developed in 2009. Fifteen dental education institutions in the United States and Europe have implemented the EZCodes dental diagnostic terminology. This article reports on the utilization and valid entry of the EZCodes at three of the dental schools that have adopted this standardized dental diagnostic terminology. Electronic data on the use of procedure codes with diagnostic terms from the three schools over a period from July 2010 to June 2011 were aggregated. The diagnostic term and procedure code pairs were adjudicated by three calibrated dentists. Analyses were conducted to gain insight into the utilization and valid entry of the EZCodes diagnostic terminology in the one-year period. Error proportions in the entry of diagnostic term (and by diagnostic category) were also computed. In the twelve-month period, 29,965 diagnostic terms and 249,411 procedure codes were entered at the three institutions resulting in a utilization proportion of 12 percent. Caries and periodontics were the most frequently used categories. More than 1,000 of the available 1,321 diagnostic terms were never used. Overall, 60.5 percent of the EZCodes entries were found to be valid. The results demonstrate low utilization of EZCodes in an electronic health record and raise the need for specific training of dental providers on the importance of using dental diagnostic terminology and specifically how to use the terms in the electronic record. These findings will serve to increase the use/correct use of the EZCodes dental diagnostic terminology and ultimately create a reliable platform for undertaking clinical, outcomes, and quality improvement-related research.

ObjectiveTreatment planning, an essential component of clinical practice, has received little attention in the dental literature and there appears to be no consistent format being followed in the teaching and development of treatment plans within dental school curricula. No investigation, to our knowledge, has been carried out to explore the subject of treatment planning since the advent of electronic health record (EHR) use in dentistry. It is therefore important to examine the topic of treatment planning in the context of EHRs.MethodsThis paper reports on how 25 predoctoral dental students from two U.S. schools performed when asked to complete diagnosis and treatment planning exercises for two clinical scenarios in an EHR. Three calibrated clinical teaching faculty scored diagnosis entry, diagnosis-treatment (procedure) pairing, and sequencing of treatment according to criteria taught in their curriculum. Scores were then converted to percent correct and reported as means (with standard deviations).ResultsOverall, the participants earned 48.2% of the possible points. Participants at School 2 earned a mean of 54.3% compared with participants at School 1, who earned 41.9%. Students fared better selecting the appropriate treatment (59.8%) compared with choosing the correct diagnoses (41.9%) but performed least favorably when organizing the sequence of their treatment plans (41.7%).ConclusionOur results highlight the need to improve the current process by which treatment planning is taught and also to consider the impact of technology on the fundamental skills of diagnosis and treatment planning within the modern educational setting.

Ever since Kumar Patel introduced lasers in 1960s’, researchers have been looking into its possible applications in the field of dentistry. Researchers have investigated the effects of laser radiation on teeth, bone, pulp and oral mucosal tissues (Taylor, Shklar, & Roeber, 1965). CO2 lasers have been used extensively in medical field and the first laser to be approved by FDA for dental application was Nd:YAG (Neodymium-Yttrium-Aluminum-Garnet) in 1990s. Since then many types of lasers including CO2, Er:YAG (Erbium-Yttrium-Aluminum-Garnet), Diode, Er Cr:YSGG (Erbium-Chromium-Yttrium-Scallium-Gallium-Garnet) have been approved for dental use. FDA approved Er:YAG for dental hard tissue in 1997 and has approved other types of lasers for soft and hard tissue procedures in many area of dentistry.

Quantitative light-induced fluorescence (QLF) method has been used in vitro, in situ and in vivo as a diagnostic method in assessing smooth surface caries over the last twenty years. Coincidentally, reversal or remineralization of early smooth surface lesion by remineralizing agents has been substantiated by in vitro and in situ studies over the last twenty years. However, there are very few reported clinical trials to assess the effectiveness of these remineralizing agents by using QLF. This research aims at exploring the remineralizing potential of a specially formulated remineralizing mouth rinse and the ‘gold-standard’ Sodium Fluoride (NaF) rinse by using QLF as a monitoring tool. Methods: Twelve Subjects at high-risk for caries progression with smooth surface caries on anterior teeth were selected and randomly assigned to the control and to the test group. Twice daily (after breakfast and after dinner) the subjects rinsed 1 oz of either the test (Calcium and Fluoride; 250 ppm F) or the control rinse (NaF; 250 ppm F). Baseline assessments of the progression of the smooth surface lesion were done by QLF at 0, 1, 2, and end of 3 months. A two-sided t-test, MANOVA and exact F-statistics were used to determine differences between treatment groups. Results: There was no significant difference between the two groups in preventing the progression of caries (p> 0.05). It was concluded that QLF could be a useful diagnostic tool to aid the clinician in caries management.