Learning from data in dentistry: Summary of the third annual OpenWide conference.

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) 2022 annual conference.

Evaluating and improving the usability of a mHealth platform to assess postoperative dental pain.

Objectives: The use of interactive mobile health (mHealth) applications to monitor patient-reported postoperative pain outcomes is an emerging area in dentistry that requires further exploration. This study aimed to evaluate and improve the usability of an existing mHealth application. Materials and methods: The usability of the application was assessed iteratively using a 3-phase approach, including a rapid cognitive walkthrough (Phase I), lab-based usability testing (Phase II), and in situ pilot testing (Phase III). The study team conducted Phase I, while providers and patients participated in Phase II and III. Results: The rapid cognitive walkthrough identified 23 potential issues that could negatively impact user experience, with the majority classified as system issues. The lab-based usability testing yielded 141 usability issues.; 43% encountered by patients and 57% by dentists. Usability problems encountered during pilot testing included undelivered messages due to mobile phone carrier and service-related issues, errors in patients' phone number data entry, and problems in provider training. Discussion: Through collaborative and iterative work with the vendor, usability issues were addressed before launching a trial to assess its efficacy. Conclusion: The usability of the mHealth application for postoperative dental pain was remarkably improved by the iterative analysis and interdisciplinary collaboration.

Patient and dentist perspectives on collecting patient reported outcomes after painful dental procedures in the National Dental PBRN.

BACKGROUND: Dental Patient Reported Outcomes (PROs) relate to a dental patient's subjective experience of their oral health. How practitioners and patients value PROs influences their successful use in practice. METHODS: Semi-structured interviews were conducted with 22 practitioners and 32 patients who provided feedback on using a mobile health (mHealth) platform to collect the pain experience after dental procedures. A themes analysis was conducted to identify implementation barriers and facilitators. RESULTS: Five themes were uncovered: (1) Sense of Better Care. (2) Tailored Follow-up based on the dental procedure and patient's pain experience. (3) Effective Messaging and Alerts. (4) Usable Digital Platform. (5) Routine mHealth Integration. CONCLUSION: Frequent automated and preferably tailored follow-up messages using an mHealth platform provided a positive care experience for patients, while providers felt it saved them time and effort. Patients thought that the mHealth questionnaires were well-developed and of appropriate length. The mHealth platform itself was perceived as user-friendly by users, and most would like to continue using it. PRACTICAL IMPLICATIONS: Patients are prepared to use mobile phones to report their pain experience after dental procedures. Practitioners will be able to close the post-operative communication gap with their patients, with little interruption of their workflow.

Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data.

AIM: To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions. MATERIALS AND METHODS: Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility. Chart reviews were conducted to confirm the algorithm's ability to accurately extract clinical data from the EHR, and then to test its ability to suggest an accurate diagnosis. Subsequently, refinements were made to address limitations of the data and specific clinical situations. Each refinement was evaluated through chart reviews by expert periodontists at the study sites. RESULTS: Three-hundred and twenty-three charts were manually reviewed, and a periodontal diagnosis (healthy, gingivitis or periodontitis including stage and grade) was made by expert periodontists for each case. After developing the initial version of the algorithm using the unmodified 2017 World Workshop criteria, accuracy was 71.8% for stage alone and 64.7% for stage and grade. Subsequently, 16 modifications to the algorithm were proposed and 14 were accepted. This refined version of the algorithm had 79.6% accuracy for stage alone and 68.8% for stage and grade together. CONCLUSIONS: Our findings suggest that a rule-based algorithm for suggesting a periodontal diagnosis using EHR data can be implemented with moderate accuracy in support of chairside clinical diagnostic decision making, especially for inexperienced clinicians. Grey-zone cases still exist, where clinical judgement will be required. Future applications of similar algorithms with improved performance will depend upon the quality (completeness/accuracy) of EHR data.

Developing and assessing the use of demonstration videos in the preclinical simulation laboratory.

PURPOSE: This study aims to report the development of a preclinical simulation laboratory Demonstration Video Series (DVS) for the 2021-2022 academic year, measure its usage and usefulness, and compare these findings to the usage and usefulness of the existing didactic lecture videos. METHODS: The DVS videos were intended to be viewed before each preclinical simulation laboratory session along with the pre-existing didactic lectures (DL) by University of California San Francisco (UCSF) learners. Usage measurements included the percentage of the class that viewed each video, the number of views that each video received, and the average duration of each video that was watched. Usefulness of the videos was measured by a survey that assessed learner perspective on knowledge and ability to apply that knowledge during the simulation lab exercises. Both usage and usefulness of the DVS were then compared to the usage and usefulness of the DL. Both descriptive statistics and independent sample hypothesis tests were performed to compare the differences in proportion between DVS and DL mediums. RESULTS: Statistically significant differences were found in terms of both usage and usefulness of the DVS compared to the DL, with DVS being utilized more overall. With an 81% response rate, survey analysis revealed statistically significant differences among the learners' perspectives on the usefulness of the DVS compared to the DL, with a clear preference for the DVS over the DL and an overwhelmingly positive perception of the DVS. CONCLUSION: The DVS was found to be a valuable addition to the preclinical laboratory sessions for first-year learners.

Evaluating the Impact of an mHealth Platform for Managing Acute Postoperative Dental Pain: Randomized Controlled Trial.

Background: Postoperative dental pain is pervasive and can affect a patient's quality of life. Adopting a patient-centric approach to pain management involves having contemporaneous information about the patient's experience of pain and using it to personalize care. Objective: In this study, we evaluated the use of a mobile health (mHealth) platform to collect pain-related patient-reported outcomes over 7 days after the patients underwent pain-inducing dental procedures; we then relayed the information to the dentist and determined its impact on the patient's pain experience. Methods: The study used a cluster-randomized experimental study design with an intervention arm where patients were prompted to complete a series of questions relating to their pain experience after receiving automated text notifications on their smartphone on days 1, 3, 5, and 7, with the resulting information fed back to dentists, and a control arm where patients received usual care. Providers were randomized, and patients subsequently assumed the enrollment status of their providers. Providers or their staff identified eligible patients and invited them to participate in the study. Provider interviews and surveys were conducted to evaluate acceptance of the mHealth platform. Results: A total of 42 providers and 1525 patients participated. For the primary outcome (pain intensity on a 1 to 10 scale, with 10 being the most painful), intervention group patients reported an average pain intensity of 4.8 (SD 2.6), while those in the control group reported an average pain intensity of 4.7 (SD 2.8). These differences were not significant. There were also no significant differences in secondary outcomes, including pain interference with activity or sleep, patient satisfaction with pain management, or opioid prescribing. Patient surveys revealed reluctance to use the app was mostly due to technological challenges, data privacy concerns, and a preference for phone calls over texting. Providers had high satisfaction with the app and suggested integrating additional features, such as an in-system camera for patients to upload pictures and videos of the procedural site, and integration with the electronic health record system. Conclusions: While the mHealth platform did not have a significant impact on acute postoperative pain experience, patients and providers indicated improvement in patient-provider communication, patient-provider relationship, postoperative complication management, and ability to manage pain medication prescribing. Expanded collaboration between mHealth developers and frontline health care providers can facilitate the applicability of these platforms, further help improve its integration with the normal clinic workflow, and assist in moving toward a more patient-centric approach to pain management.

Assessing the safety of deep sedation in outpatient pediatric oral health care.

BACKGROUND: Children are the patient subgroup with the lowest error tolerance regarding deep sedation (DS)-supported care. This study assessed the safety of DS-supported pediatric dental treatment carried out in an outpatient setting through retrospective review of patient charts. METHODS: An automated script was developed to identify charts of pediatric patients who underwent DS-supported dental procedures from 2017 through 2019 at a dental clinic. Charts were assessed for the presence of sedation-related adverse events (AEs). A panel of experts performed a second review and confirmed or refuted the designation of AE (by the first reviewer). AEs were classified with the Tracking and Reporting Outcomes of Procedural Sedation system. RESULTS: Of the 175 DS cases, 19 AEs were identified in 15 cases (8.60%). Using the Tracking and Reporting Outcomes of Procedural Sedation classification system, 7 (36.84%) events were related to the airway and breathing category, 9 (47.37%) were related to sedation quality (including a dizzy patient who fell at the checkout desk and sustained a head laceration), and 3 (15.79%) were classified as an allergy. CONCLUSION: This study suggests an AE (whether relatively minor or of potentially major consequence) occurs in 1 of every 12 DS cases involving pediatric patients, performed at an outpatient dental clinic. Larger studies are needed, in addition to root cause analyses. PRACTICAL IMPLICATIONS: As dentists increasingly pivot in the use of DS services from in-hospital to outpatient settings, patients expect comparable levels of safety. This work helps generate evidence to drive targeted efforts to improve the safety and reliability of pediatric outpatient sedation.

Designing audit and feedback dashboards for dentists to monitor their opioid prescribing.

BACKGROUND AND OBJECTIVE: Prescription drug abuse is a major factor leading to drug overdose deaths in the US and dentists are one of the leading prescribers of opioid pain medication. Knowing that Audit & Feedback (A&F) dashboards are an effective tool and are used as quality improvement interventions, we aimed to develop such dashboards personalized for dental providers which could allow them to monitor their own opioid prescribing performance. METHODS: In this paper we report on the process for designing the A&F dashboards for dentists which were developed by using an iterative human-centered design process. The results obtained from each iteration were used to enrich the information needs analyses, provide function testing, and guide the design decisions of the next iteration. RESULTS: Engaging dentists in the development and refinement of the dashboards while using the think-aloud protocol for user-testing, provided rapid feedback and identified areas that were confusing and needed either a redesign or additional explanatory content. The final version of dashboards consisted of displaying necessary information through easy to interpret visualizations and interactive features. These included providing access to current national and organizational prescribing guidelines, displaying changes in individual prescribing behavior over time, comparing individual prescribing rate to peer group rate and target rate, displaying procedure specific prescribing, integrating patient reported post-operative dental pain experience and providing navigation and interpretation tips for users. The dashboards were easy to learn and understand for the dentists and were deemed as worth using often in dental practice. CONCLUSION: Our research was able to demonstrate the creation of useful and usable A&F dashboards using data from electronic dental records and patient surveys, for dentists to effectively monitor their opioid prescribing behavior. Efficacy of the dashboards will be tested in future work.

Testing dental quality measures: Emergency department visits for nontraumatic dental conditions and subsequent follow-up dental visits.

BACKGROUND: The goal of this study was to test the feasibility, reliability, and validity of the Dental Quality Alliance's adult dental quality measures for system-level implementation for ambulatory care sensitive (ACS) emergency department (ED) visits for nontraumatic dental conditions (NTDCs) in adults and follow-up after ED visits for NTDCs in adults. METHODS: Medicaid enrollment and claims data from Oregon and Iowa were used for measure testing. Testing included validation of diagnosis codes in claims data through patient record reviews of ED visits and calculations of κ statistic, sensitivity, and specificity. RESULTS: Adult Medicaid enrollees' ACS NTDC ED visits ranged from 209 through 310 per 100,000 member-months. In both states, patients in the age category 25 through 34 years and non-Hispanic Black patients had the highest rates of ACS ED visits for NTDCs. Only one-third of all ED visits were associated with a follow-up dental visit within 30 days, decreasing to approximately one-fifth with a 7-day follow-up. The agreement between the claims data and patient records for identification of ACS ED visits for NTDCs was 93%, κ statistic was 0.85, sensitivity was 92%, and specificity was 94%. CONCLUSIONS: Testing revealed the feasibility, reliability, and validity of 2 DQA quality measures. Most beneficiaries did not have a follow-up with a dentist within 30 days of an ED visit. PRACTICAL IMPLICATIONS: Adoption of quality measures by state Medicaid programs and other integrated care systems will enable active tracking of beneficiaries with ED visits for NTDCs and develop strategies to connect them to dental homes.

Identifying Contributing Factors Associated With Dental Adverse Events Through a Pragmatic Electronic Health Record-Based Root Cause Analysis.

OBJECTIVE: This study assessed contributing factors associated with dental adverse events (AEs). METHODS: Seven electronic health record-based triggers were deployed identifying potential AEs at 2 dental institutions. From 4106 flagged charts, 2 reviewers examined 439 charts selected randomly to identify and classify AEs using our dental AE type and severity classification systems. Based on information captured in the electronic health record, we analyzed harmful AEs to assess potential contributing factors; harmful AEs were defined as those that resulted in temporary moderate to severe harm, required hospitalization, or resulted in permanent moderate to severe harm. We classified potential contributing factors according to (1) who was involved (person), (2) what were they doing (tasks), (3) what tools/technologies were they using (tools/technologies), (4) where did the event take place (environment), (5) what organizational conditions contributed to the event? (organization), (6) patient (including parents), and (7) professional-professional collaboration. A blinded panel of dental experts conducted a second review to confirm the presence of an AE. RESULTS: Fifty-nine cases had 1 or more harmful AEs. Pain occurred most frequently (27.1%), followed by nerve injury (16.9%), hard tissue injury (15.2%), and soft tissue injury (15.2%). Forty percent of the cases were classified as "temporary not moderate to severe harm." Person (training, supervision, and fatigue) was the most common contributing factor (31.5%), followed by patient (noncompliance, unsafe practices at home, low health literacy, 17.1%), and professional-professional collaboration (15.3%). CONCLUSIONS: Pain was the most common harmful AE identified. Person, patient, and professional-professional collaboration were the most frequently assessed factors associated with harmful AEs.

Development of quality measures to assess tooth decay outcomes from electronic health record data.

OBJECTIVES: To develop outcomes of care quality measures derived from the dental electronic health record (EHR) to assess the occurrence and timely treatment of tooth decay. METHODS: Quality measures were developed to assess whether decay was treated within 6 months and if new decay occurred in patients seen. Using EHR-derived data of the state of each tooth surface, algorithms compared the patient's teeth at different dates to determine if decay was treated or new decay had occurred. Manual chart reviews were conducted at three sites to validate the measures. The measures were implemented and scores were calculated for three sites over four calendar years, 2016 through 2019. RESULTS: About 954 charts were manually reviewed for the timely treatment of tooth decay measure, with measure performance of sensitivity 97%, specificity 85%, positive predictive value (PPV) 91%, negative predictive value (NPV) 95%. About 739 charts were reviewed for new decay measure, with sensitivity 94%, specificity 99%, PPV 99%, and NPV 94%. Across all sites and years, 52.8% of patients with decay were fully treated within 6 months of diagnosis (n = 247,959). A total of 23.8% of patients experienced new decay, measured at an annual exam (n = 640,004). CONCLUSION: Methods were developed and validated for assessing timely treatment of decay and occurrence of new decay derived from EHR data, creating effective outcome measures. These EHR-based quality measures produce accurate and reliable results that support efforts and advancement in quality assessment, quality improvement, patient care and research.

Study protocol: understanding pain after dental procedures, an observational study within the National Dental PBRN.

BACKGROUND: Patient-reported outcome measures provide an essential perspective on the quality of health care provided. However, how data are collected, how providers value and make sense of the data, and, ultimately, use the data to create meaningful impact all influence the success of using patient-reported outcomes. OBJECTIVES: The primary objective is to assess post-operative pain experiences by dental procedure type through 21 days post-procedure as reported by patients following dental procedures and assess patients' satisfaction with pain management following dental surgical procedures. Secondary objectives are to: 1) assess post-operative pain management strategies 1 week following dental surgical procedures, as recommended by practitioners and reported by patients, and 2) evaluate practitioner and patient acceptance of the FollowApp.Care post visit patient monitoring technology (FollowApp.Care). We will evaluate FollowApp.Care usage, perceived usefulness, ease of use, and impact on clinical workload. DESIGN AND METHODS: We describe the protocol for an observational study involving the use of the FollowApp.Care platform, an innovative mobile application that collects dental patients' assessments of their post-operative symptoms (e.g., pain). The study will be conducted in collaboration with the National Dental Practice-based Research Network, a collective Network of dental practices that include private and group practices, public health clinics, community health centers and Federal Qualified Health Centers, academic institutional settings, and special patient populations. We will recruit a minimum of 150 and up to 215 dental providers and up to 3147 patients who will receive push notifications through text messages FollowApp.Care on their mobile phones at designated time intervals following dental procedures. This innovative approach of implementing an existing and tested mobile health system technology into the real-world dental office setting will actively track pain and other complications following dental procedures. Through patients' use of their mobile phones, we expect to promptly and precisely identify specific pain levels and other issues after surgical dental procedures. The study's primary outcome will be the patients' reported pain experiences. Secondary outcomes include pain management strategies and medications implemented by the patient and provider and perceptions of usefulness and ease of use by patients and providers.

Building Consensus for a Shared Definition of Adverse Events: A Case Study in the Profession of Dentistry.

BACKGROUND: To achieve high-quality health care, adverse events (AEs) must be proactively recognized and mitigated. However, there is often ambiguity in applying guidelines and definitions. We describe the iterative calibration process needed to achieve a shared definition of AEs in dentistry. Our alignment process includes both independent and consensus building approaches. OBJECTIVE: We explore the process of defining dental AEs and the steps necessary to achieve alignment across different care providers. METHODS: Teams from 4 dental institutions across the United States iteratively reviewed patient records after identification of charts using an automated trigger tool. Calibration across teams was supported through negotiated definition of AEs and standardization of evidence provided in review. Interrater reliability was assessed using descriptive and κ statistics. RESULTS: After 5 iterative cycles of calibration, the teams (n = 8 raters) identified 118 cases. The average percent agreement for AE determination was 82.2%. Furthermore, the average, pairwise prevalence and bias-adjusted κ (PABAK) was 57.5% (κ = 0.575) for determining AE presence. The average percent agreement for categorization of the AE type was 78.5%, whereas the PABAK was 48.8%. Lastly, the average percent agreement for categorization of AE severity was 82.2% and the corresponding PABAK was 71.7%. CONCLUSIONS: Successful calibration across reviewers is possible after consensus building procedures. Higher levels of agreement were found when categorizing severity (of identified events) rather than the events themselves. Our results demonstrate the need for collaborative procedures as well as training for the identification and severity rating of AEs.

Translating periodontal data to knowledge in a learning health system.

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.

Development of an Inventory of Dental Harms: Methods and Rationale.

OBJECTIVES: While adverse events (AEs) are all too prevalent, their underlying causes are difficult to assess because they are often multifactorial. Standardizing the language of dental AEs is an important first step toward increasing patient safety for the dental patient. METHODS: We followed a multimodal approach building a dental AE inventory, which included a literature review; review of the MAUDE database; a cross-sectional, self-administered patient survey; focus groups; interviews with providers and domain experts; and chart reviews. RESULTS: One hundred eight unique allergy/toxicity/foreign body response, 70 aspiration/ingestion of foreign body, 70 infection, 52 wrong site/wrong patient/wrong procedure, 23 bleeding, 48 pain, 149 hard tissue injury, 127 soft tissue injury, 91 nerve injury, 171 other systemic complication, and 177 other orofacial complication were identified. Subtype AEs within the categories revealed that allergic reaction, aspiration, pain, and wrong procedure were the most common AEs identified among known (i.e., chart reviews) and hypothetical (i.e., interviews) sources. CONCLUSIONS: Using a multimodal approach, a broad list of dental AEs was developed, in which the AEs were classed into 12 categories. Hard tissue injury was noted frequently during interviews and in actuality. Pain was the unexpected AE that was consistently identified with every modality used. PRACTICAL IMPLICATIONS: Most AEs result in temporary harm with hard tissue injury being a common AE identified through interviews and in actuality through chart reviews. Acknowledging that AEs happen is an important step toward mitigating them and assuring quality of care for our patients.

A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing and periapical images. CLINICAL SIGNIFICANCE: 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 Risk Documentation And Prevention: eMeasures For Dental Electronic Health Records.

BACKGROUND: Longitudinal patient level data available in the electronic health record (EHR) allows for the development, implementation, and validations of dental quality measures (eMeasures). OBJECTIVE: We report the feasibility and validity of implementing two eMeasures. The eMeasures determined the proportion of patients receiving a caries risk assessment (eCRA) and corresponding appropriate risk-based preventative treatments for patients at elevated risk of caries (appropriateness of care [eAoC]) in two academic institutions and one accountable care organization, in the 2019 reporting year. METHODS: Both eMeasures define the numerator and denominator beginning at the patient level, populations' specifications, and validated the automated queries. For eCRA, patients who completed a comprehensive or periodic oral evaluation formed the denominator, and patients of any age who received a CRA formed the numerator. The eAoC evaluated the proportion of patients at elevated caries risk who received the corresponding appropriate risk-based preventative treatments. RESULTS: EHR automated queries identified in three sites 269,536 patients who met the inclusion criteria for receiving a CRA. The overall proportion of patients who received a CRA was 94.4% (eCRA). In eAoC, patients at elevated caries risk levels (moderate, high, or extreme) received fluoride preventive treatment ranging from 56 to 93.8%. For patients at high and extreme risk, antimicrobials were prescribed more frequently site 3 (80.6%) than sites 2 (16.7%) and 1 (2.9%). CONCLUSION: Patient-level data available in the EHRs can be used to implement process-of-care dental eCRA and AoC, eAoC measures identify gaps in clinical practice. EHR-based measures can be useful in improving delivery of evidence-based preventative treatments to reduce risk, prevent tooth decay, and improve oral health.

Development of a Quality Improvement Dental Chart Review Training Program.

INTRODUCTION: Chart review is central to understanding adverse events (AEs) in medicine. In this article, we describe the process and results of educating chart reviewers assigned to evaluate dental AEs. METHODS: We developed a Web-based training program, "Dental Patient Safety Training," which uses both independent and consensus-based curricula, for identifying AEs recorded in electronic health records in the dental setting. Training included (1) didactic education, (2) skills training using videos and guided walkthroughs, (3) quizzes with feedback, and (4) hands-on learning exercises. In addition, novice reviewers were coached weekly during consensus review discussions. TeamExpert was composed of 2 experienced reviewers, and TeamNovice included 2 chart reviewers in training. McNemar test, interrater reliability, sensitivity, specificity, positive predictive value, and negative predictive value were calculated to compare accuracy rates on the identification of charts containing AEs at the start of training and 7 months after consensus building discussions between the 2 teams. RESULTS: TeamNovice completed independent and consensus development training. Initial chart reviews were conducted on a shared set of charts (n = 51) followed by additional training including consensus building discussions. There was a marked improvement in overall percent agreement, prevalence and bias-adjusted κ correlation, and diagnostic measures (sensitivity, specificity, positive predictive value, and negative predictive value) of reviewed charts between both teams from the phase I training program to phase II consensus building. CONCLUSIONS: This study detailed the process of training new chart reviewers and evaluating their performance. Our results suggest that standardized training and continuous coaching improves calibration between experts and trained chart reviewers.

Presence of management, entrepreneurship, leadership and marketing topics in the dental school curriculum in Brazil.

INTRODUCTION: To analyse the presence and characteristics of curricular components related to management, entrepreneurship, leadership and marketing as part of the structure and teaching methods of undergraduate courses in dentistry in Brazil. MATERIALS AND METHODS: This is an observational study that used the Ministry of Education's Undergraduate Course Accreditation Platform, which included 424 undergraduate courses in Dentistry on the last date of collection (August 31 2019). The following items were analysed as follows: the existence of curricular components in relation to the proposed themes, the most recurring denominations of curricular components, minimum and maximum workload, mandatory/optional classification, theoretical/practical teaching condition and in which year the curricular components were inserted. RESULTS: 367/424 (86.6%) of dentistry courses in Brazil included at least one of the topics: management, entrepreneurship, leadership and marketing curricular components in their curriculum, whilst 57/424 (13.4%) did not have these curricular components in their curricular structure. The most frequent names were "Management" 99 (45.21%) and "Entrepreneurship" 80 (36.5%). There was a predominance of the "theoretical method" and the number of hours varied considerably, with the most common course hours between 40 and 60 h. The majority of curricular components were inserted in the third to fifth year and offered on a compulsory basis. CONCLUSION: Most curricular matrices of dentistry courses in Brazil had components related to the topics studied. However, due to the variety of curricular components' names, hours, periods of courses and different teaching methodologies, there is a need to redesign the teaching and learning process, defining educational and evaluation models with common curricular components.

Assessing the completeness of periodontal disease documentation in the EHR: a first step in measuring the quality of care.

BACKGROUND: Our objective was to measure the proportion of patients for which comprehensive periodontal charting, periodontal disease risk factors (diabetes status, tobacco use, and oral home care compliance), and periodontal diagnoses were documented in the electronic health record (EHR). We developed an EHR-based quality measure to assess how well four dental institutions documented periodontal disease-related information. An automated database script was developed and implemented in the EHR at each institution. The measure was validated by comparing the findings from the measure with a manual review of charts. RESULTS: The overall measure scores varied significantly across the four institutions (institution 1 = 20.47%, institution 2 = 0.97%, institution 3 = 22.27% institution 4 = 99.49%, p-value < 0.0001). The largest gaps in documentation were related to periodontal diagnoses and capturing oral homecare compliance. A random sample of 1224 charts were manually reviewed and showed excellent validity when compared with the data generated from the EHR-based measure (Sensitivity, Specificity, PPV, and NPV > 80%). CONCLUSION: Our results demonstrate the feasibility of developing automated data extraction scripts using structured data from EHRs, and successfully implementing these to identify and measure the periodontal documentation completeness within and across different dental institutions.