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.

Informatics approaches to improve the quality of dental care.

Despite technological advances, challenges exist in US dental care, including variations in quality of care, access and untreated dental needs. The implementation of learning health systems (LHSs) in dentistry can help to address these challenges. LHSs use robust informatics infrastructure including data and technology to continuously measure and improve the quality and safety of care and can help to reduce costs and improve patient outcomes. The use of EHRs and standardized diagnostic terminologies are highlighted, as they allow for the storage and sharing of patient data, providing a comprehensive view of a patient's medical and dental history, and can be used to identify patterns and trends to improve the delivery of care. The BigMouth Dental Data Repository is an example of an informatic platform that aggregates patient data from multiple institutions and is being used to for scientific inquiry to improve oral health.

Use of the deep learning approach to measure alveolar bone level.

AIM: The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis. MATERIALS AND METHODS: A deep learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cemento-enamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth. A provisional periodontal diagnosis was assigned using the 2018 periodontitis classification. RBL percentage, staging, and presumptive diagnosis were compared with the measurements and diagnoses made by the independent examiners. RESULTS: The average Dice Similarity Coefficient (DSC) for segmentation was over 0.91. There was no significant difference in the RBL percentage measurements determined by DL and examiners ( p=.65 ). The area under the receiver operating characteristics curve of RBL stage assignment for stages I, II, and III was 0.89, 0.90, and 0.90, respectively. The accuracy of the case diagnosis was 0.85. CONCLUSIONS: The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images. However, this model has to be further optimized and validated by a larger number of images to facilitate its application.

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.

Unintended consequences and challenges of quality measurements in dentistry.

BACKGROUND: In recent years, several state dental programs, researchers and the Dental Quality Alliance (DQA) have sought to develop baseline quality measures for dentistry as a way to improve health outcomes, reduce costs and enhance patient experiences. Some of these measures have been tested and validated for various population groups. However, there are some unintended consequences and challenges with quality measurement in dentistry as observed from our previous work on refining and transforming dental quality measures into e-measures. MAIN BODY: Some examples of the unintended consequences and challenges associated with implementing dental quality measures include: a de-emphasis on patient-centeredness with process-based quality measures, an incentivization of unethical behavior due to fee-for-service reimbursement systems, the risk of compromising patient and provider autonomy with plan-level measures, a disproportionate benefits of dental quality measurement going toward payers, and the risk of alienating smaller dental offices due to the resource-intensive nature of quality measurement. CONCLUSION: As our medical counterparts have embraced quality measurement for improved health outcomes, so too must the dental profession. Our ultimate goal is to ensure the delivery of high quality, patient-centered dental care and effective quality measurement is the first step. By continuously monitoring the performance of dental quality measures and their continued refinement when unintended consequences are observed, we can improve patient and population health outcomes.

Assessment of partial-mouth periodontal examination protocols for periodontitis surveillance.

OBJECTIVE: To evaluate bias associated with nine identified partial-mouth periodontal examination (PMPE) protocols in estimating periodontitis prevalence using the periodontitis case definition given by the Centers of Disease Control and Prevention and American Academy of Periodontology (CDC/AAP). MATERIAL AND METHODS: Prevalence from full-mouth examination was determined in a sample of 3667 adults ≥30 years old from the National Health and Nutrition Examination Survey (NHANES) 2009-2010. Prevalence, absolute bias, relative bias, sensitivity and inflation factor were derived for these protocols according to the CDC/AAP definition and half-reduced CDC/AAP definition as ≤50% of sites were measured. RESULTS: Bias in moderate and severe periodontitis prevalence ranged between 11.1-52.5% and 27.1-76.3% for full-mouth mesiobuccal-distolingual protocol and half-mouth mesiobuccal protocol respectively; according to the CDC/AAP definition. With half-reduced CDC/AAP definition, half-mouth four sites protocol provided small absolute bias (3.2%) and relative bias (9.3%) for the estimates of moderate periodontitis prevalence; corresponding biases for severe periodontitis were -1.2% and -10.2%. CONCLUSION: Periodontitis prevalence can be estimated with limited bias when a half-mouth four sites protocol and a half-reduced CDC/AAP case definition are used in combination.

Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach.

OBJECTIVES: This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting. METHODS: Information on periodontitis patients and nineteen factors identified at the initial visit was extracted from electronic health records. The primary outcome is tooth loss phenotype (presence or absence of tooth loss). Prediction models were built on significant factors (single or combinatory) selected by the RuleFit algorithm, and these factors were further adopted by regression models. Model performance was evaluated by Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC). Associations between predictors and the tooth loss phenotype were also evaluated by classical statistical approaches to validate the performance of machine learning models. RESULTS: In total, 7840 patients were included. The machine learning model predicting the tooth loss phenotype achieved AUROC of 0.71 and AUPRC of 0.66. Age, periodontal diagnosis, number of missing teeth at baseline, furcation involvement, and tooth mobility were associated with the tooth loss phenotype in both machine learning and classical statistical models. CONCLUSIONS: The rule-based machine learning approach improves model explainability compared to classical statistical methods. However, the model's generalizability needs to be further validated by external datasets. CLINICAL SIGNIFICANCE: Predictors identified by the current machine learning approach using the RuleFit algorithm had clinically relevant thresholds in predicting the tooth loss phenotype in a large and diverse periodontitis patient cohort. The results of this study will assist clinicians in performing risk assessment for periodontitis at the initial visit.

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.

Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.

OBJECTIVES: This work describes the process by which the quality of electronic health care data for a public health study was determined. The objectives were to adapt, develop, and implement data quality assessments (DQAs) based on the National Institutes of Health Pragmatic Trials Collaboratory (NIHPTC) data quality framework within the three domains of completeness, accuracy, and consistency, for an investigation into oral health care disparities of a preventive care program. METHODS: Electronic health record data for eligible children in a dental accountable care organization of 30 offices, in Oregon, were extracted iteratively from January 1, 2014, through March 31, 2022. Baseline eligibility criteria included: children ages 0-18 with a baseline examination, Oregon home address, and either Medicaid or commercial dental benefits at least once between 2014 and 2108. Using the NIHPTC framework as a guide, DQAs were conducted throughout data element identification, extraction, staging, profiling, review, and documentation. RESULTS: The data set included 91,487 subjects, 11 data tables comprising 75 data variables (columns), with a total of 6,861,525 data elements. Data completeness was 97.2%, the accuracy of EHR data elements in extracts was 100%, and consistency between offices was strong; 29 of 30 offices within 2 standard deviations of the mean (s = 94%). CONCLUSIONS: The NIHPTC framework proved to be a useful approach, to identify, document, and characterize the dataset. The concepts of completeness, accuracy, and consistency were adapted by the multidisciplinary research team and the overall quality of the data are demonstrated to be of high quality.

Assessing periodontitis in populations: a systematic review of the validity of partial-mouth examination protocols.

OBJECTIVE: To estimate bias associated with partial-mouth periodontal examination (PMPE) protocols regarding estimates of prevalence, severity and extent of clinical attachment loss (CAL), pocket depth (PD) and gingival recession (REC). MATERIAL AND METHODS: A search was made for articles published in English, from 1946 to 2012, which compared PMPE versus full-mouth periodontal examination protocols for CAL or PD ≥ 4 mm or REC ≥3 mm thresholds. PMPE protocols were evaluated for sensitivity of estimates of periodontitis prevalence, relative biases for severity and extent estimates. RESULTS: A review of the literature identified 12 studies which reported 32 PMPE protocols. Three PMPE protocols which had sensitivities ≥85% and relative biases ≤0.05 in absolute values for severity and extent estimates were as follows: (1) half-mouth six-sites, (2) diagonal quadrants six-sites and (3) full-mouth mesiobuccal-midbuccal-distobuccal (MB-B-DB). Two other PMPE protocols (full-mouth and half-mouth mesiobuccal-midbuccal-distolingual) performed well for prevalence and severity of periodontitis; however, their performance in estimates of extent was unknown. CONCLUSIONS: Among the 32 PMPE protocols listed, the half-mouth six-sites and full-mouth MB-B-DB protocols had the highest sensitivities for prevalence estimates and lowest relative biases for severity and extent estimates.

Identifying predictors of tooth loss using a rule-based machine learning approach: A retrospective study at university-setting clinics.

BACKGROUND: This study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach. METHODS: Information on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two-step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root-mean-squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model. RESULTS: In total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models. CONCLUSION: The two-step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule-based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.

Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research.

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.

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.

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records.

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the importance of seed quality rather than quantity in feeding NER models. This research reports on an efficient and accurate way to mine clinical notes for periodontal diagnoses, allowing researchers to easily and quickly build a NER model with the prompt generation approach.

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.

Assessing the delivery of comprehensive care at a dental school.

A key mission of a dental school is to train students to be competent dentists through the delivery of comprehensive care to patients. Comprehensive care is defined as a seamless and integrated dental treatment that addresses all patients' dental needs. Identification of a health care problem is the essential first step in quality improvement to medical education curriculum and its outcomes. It is critical for students to receive adequate clinical experience and for patients to receive needed treatment. This study assessed the degree to which comprehensive care was delivered from the patient and student perspective, and to determine why patients discontinue their course of treatment. We conducted a retrospective analysis of electronic health record (EHR) data in one group practice at the University of Texas School of Dentistry at Houston. Semistructured interviews of patients, students and faculty were also conducted. The results showed that 29% of assessed and admitted patients received comprehensive care. A large proportion of dropouts occurred after the third or fourth visit. It took on average 9.8 visits and 210 days for patients to complete their planned treatments. Dental students had a patient family of 25-29 patients, delivered 75% of their care in their fourth year, and predominantly provided restorative treatments compared with other dental disciplines. Interview transcripts were analyzed to determine strengths, weaknesses, and opportunities relating to the provision of comprehensive care. Patients perceived that they received cost effective and high quality care. Students and faculty provided suggestions for streamlining care. Findings from both the retrospective analysis of EHR data and semi-structured interviews revealed several areas for improvement. One solution that was subsequently piloted was to combine the separate assessment and treatment planning appointments into a single all-day session to reduce patient dropouts. During the pilot period over the summer session, 84 patients were scheduled in the combined assessment and treatment planning session. Of this population, 69% percent were accepted and deemed suitable for undergraduate care. And 83% among those accepted received a treatment plan on the first appointment. In the future we expect to integrate more formal evidence-based exercises and reassess the impact of these changes in improving educational and clinical care outcomes. In addition we expect to adopt evidence-based solutions and reassess the impact of these changes in improving educational and clinical care outcomes.

An Observational Retrospective Study of Adverse Events and Behavioral Outcomes During Pediatric Dental Sedation.

Purpose: The purpose of this study was to examine a university-based dental electronic health records (EHR) database to identify sedation-related adverse events (AEs) and assess patients' behavioral outcomes during routine pediatric dental sedations (PDSs) in a dental school clinic. Methods: A database was screened for patients younger than 18 years old who had received dental sedation in 2019. The qualifying EHRs were then accessed and sedations were reviewed for AEs, which were categorized using a 12-point classification system and the Tracking and Reporting Outcomes of Procedural Sedation Tool. Patient behaviors were assessed using provider progress notes and categorized as presence/ absence of agitation. Results: A total of 690 sedations were reviewed, yielding 28 AEs. Emesis was the most common AE observed in 1.3 percent of sedations. Respiratory and cardiovascular AEs were observed in 0.7 percent and 0.6 percent of sedations, respectively. Agitation was identified in 47.5 percent of sedations, while 34.1 percent of agitations resulted in the documented suspension of dental treatment. Agitation was mainly observed for nitrous oxide and oral sedation resulting in one failed sedation out of five sedations for each method. Conclusions: Potentially serious adverse effects were identified during pediatric dental sedations, but their incidence was low. A significant proportion of the sedated children experienced agitation, resulting in some sedation failures. Such events need to be tracked and examined for risk assessment reduction and quality-of-care improvement.

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.

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.

BigMouth: development and maintenance of a successful dental data repository.

Few clinical datasets exist in dentistry to conduct secondary research. Hence, a novel dental data repository called BigMouth was developed, which has grown to include 11 academic institutions contributing Electronic Health Record data on over 4.5 million patients. The primary purpose for BigMouth is to serve as a high-quality resource for rapidly conducting oral health-related research. BigMouth allows for assessing the oral health status of a diverse US patient population; provides rationale and evidence for new oral health care delivery modes; and embraces the specific oral health research education mission. A data governance framework that encouraged data sharing while controlling contributed data was initially developed. This transformed over time into a mature framework, including a fee schedule for data requests and allowing access to researchers from noncontributing institutions. Adoption of BigMouth helps to foster new collaborations between clinical, epidemiological, statistical, and informatics experts and provides an additional venue for professional development.