The PocketPerio application significantly increases the accuracy of diagnosing periodontal conditions in didactic and chairside settings.

The study aimed to determine the accuracy of diagnosing periodontal conditions using the developed web-based PocketPerio application and evaluate the user's perspective on the use of PocketPerio. First, 22 third-year dental students (DS3) diagnosed ten cases without PocketPerio (control) and with PocketPerio (test) during a mock examination. Then, 105 DS3, 13 fourth-year dental students (DS4), and 32 senior second-year International Standing Program students (ISP2) used PocketPerio chairside. Statistical analysis was performed using a non-parametric paired two-tailed test of significance with the Wilcoxon matched-pairs signed rank test. The null hypothesis that PocketPerio did not increase the accuracy of periodontal diagnoses was rejected at α < 0.01. Periodontal diagnoses made using PocketPerio correlated with those made by periodontics faculty ("gold standard") in all cases. During the mock examination, PocketPerio significantly increased the accuracy of periodontal diagnoses compared to the control (52.73 vs. 13.18%, respectively). Chairside, PocketPerio significantly increased the accuracy of primary (100 vs. 40.0%) and secondary (100 vs. 14.25%) periodontal diagnoses compared to the respective controls. Students regardless of their training year felt more confident in diagnosing periodontal conditions using PocketPerio than their current tools, provided positive feedback on its features, and suggested avenues for its further development.

A retrospective analysis of the role of age and sex in outcomes of non-surgical periodontal therapy at a single academic dental center.

The present study examined the role of age and sex in the outcomes of non-surgical periodontal therapy (NSPT). De-identified demographic and periodontal characteristics of patients who presented for baseline periodontal evaluation, NSPT, and periodontal re-evaluation were abstracted from electronic health records. Independent associations of age and sex with severe periodontitis defined as ≥ 5 mm clinical attachment loss (CAL) and ≥ 6 mm probing depth (PD) were determined using multinomial logistic regression. The null hypothesis was rejected at α < 0.05. A total of 2866 eligible subjects were included in the analysis. Significantly lower odds of CAL ≤ 4 mm than CAL ≥ 5 mm (reference) were observed in adults aged 35-64 (odds ratio, OR, 0.19; 95% confidence interval, CI 0.13, 0.29) and ≥ 65 years (OR 0.13; 95% CI 0.07, 0.25) compared to those aged 18-34 years. Odds of PD < 4 mm versus PD ≥ 6 mm (reference) were lower in adults aged 35-64 years than those aged 18-34 years (OR 0.71; 95% CI 0.55, 0.90) and higher in females compared to males (OR 1.67; 95% CI 1.14, 2.44). These results suggest more compromised post-NSPT outcomes in older adults and males compared to the respective populations and highlight the need for personalized therapeutic strategies in these populations.

How decisions are made: Antibiotic stewardship in dentistry.

BACKGROUND: We performed a preimplementation assessment of workflows, resources, needs, and antibiotic prescribing practices of trainees and practicing dentists to inform the development of an antibiotic-stewardship clinical decision-support tool (CDST) for dentists. METHODS: We used a technology implementation framework to conduct the preimplementation assessment via surveys and focus groups of students, residents, and faculty members. Using Likert scales, the survey assessed baseline knowledge and confidence in dental providers' antibiotic prescribing. The focus groups gathered information on existing workflows, resources, and needs for end users for our CDST. RESULTS: Of 355 dental providers recruited to take the survey, 213 (60%) responded: 151 students, 27 residents, and 35 faculty. The average confidence in antibiotic prescribing decisions was 3.2 ± 1.0 on a scale of 1 to 5 (ie, moderate). Dental students were less confident about prescribing antibiotics than residents and faculty (P < .01). However, antibiotic prescribing knowledge was no different between dental students, residents, and faculty. The mean likelihood of prescribing an antibiotic when it was not needed was 2.7 ± 0.6 on a scale of 1 to 5 (unlikely to maybe) and was not meaningfully different across subgroups (P = .10). We had 10 participants across 3 focus groups: 7 students, 2 residents, and 1 faculty member. Four major themes emerged, which indicated that dentists: (1) make antibiotic prescribing decisions based on anecdotal experiences; (2) defer to physicians' recommendations; (3) have limited access to evidence-based resources; and (4) want CDST for antibiotic prescribing. CONCLUSIONS: Dentists' confidence in antibiotic prescribing increased by training level, but knowledge did not. Trainees and practicing dentists would benefit from a CDST to improve appropriateness of antibiotic prescribing.

DentaSeal: A school-based dental sealant efficiency assessment tool to support statewide monitoring and reporting: A field report.

BACKGROUND: The evidence base supports effectiveness of dental sealants for prevention of childhood caries in school-aged children. OBJECTIVE: This study describes planning, development, usability testing and outcomes following implementation of DentaSeal, a web-based application designed to accurately track unique student data and generate reports for all Wisconsin school-based sealant placement (SP) programs. METHODS: Application software development was informed by a steering committee of representative stakeholders who were interviewed to inform design and provide feedback for design of DentaSeal during development and evaluation. Software development proceeded based on wireframes developed to build architectural design. Usability testing followed and informed any required adjustments to the application. The DentaSeal prototype was beta tested and fully implemented subsequently in the public health sector. RESULTS: The DentaSeal application demonstrated capacity to: 1) track unique student SP data and longitudinal encounter history, 2) generate reports and 3) support administrative tracking. In 2019, DentaSeal captured SP data of 47 school-based programs in Wisconsin that sponsored > 7,000 program visits for 184,000 children from 62 counties. Delivery of > 548,000 SP services were catalogued. CONCLUSIONS: For public health initiatives targeting reduction in caries incidence, web-based applications such as DentaSeal represent useful longitudinal tracking tools for cataloguing SP in school-based program participants.

Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach.

Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall-precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care.

How Medical-Dental EHR Integration Can Improve Diabetes Care.

Since the mid-1990s, poor oral health has been neglected as a public health threat, despite its recognition as epidemic in scale by the US Department of Health and Human Services Office of the Surgeon General. Americans' poor oral health influences their overall health and, from a population standpoint, incurs dire economic and human costs. This article describes how health information transfer within the Marshfield Clinic Health System's integrated medical and dental practice can improve diabetes care. This article also considers ethics and equity implications of improving MDP electronic health record interoperability in this large, rural Wisconsin organization.

Systematic review of studies examining contribution of oral health variables to risk prediction models for undiagnosed Type 2 diabetes and prediabetes.

OBJECTIVE: To conduct systematic review applying "preferred reporting items for systematic reviews and meta-analyses statement" and "prediction model risk of assessment bias tool" to studies examining the performance of predictive models incorporating oral health-related variables as candidate predictors for projecting undiagnosed diabetes mellitus (Type 2)/prediabetes risk. MATERIALS AND METHODS: Literature searches undertaken in PubMed, Web of Science, and Gray literature identified eligible studies published between January 1, 1980 and July 31, 2018. Systematically reviewed studies met inclusion criteria if studies applied multivariable regression modeling or informatics approaches to risk prediction for undiagnosed diabetes/prediabetes, and included dental/oral health-related variables modeled either independently, or in combination with other risk variables. RESULTS: Eligibility for systematic review was determined for seven of the 71 studies screened. Nineteen dental/oral health-related variables were examined across studies. "Periodontal pocket depth" and/or "missing teeth" were oral health variables consistently retained as predictive variables in models across all systematically reviewed studies. Strong performance metrics were reported for derived models by all systematically reviewed studies. The predictive power of independently modeled oral health variables was marginally amplified when modeled with point-of-care biological glycemic measures in dental settings. Meta-analysis was precluded due to high inter-study variability in study design and population diversity. CONCLUSIONS: Predictive modeling consistently supported "periodontal measures" and "missing teeth" as candidate variables for predicting undiagnosed diabetes/prediabetes. Validation of predictive risk modeling for undiagnosed diabetes/prediabetes across diverse populations will test the feasibility of translating such models into clinical practice settings as noninvasive screening tools for identifying at-risk individuals following demonstration of model validity within the defined population.

Identifying oral disease variables associated with pneumonia emergence by application of machine learning to integrated medical and dental big data to inform eHealth approaches.

Background: The objective of this study was to build models that define variables contributing to pneumonia risk by applying supervised Machine Learning-(ML) to medical and oral disease data to define key risk variables contributing to pneumonia emergence for any pneumonia/pneumonia subtypes. Methods: Retrospective medical and dental data were retrieved from Marshfield Clinic Health System's data warehouse and integrated electronic medical-dental health records (iEHR). Retrieved data were pre-processed prior to conducting analyses and included matching of cases to controls by (a) race/ethnicity and (b) 1:1 Case: Control ratio. Variables with >30% missing data were excluded from analysis. Datasets were divided into four subsets: (1) All Pneumonia (all cases and controls); (2) community (CAP)/healthcare associated (HCAP) pneumonias; (3) ventilator-associated (VAP)/hospital-acquired (HAP) pneumonias and (4) aspiration pneumonia (AP). Performance of five algorithms were compared across the four subsets: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Random Forests. Feature (input variables) selection and ten-fold cross validation was performed on all the datasets. An evaluation set (10%) was extracted from the subsets for further validation. Model performance was evaluated in terms of total accuracy, sensitivity, specificity, F-measure, Mathews-correlation-coefficient and area under receiver operating characteristic curve (AUC). Results: In total, 6,034 records (cases and controls) met eligibility for inclusion in the main dataset. After feature selection, the variables retained in the subsets were: All Pneumonia (n = 29 variables), CAP-HCAP (n = 26 variables); VAP-HAP (n = 40 variables) and AP (n = 37 variables), respectively. Variables retained (n = 22) were common across all four pneumonia subsets. Of these, the number of missing teeth, periodontal status, periodontal pocket depth more than 5 mm and number of restored teeth contributed to all the subsets and were retained in the model. MLP outperformed other predictive models for All Pneumonia, CAP-HCAP and AP subsets, while SVM outperformed other models in VAP-HAP subset. Conclusion: This study validates previously described associations between poor oral health and pneumonia. Benefits of an integrated medical-dental record and care delivery environment for modeling pneumonia risk are highlighted. Based on findings, risk score development could inform referrals and follow-up in integrated healthcare delivery environment and coordinated patient management.

ESTABLISHING A QUALITY IMPROVEMENT CULTURE WITHIN A LARGE INTEGRATED MEDICAL-DENTAL HEALTH SYSTEM WITH A POPULATION BASED FOCUS.

OBJECTIVES: Quality improvement strategies have been an integral part of healthcare to attain improved care delivery and effective health outcomes. The dental quality initiative improvement (DQII) presented in this manuscript represents a case study of successful implementation of a quality improvement culture within a large integrated-medical-dental health system serving a largely rural population. METHODS: The key elements of DQII included steering committee establishment, definition or dental quality measures and development/implementation of a dental quality analytics dashboard (DQAD) that provides relevant data on dental quality measures. Qualitative metrics were applied to look at the improvement in performance for the various measures relative to quality benchmarks. RESULTS: DQII facilitated improved oversight of care continuity and provider performance surrounding quality measures at granular and/or institutional level. Improvement associated with care delivery performance relative to benchmarks was observed. CONCLUSIONS: DQII further advanced the quality improvement culture prevalent in our learning healthcare environment with its focus on value-based care delivery. DQII initiative and establishment of DQAD provided ability to track performance in operational care delivery for dental providers in a clinical setting in real time.

Community Awareness and Oral Cancer Screening in Rural Wisconsin.

Purpose: Oral cancer risks have been shown to be modified by improving public awareness and reducing barriers to preventive care. The purpose for this study was to assess oral cancer knowledge and awareness and provide oral cancer screenings and education to a population of rural farmers in Wisconsin.Methods: Attendees 18 years of age and older at a rural farming exposition in Wisconsin were invited to complete a 12-item oral cancer awareness paper survey and to receive a visual and tactile head and neck examination/ oral cancer screening. Completing both the survey and the screening were optional. Participants also received educational materials on oral cancer. Individuals with abnormal lesions were provided with dental referrals.Results: A total of 236 attendees consented to participate either the survey or oral cancer screening (n=236). Most (72%) reported seeing a dentist in the past six months regardless of insurance status. In spite of having had recent dental encounters, only 28% of women and 46% of men were able to identify at least one risk factor associated with oral cancer. Among participants consenting to the oral cancer screening (n=194), 17% (n=33) presented with oral lesions requiring additional assessment and were recommended for follow-up care.Conclusions: Knowledge and awareness of oral cancer risk factors, signs and symptoms was low among the participants in this rural population despite high rates of dental care access. Oral cancer screenings and education provided in varied settings could improve oral cancer knowledge and awareness and early detection of malignant oral lesions in rural communities.

Knowledge, Attitudes, Behaviors of Women Related to Pregnancy, and Early Childhood Caries Prevention: A Cross-Sectional Pilot Study.

OBJECTIVE: Health education interventions during pregnancy can influence maternal oral health (OH), maternal OH-behaviors and children's OH. Interventions that can be delivered at anytime and anywhere, for example mobile-health (mHealth) provides an opportunity to address challenges of health education and support activation of women in underserved and rural communities to modify their health behavior. This pilot study was undertaken as a part of a mHealth initiative to determine knowledge, attitudes, and behaviors related to pregnancy and ECC prevention among women attending obstetrics/gynecology (OB/GYN) practices at a large rurally-based clinic. METHODS: A cross-sectional survey study was voluntarily engaged by women (n = 191) aged 18 to 59 years attending OB/GYN visits, over a 3-week period from 12/2019 to 1/2020. Survey results were analyzed applying descriptive statistics, X2 and Fisher's Exact tests. The significance level was set at P < .0001 for all analyses. RESULTS: Approximately half of respondents were between 18 and 29 years (53%), had a college degree (55%), and 100% reported cell phone use. Whereas 53% and 31%, respectively, indicated that they were "somewhat" or "very" sure of how to prevent ECC in their children, only 9% recognized evidence of early decay and 30% did not know the purpose of fluoride. Overall, only 27% of participants correctly answered the knowledge-based questions. Further, only 57% reported their provider explained things in a way that was easy to understand. Only 24% reported seeing a dentist during their current pregnancy. CONCLUSIONS: Study results suggested potential gaps in knowledge and behaviors related to ECC prevention and provided baseline data to inform future interventions to improve ECC prevention practices. Notably, majority of participants used their cell phones for making medical/dental appointments and reported using their phones to look up health-related information. This demographic represents a potentially receptive target for mHealth approaches to improve understanding of oral health maintenance during pregnancy and ECC prevention.

Longitudinal Observation of Outcomes and Patient Access to Integrated Care Following Point-of-Care Glycemic Screening in Community Health Center Dental Safety Net Clinics.

Introduction: Rates of diabetes/prediabetes continue to increase, with disparity populations disproportionately affected. Previous field trials promoted point-of-care (POC) glycemic screening in dental settings as an additional primary care setting to identify potentially at-risk individuals requiring integrated care intervention. The present study observed outcomes of POC hemoglobin A1c (HbA1c) screening at community health center (CHC) dental clinics (DC) and compliance with longitudinal integrated care management among at-risk patients attending dental appointments. Materials and Methods: POC HbA1c screening utilizing Food and Drug Administration (FDA)-approved instrumentation in DC settings and periodontal evaluation of at-risk dental patients with no prior diagnosis of diabetes/prediabetes and no glycemic testing in the preceding 6 months were undertaken. Screening of patients attending dental appointments from October 24, 2017, through September 24, 2018, was implemented at four Wisconsin CHC-DCs serving populations with a high representation of disparity. Subjects meeting at-risk profiles underwent POC HbA1c screening. Individuals with measures in the diabetic/prediabetic ranges were advised to seek further medical evaluation and were re-contacted after 3 months to document compliance. Longitudinal capture of glycemic measures in electronic health records for up to 2 years was undertaken for a subset (n = 44) of subjects with available clinical, medical, and dental data. Longitudinal glycemic status and frequency of medical and dental access for follow-up care were monitored. Results: Risk assessment identified 224/915 (24.5%) patients who met inclusion criteria following two levels of risk screening, with 127/224 (57%) qualifying for POC HbA1c screening. Among those tested, 62/127 (49%) exhibited hyperglycemic measures: 55 in the prediabetic range and seven in the diabetic range. Moderate-to-severe periodontitis was more prevalent in patients with prediabetes/diabetes than in individuals with measures in the normal range. Participant follow-up compliance at 3 months was 90%. Longitudinal follow-up documented high rates of consistent access (100 and 89%, respectively), to the integrated medical/DC environment over 24 months for individuals with hyperglycemic screening measures. Conclusion: POC glycemic screening revealed elevated HbA1c measures in nearly half of at-risk CHC-DC patients. Strong compliance with integrated medical/dental management over a 24-month interval was observed, documenting good patient receptivity to POC screening in the dental setting and compliance with integrated care follow-up by at-risk patients.

Positioning operations in the dental safety net to enhance value-based care delivery in an integrated health-care setting.

OBJECTIVES: Impact of implementing data-driven performance metric-tracking across a 10-dental center infrastructure established by Family Heath Center of Marshfield (FHC-M) was examined for relative impact on achieving value-based care delivery in serving a patient population characterized by 88% Medicaid representation. METHODS: To track progress toward national benchmarks for preventive care delivery, dental quality analytics dashboard tracking was implemented in real time with sharing of performance metrics across centers. Compliance rate with Uniform Data Systems reporting requirements for sealant placement on permanent first molars in children aged 6-9 years of age at moderate-to-high risk of caries was targeted at FHC-M dental centers for comparison with those of other community health centers statewide and nationally. Hygienist-to-dentist ratio to support robust sealant placement capacity was further examined. RESULTS: Uniform Data Systems data for rate of sealant placement between 2016-2018 revealed that FHC-M consistently exceeded rates reported statewide and nationally. For this quality indicator, performance across all dental practices in 27 states reported by Centers for Medicare and Medicaid Services in 2018 achieved 23% in 2017 compared to 73% and 52% placement rates reported by FHC-M and community health centers, respectively. A 1:1 hygienist-to-dentist was documented across FHC-M dental centers compared to 0.5:1 reported nationally. CONCLUSIONS: Implementation of quality metric dashboard and a 1:1 dentist-to-hygienist ratio supported realization of value-based dental care delivery relative to caries prevention in a moderate-to-high risk pediatric Medicaid population through achievement of robust sealant placement. Importance of adequate hygienist staffing, "same day" sealant placement and performance feedback supported by technology are highlighted.

Feasibility of establishing tele-dental approach to non-traumatic dental emergencies in medical settings.

PURPOSE: Non-traumatic dental condition visits (NTDCs) represent about 1.4% to 2% of all Emergency Department (ED) visits and are limited to palliative care only, while associated with high cost of care. Feasibility of establishing a tele-dental approach to manage NTDCs in ED and Urgent care (UC) settings was undertaken to explore the possibility of utilizing remote tele-dental consults. METHODS: Participants with NTDCs in ED/UCs were examined extra and intra-orally: (1) directly by ED provider, (2) remotely by tele-dental examiner (trained dentist) using intra-oral camera and high-definition pan-tilt-zoom (PTZ) camera, (3) directly by treating dentist post ED/UC visit (if applicable) and, (4) secondary assessment by tele-dental reviewer. Comparisons were drawn between differential diagnoses and recommended managements provided by ED/UC providers, tele-dental examiner, treating dentist, and tele-dental reviewer. RESULTS: 13 patients participated in the study. The overall inter-rater agreement between the tele-dental examiner and tele-dental reviewer was high while it was low between tele-dentists and the ED providers. The preliminary testing of tele-dental intervention in the ED/UC setting demonstrated potential feasibility in addressing the NTDC landing in ED/UC. Larger interventional studies in multi-site setting are needed to validate this approach and especially evaluate impact on cost, ED/UC workflow and patient outcomes. CLINICAL SIGNIFICANCE: Using tele-dentistry to triage non-traumatic dental visits to the emergency room may be a promising approach. Once this approach is validated through a larger study, tele-dental outreach could help in directing non-traumatic dental emergency patients to the appropriate dental setting to provide treatment for the patients.

Patient Awareness of Association of Diabetes and Periodontal Disease.

This cross-sectional study sought to assess the current awareness, knowledge, and behavior regarding diabetes mellitus (DM) and periodontal disease (PD) association among a convenience sample of patients from a large Wisconsin-based integrated medical-dental health care organization serving largely rurally based communities. An anonymous 10-question survey was distributed at regional medical and dental centers of dental and medical clinics of a single health care institution over a 4-week period, to achieve a cross-sectional sampling of patients aged 18 to 80 years. Among 946 respondents, 616 were female. Patient-reported periodicity for dental visits was highest between 6 months and 1 year (56.4%). Respondents reporting "poor-fair" knowledgeability surrounding DM-PD association correlated with highest interest in learning more about DM-PD relationship (p <.0001). While over 80% of respondents correctly answered questions about gum disease symptomology and contribution of oral health practices on diabetes prevention, only 51% knew that PD affected blood sugar control. Willingness to comply with medical screening conducted by dental providers for diseases affecting oral health was indicated by 44% of respondents (p < .0001). Study results indicated that knowledgeability levels among patients surrounding the effect of PD on DM needed improvement. Strategic educational interventions targeting improved health literacy among patients may further promote prevention of DM-PD complications. Health literacy gaps remain to be addressed in patient understanding of the importance of detecting and managing dysglycemia for maintenance of periodontal health, creating opportunities for patient education.

Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting.

BACKGROUND: Periodontitis (PD), a form of gum disease, is a major public health concern as it is globally prevalent and harms both individual quality of life and economic productivity. Global cost in lost productivity is estimated at US$54 billion annually. Moreover, current PD assessment applies only after the damage has already occurred. OBJECTIVE: This study proposes and tests a new PD risk assessment model applicable at point-of-care, using supervised machine learning methods. METHODS: We compare the performance of five algorithms using retrospective clinical data: Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT). RESULTS: DT and ANN demonstrated higher accuracy in classifying the patients with high or low PD risk as compared to NB, LR and SVM. The resultant model with DT showed a sensitivity of 87.08% (95% CI 84.12% to 89.76%) and specificity of 93.5% (95% CI 91% to 95.49%). CONCLUSIONS: A predictive model with high sensitivity and specificity to stratify individuals into low and high PD risk tiers was developed. Validation in other populations will inform translational value of this approach and its potential applicability as clinical decision support tool.

Knowledgeability, attitude, and practice behaviors of primary care providers toward managing patients' oral health care in medical practice: Wisconsin statewide survey.

BACKGROUND: In this study, the authors sought to explore the receptivity, preparedness, and rates of adoption of integrated medical-dental models of care (MOCs) in the practice setting among primary care providers (PCPs) treating patients with diabetes mellitus (DM). METHODS: The authors conducted an anonymous statewide survey targeting PCPs across a range of Wisconsin-based practice settings to evaluate knowledgeability, attitude, practice behaviors, and perceived barriers to oral health screening in a medical setting. Qualitative analytical approaches included thematic analyses applied to evaluate the status of and barriers to integrated medical-dental MOC adoption. RESULTS: The integrated medical-dental MOC adoption rate was 34%. Top perceived barriers to integrated medical-dental MOC adoption included insurance coverage (71%) and care access (70%). A total of 39% indicated competency for educating patients about the association between DM and periodontitis. Although 72% of PCPs indicated optimal periodicity for oral health assessment as frequent, 39% reported frequently conducting such assessments. CONCLUSIONS: Although PCPs indicate receptivity to integrated medical-dental MOCs, PCPs identify suboptimal education, lack of adequate training in oral-systemic disease assessment, and barriers to oral health care access as barriers to integrated medical-dental MOC adoption. PRACTICAL IMPLICATIONS: Integrated medical-dental MOC adoption in care delivery to patients with DM remains below average. Interdisciplinary efforts and education are needed to address identified barriers to care integration.

Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment.

The objective was to develop a predictive model using medical-dental data from an integrated electronic health record (iEHR) to identify individuals with undiagnosed diabetes mellitus (DM) in dental settings. Retrospective data retrieved from Marshfield Clinic Health System's data-warehouse was pre-processed prior to conducting analysis. A subset was extracted from the preprocessed dataset for external evaluation (Nvalidation) of derived predictive models. Further, subsets of 30%-70%, 40%-60% and 50%-50% case-to-control ratios were created for training/testing. Feature selection was performed on all datasets. Four machine learning (ML) classifiers were evaluated: logistic regression (LR), multilayer perceptron (MLP), support vector machines (SVM) and random forests (RF). Model performance was evaluated on Nvalidation. We retrieved a total of 5319 cases and 36,224 controls. From the initial 116 medical and dental features, 107 were used after performing feature selection. RF applied to the 50%-50% case-control ratio outperformed other predictive models over Nvalidation achieving a total accuracy (94.14%), sensitivity (0.941), specificity (0.943), F-measure (0.941), Mathews-correlation-coefficient (0.885) and area under the receiver operating curve (0.972). Future directions include incorporation of this predictive model into iEHR as a clinical decision support tool to screen and detect patients at risk for DM triggering follow-ups and referrals for integrated care delivery between dentists and physicians.

Patient awareness/knowledge towards oral cancer: a cross-sectional survey.

BACKGROUND: Oral cancer (OC) is associated with multiple risk factors and high mortality rates and substantially contributes to the global cancer burden despite being highly preventable. This cross-sectional study sought to assess current knowledge, awareness, and behaviors of patients in rural communities surrounding OC risk. METHODS: An anonymous 21-question survey was distributed to patients in waiting rooms of a large integrated medical-dental health system serving north-central Wisconsin. Survey results were summarized via descriptive statistics. Odds ratios surrounding health literacy on OC risk factors were obtained using unconditional univariate logistic regression analysis. RESULTS: Of 504 dental and 306 medical patients completing the survey, 62.2% were female, Caucasian/White (92%) with 41% having a ≤ high school diploma/equivalent. Current smoker/smokeless tobacco use was reported by 34%, while 39% reported former tobacco exposure. Alcohol use was reported by 54% of respondents at the following frequencies: < once/week, (35%); 1-2 times/week, (16%); 3-4 times/week, (6%); 5-6 times/week, (2%); and daily, (23%). Knowledge about tobacco and alcohol use and increased OC risk was reported by 94 and 40%, respectively. About 50% reported knowledgeability regarding cancer-associated symptomology. Tobacco cessation was reported by 20% of responders. Receipt of education on OC from healthcare providers and human papilloma virus links to OC causation was reported by 38 and 21%, respectively. CONCLUSION: Patients who smoked > 20+ cigarettes per day were more knowledgeable about tobacco and OC risk compared to non-smokers and those who smoked ≤ 19 cigarettes/day (p = 0.0647). Patients who were alcohol consumers exhibited higher knowledgeability surrounding increased OC risk with alcohol and tobacco exposures compared to alcohol abstainers (p = 0.06). We concluded that patients recognized links between tobacco and OC risk but demonstrated lower knowledge of other causal factors. Strategic patient education by providers could increase awareness of OC risk.

Tobacco use status from clinical notes using Natural Language Processing and rule based algorithm.

BACKGROUND: This cross-sectional retrospective study utilized Natural Language Processing (NLP) to extract tobacco-use associated variables from clinical notes documented in the Electronic Health Record (EHR). OBJECITVE: To develop a rule-based algorithm for determining the present status of the patient's tobacco-use. METHODS: Clinical notes (n= 5,371 documents) from 363 patients were mined and classified by NLP software into four classes namely: "Current Smoker", "Past Smoker", "Nonsmoker" and "Unknown". Two coders manually classified these documents into above mentioned classes (document-level gold standard classification (DLGSC)). A tobacco-use status was derived per patient (patient-level gold standard classification (PLGSC)), based on individual documents' status by the same two coders. The DLGSC and PLGSC were compared to the results derived from NLP and rule-based algorithm, respectively. RESULTS: The initial Cohen's kappa (n= 1,000 documents) was 0.9448 (95% CI = 0.9281-0.9615), indicating a strong agreement between the two raters. Subsequently, for 371 documents the Cohen's kappa was 0.9889 (95% CI = 0.979-1.000). The F-measures for the document-level classification for the four classes were 0.700, 0.753, 0.839 and 0.988 while the patient-level classifications were 0.580, 0.771, 0.730 and 0.933 respectively. CONCLUSIONS: NLP and the rule-based algorithm exhibited utility for deriving the present tobacco-use status of patients. Current strategies are targeting further improvement in precision to enhance translational value of the tool.