Identifying Patients' Smoking Status from Electronic Dental Records Data

Smoking is a significant risk factor for initiation and progression of oral diseases. A patient's current smoking status and tobacco dependency can aid clinical decision making and treatment planning. The free-text nature of this data limits accessibility causing obstacles during the time of care and research utility. No studies exist on extracting patient's smoking status automatically from the Electronic Dental Record. This study reports the development and evaluation of an NLP system for this purpose

Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity

Background Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients' smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing. Objective To determine patients' detailed smoking status based on smoking intensity from the EDR. Methods First, the authors created a reference standard of 3,296 unique patients’ smoking histories from the EDR that classified patients based on their smoking intensity. Next, they trained three machine learning classifiers (support vector machine, random forest, and naïve Bayes) using the training set (2,176) and evaluated performances on test set (1,120) using precision (P), recall (R), and F-measure (F). Finally, they applied the best classifier to classify smoking status from an additional 3,114 patients’ smoking histories. Results Support vector machine performed best to classify patients into smokers, nonsmokers, and unknowns (P, R, F: 98%); intermittent smoker (P: 95%, R: 98%, F: 96%); past smoker (P, R, F: 89%); light smoker (P, R, F: 87%); smokers with unknown intensity (P: 76%, R: 86%, F: 81%), and intermediate smoker (P: 90%, R: 88%, F: 89%). It performed moderately to differentiate heavy smokers (P: 90%, R: 44%, F: 60%). EDR could be a valuable source for obtaining patients’ detailed smoking information. Conclusion EDR data could serve as a valuable source for obtaining patients' detailed smoking information based on their smoking intensity that may not be readily available in the EHR

Advances in Teaching & Learning Day Abstracts 2004

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2004

Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records

Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it’s unclear to what extent patient-reported CVD information is accurately captured in Electronic Dental Records (EDRs). In this pilot study, we aimed to measure the reliability of patient-reported CVD conditions in EDRs. We assessed information congruence by comparing patients’ self-reported dental histories to their original diagnosis assigned by their medical providers in the Electronic Medical Record (EMR). To enable this comparison, we encoded patients CVD information from the free-text data of EDRs into a structured format using natural language processing (NLP). Overall, our NLP approach achieved promising performance extracting patients’ CVD-related information. We observed disagreement between self-reported EDR data and physician-diagnosed EMR data

Why Not the Best? Results From the National Scorecard on U.S. Health System Performance, 2011

Assesses the U.S. healthcare system's average performance in 2007-09 as measured by forty-two indicators of health outcomes, quality, access, efficiency, and equity compared with the 2006 and 2008 scorecards and with domestic and international benchmarks

The Relationship Between Periodontal Disease and Obesity: A 5 Year Review

Purpose: To expand upon the current evidence that there is an association between BMI and periodontal disease using a retrospective study design with a larger sample size, and thereby a greater statistical power than previously performed studies. We hypothesize that BMI is positively correlated with prevalence of periodontal disease. Materials and Methods: Data from the electronic health records maintained by the University of Pittsburgh School of Dental Medicine from August 2008 to February 2014 was extracted for variables including age, gender, ethnicity, smoking history, diabetes, probing depths, height, and weight. Multivariate logistic regression was performed to determine the odds ratio for the association between periodontal disease status and all the other variables (BMI, age, ethnicity, gender, smoking status, and diabetes). Results: A total of 27,052 subjects were included in the data set. Multivariate analysis of the data showed that the odds ratio of having periodontal disease with a BMI greater than or equal to 30 versus less than 30 was 1.22 when accounting for all the other confounding variables (p=0.013). Furthermore, sex, age, ethnicity, and smoking were all associated with statistically significant odds ratio for development of periodontal disease when analyzed accounting for the other confounding variables (p<0.001). Only diabetes did not show a statistically significant correlation with periodontal disease (p=0.394). Conclusion: Our results reaffirm that increased BMI is positively correlated with periodontal disease prevalence

Assessment and prevention of behavioural and social risk factors associated with oral cancer: protocol for a systematic review of clinical guidelines and systematic reviews to inform primary care dental professionals

Background: Tobacco and alcohol are recognised as the major risk factors for both oral cavity (mouth) and oropharyngeal (throat) cancers, with increasing acceptance of the role of human papillomavirus (HPV) in the aetiology of oropharyngeal cancers. In addition, there is a significant increased risk for oral cancer among lower socioeconomic groups, males and older age groups. There is a growing evidence for the potential role of primary care professionals in smoking cessation and reducing alcohol-related harm. However, there are uncertainties about the best approaches/strategies to assess risk factors associated with oral cancer, effective components of preventive interventions for behaviour change and implementation strategies in primary care dental settings. Thus, in order to contribute to the prevention of oral cancer effectively, dental professionals need to assess patients on the major risk factors (tobacco, alcohol and HPV/sexual behaviours) and deliver appropriate prevention, taking into account the patient’s sociodemographic context. Aim: The study aims to synthesise evidence on the best practice for undertaking an assessment of major behavioural risk factors associated with oral cancer and delivering effective behaviour change preventive interventions (e.g. advice, counselling, patient recall, signposting/referral to preventive services) by dental professionals in primary care dental settings. Method: The study involves a systematic review and evidence appraisal. We will search for clinical guidelines and systematic reviews from the following databases: Cochrane Library, Ovid MEDLINE, EMBASE, Web of Science, PsychINFO, PubMed, TRIP and Google Scholar. We will also search websites of professional organisations/agencies and bibliographies/reference lists of selected papers. Quality will be assessed with the AGREE II (Appraisal of Guidelines for Research &#38; Evaluation II) instrument for included clinical guidelines and the AMSTAR (A Measurement Tool to Assess Systematic Reviews) and ROBIS instruments for included systematic reviews. The best practice evidence will be assessed via a narrative synthesis of extracted data, considering publication quality. Discussion: This systematic review will synthesise evidence on the best practice for oral cancer risk factor assessment and prevention and evaluate the relationship between available clinical guidelines and the review evidence base. This collation of evidence will be useful for making recommendations for future intervention, research and guideline development

Health Status and Health Care Access of Farm and Rural Populations

Rural residents have higher rates of age-adjusted mortality, disability, and chronic disease than their urban counterparts, though mortality and disability rates vary more by region than by metro status. Contributing negatively to the health status of rural residents are their lower socioeconomic status, higher incidence of both smoking and obesity, and lower levels of physical activity. Contributing negatively to the health status of farmers are the high risks from workplace hazards, which also affect other members of farm families who live on the premises and often share in the work; contributing positively are farmers’ higher socioeconomic status, lower incidence of smoking, and more active lifestyle. Both farm and rural populations experience lower access to health care along the dimensions of affordability, proximity, and quality, compared with their nonfarm and urban counterparts.Health Economics and Policy, agriculture safety and health, electronic health records, farmer health, health, health care access, health care affordability, health care quality, health disparities, health IT, health status, mortality, rural health, telehealth, uninsured,

States' Roles in Shaping High Performance Health Systems

Analyzes results from the State Health Policies Aimed at Promoting Excellent Systems survey and a review of current research on efforts to improve state healthcare systems, with a focus on coverage; quality, safety, and value; and infrastructure