Designing Clinical Data Presentation Using Cognitive Task Analysis Methods

Despite the many decades of research on effective use of clinical systems in medicine, the adoption of health information technology to improve patient care continues to be slow especially in ambulatory settings. This applies to dentistry as well, a primary care discipline with approximately 137,000 practicing dentists in the United States. One critical reason is the poor usability of clinical systems, which makes it difficult for providers to navigate through the system and obtain an integrated view of patient data during patient care. Cognitive science methods have shown significant promise to meaningfully inform and formulate the design, development and assessment of clinical information systems. Most of these methods were applied to evaluate the design of systems after they have been developed. Very few studies, on the other hand, have used cognitive engineering methods to inform the design process for a system itself. It is this gap in knowledge – how cognitive engineering methods can be optimally applied to inform the system design process – that this research seeks to address through this project proposal. This project examined the cognitive processes and information management strategies used by dentists during a typical patient exam and used the results to inform the design of an electronic dental record interface. The resulting 'proof of concept' was evaluated to determine the effectiveness and efficiency of such a cognitively engineered and application flow design. The results of this study contribute to designing clinical systems that provide clinicians with better cognitive support during patient care. Such a system will contribute to enhancing the quality and safety of patient care, and potentially to reducing healthcare costs

Characterizing Restorative Dental Treatments of Sjögren's Syndrome Patients Using Electronic Dental Records Data

Scant knowledge exists on the type of restorative treatments Sjögren's syndrome patients (SSP) receive in spite of their high dental disease burden due to hyposalivation. Increased adoption of electronic dental records (EDR) could help in leveraging information from these records to assess dental treatment outcomes in SSP. In this study, we evaluated the feasibility of using EDR to characterize the dental treatments SSP received and assess the longevity of implants in these patients. We identified 180 SSP in ten years of patients' data at the Indiana University School of Dentistry clinics. A total of 104 (57.77%) patients received restorative or endodontic treatments. Eleven patients received 23 implants with a survival rate of 87% at 40 months follow-up. We conclude that EDR data could be used for characterizing the treatments received by SSP and for assessing treatment outcomes

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

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

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

Extraction and Evaluation of Medication Data from Electronic Dental Records

With an increase in the geriatric population, dental care professionals are presented with older patients who are managing their comorbidities using multiple medications. In this study, we developed a system to extract medication information from electronic dental records (EDRs) and provided patient distribution by the number of medications

Evaluation of a Dental Diagnostic Terminology Subset

The objective of this study was to determine how well a subset of SNODENT, specifically designed for general dentistry, meets the needs of dental practitioners. Participants were asked to locate their written diagnosis for tooth conditions among the SNODENT terminology uploaded into an electronic dental record. Investigators found that 65% of providers’ original written diagnoses were in “agreement” with their selected SNODENT dental diagnostic subset concept(s)

COVID-19 and saliva: A primer for dental health care professionals

This article is made available for unrestricted research re-use and secondary analysis in any form or be any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.To contain the COVID‐19 pandemic, it is essential to find methods that can be used by a wide range of health care professionals to identify the virus. The less potential contagious nature of the collection process, the ease of collection and the convenience of frequent collection for real‐time monitoring makes saliva an excellent specimen for home‐based collection for epidemiological investigations. With respect to COVID‐19, the use of saliva offers the added advantages of greater sensitivity and potential for detection at an early stage of infection. However, the advantages from a diagnostic perspective also reflect the potential risk to dental professionals from saliva from infected patients. Although not validated in COVID‐19 patients, but by extension from studies of SARS‐CoV‐1 studies, it is suggested that using antimicrobial mouthrinses such as chlorhexidine, hydrogen peroxide or sodium hypochlorite solutions could reduce the viral load in saliva droplets and reduce the risk of direct transmission. Because large saliva droplets could deposit on inanimate surfaces, changing the personal protective equipment including the clinical gown, gloves, masks, protective eye wear and face shield between patients, as well as decontamination of the work surfaces in the clinic, could reduce the risk of indirect contact transmission

Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease

Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care

Association between intracranial carotid artery calcifications and periodontitis: A cone-beam computed tomography study

Background Intracranial carotid artery calcifications (ICACs) are one type of calcification that may be detected as incidental findings in cone-beam computed tomography (CBCT). This retrospective study aimed to examine the prevalence of ICACs on CBCT images and their associations among age, gender, chronic periodontitis, and patient-reported cardiovascular diseases (CVDs). Methods A total of 303 CBCT scans were reviewed and a total of 208 patients met the inclusion criteria. The presence or absence of ICACs was evaluated in the ophthalmic and cavernous segments of each scan. Patient demographic data, including age, gender, and medical history, specifically focused on CVDs were recorded. The presence or absence of periodontitis was recorded from each subject with full mouth radiographs and clinical measurements. Odds ratios (ORs) were calculated as part of the logistic regression analysis. Results Overall, ICACs were found in 93 subjects (45%). The bilateral ICACs were found in 43 subjects (21% of the total subjects, 46% of the subjects with ICACs). There were statistically significant associations between presence of ICACs and periodontitis (OR = 4.55), hypertension (OR = 3.02), hyperlipidemia (OR = 2.87), increasing age (OR = 2.24), and the male gender (OR = 1.85). Smoking status was not significantly correlated with ICACs. Conclusion This study revealed that nearly half (45%) of the subjects displayed ICACs on the CBCT images. ICACs are significantly related to the status of chronic periodontitis, age, gender, and CVDs. A more careful review of CBCT scans is highly recommended to detect these calcifications and refer patients for further medical evaluation