Understanding Emergency Room Visits for Nontraumatic Oral Health Conditions in a Hospital Serving Rural Appalachia: Dental Informatics Study
Bibliographic record
Abstract
BACKGROUND: In the Appalachian region, a variety of factors will impact the ability of patients to maintain good oral health, which is essential for overall health and well-being. Oral health issues have led to high costs within the Appalachian hospital system. Dental informatics examines preventable dental conditions to understand the problem and suggest cost containment. OBJECTIVE: We aimed to demonstrate the value of dental informatics in dental health care in rural Appalachia by presenting a research study that measured emergency room (ER) use for nontraumatic dental conditions (NTDCs) and the associated economic impact in a hospital system that primarily serves rural Appalachia. METHODS: The Appalachian Clinical and Translational Science Institute's oral health data mart with relevant data on patients (n=8372) with ER encounters for NTDC between 2010 and 2018 was created using Appalachian Clinical and Translational Science Institute's research data warehouse. Exploratory analysis was then performed by developing an interactive Tableau dashboard. Dental Informatics provided the platform whereby the overall burden of these encounters, along with disparities in burden by age groups, gender, and primary payer, was assessed. RESULTS: Dental informatics was essential in understanding the overall problem and provided an interactive and easily comprehensible visualization of the situation. We found that ER visits for NTDCs declined by 40% from 2010 to 2018, but a higher percentage of visits required inpatient care and surgical intervention. CONCLUSIONS: Dental informatics can provide the necessary tools and support to health care systems and state health departments across Appalachia to address serious dental problems. In this case, informatics helped identify that although inappropriate ER use for NTDCs diminished due to ER diversion efforts, they remain a significant burden. Through its visualization and data extraction techniques, dental informatics can help produce policy changes by promoting models that improve access to preventive care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".