Exploring Medical Information Needs and Accessibility in Swedish Dental Care by Analysis of Documentation Workflows and Electronic Dental Records in Dalarna: Sociotechnical Qualitative Study
Bibliographic record
Abstract
Background: Despite growing evidence demonstrating the connection between oral and systemic health, medical and dental care remain institutionally divided. A significant consequence of this division is the lack of information sharing, which is particularly problematic in dental care, where knowing patients' medical information is crucial for providing safe and effective treatments. This separation poses additional challenges in Swedish regions with limited resources, such as Dalarna, where dental care practices would benefit from improved access to relevant medical information in their electronic dental record (EDR) systems. Objective: This study aimed to explore how current documentation workflows and EDR systems support the medical information needs within dental care practices in Dalarna and consider what influence direct access to medical information could have. Methods: The study adopted an exploratory-descriptive qualitative approach. Semi-structured interviews were conducted with dental practitioners working in general dental practices. Data collection followed a sociotechnical framework, and thematic analysis was performed to identify key medical information needs, as well as current workflow and system limitations. Conceptual models were developed to reflect these findings. Results: Eighteen dental practitioners were interviewed. The identified medical information needs included specific types of medical conditions, pharmacological information, treatment history, and laboratory values. Furthermore, dental practitioners highlighted substantial challenges in existing documentation workflows and the EDR system. Proposed conceptual models demonstrated how integrating EDR systems with the Swedish National Patient Overview ("Nationell Patientöversikt") via National Service Platform ("Nationell Tjänsteplattform") could streamline workflows and enhance information accessibility. Conclusions: The findings show a clear need to improve medical information accessibility in dental care. A solution is to facilitate interoperability and align digital infrastructure with the identified needs. The proposed recommendations offer a feasible starting point for improving medical information access in Swedish dental care, particularly in resource-constrained regions like Dalarna.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".