Evaluation of a Telehealth Clinic as a Means to Facilitate Dermatologic Consultation: Pilot Project to Assess the Efficiency and Experience of Teledermatology Used in a Primary Care Network
Bibliographic record
Abstract
BACKGROUND: Primary care offices spend considerable time coordinating the specialist referral process. Patients experience long wait times for consultation and intervention. OBJECTIVE: To determine if telehealth combined with interdisciplinary team-based care can reduce wait times for dermatologic consultation while making the consultation process easier for physicians. METHODS: Retrospective chart reviews as well as patient, referring physician, nonreferring physician, clinic physician, nurse, and teledermatologist interviews were used to evaluate the clinic. A comparative immersion approach generated themes from field notes. Wait times, appointment times, and encounter durations were measured. RESULTS: Twenty-eight patients were seen (23 had previous specialist referral experience) within 1 week of referral compared to a wait period of 104 days for conventional referral. Patients requiring intervention were treated within 1 week of their initial appointment. Referring practitioners were concerned that they would lose control of patients' care. An easier referral process and faster intakes met physician expectations. CONCLUSIONS: Teledermatology improves the timeliness of appointments. Patients forgo face-to-face appointments if alternatives are available sooner. Physicians are concerned about their own liability if dermatologists do not assess the patient in person but will refer through teledermatology when patients are seen faster and they remain in control of the care process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".