Changes in Diagnosis, Treatment, and Clinical Improvement Among Patients Receiving Telemedicine Consultations
Bibliographic record
Abstract
The aim of this study was to determine whether outpatient telemedicine specialty consultations to primary care clinicians result in changes in a patient's diagnosis, treatment management, and clinical outcomes. Medical records of patients who received two or more clinical telemedicine consultations in dermatology, psychiatry, and endocrinology were evaluated in a nonconcurrent retrospective analysis. Three indicators were used to measure changes in the processes of care and clinical outcomes: change in diagnosis, change in treatment, and patient clinical improvement. A retrospective review of 223 individual telemedicine patient medical records was conducted. Specialty telemedicine consultations were found to result in changes in diagnoses in 48% of the cases, changes in treatment therapy in 81.6% of the cases, and clinical improvement in 60.1%. These results are consistent with previous literature that has assessed changes in processes of care and outcomes from face-to-face specialty consultations in outpatient clinics. Changes in diagnosis and treatment therapy were found to be associated with clinical improvement with odds ratios (ORs) of 2.66 (95% confidence interval [CI]: 1.47-4.83) and 11.22 (95% CI: 4.49-31.48), respectively. This study found that telemedicine consultations resulted in changes in diagnosis and treatment regimens and also are associated with clinical improvements.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".