Telemedicine visits requiring follow-up in-person visits at an urban academic family medicine centre
Bibliographic record
Abstract
BACKGROUND: With the onset of the COVID-19 pandemic, telemedicine was rapidly implemented in care settings globally. To understand what factors affect the successful completion of telemedicine visits in our urban, academic family medicine clinic setting, we analysed telemedicine visits carried out during the pandemic. METHODS: We conducted a retrospective chart review of telemedicine visits from 2 clinical units within a family medicine centre. To investigate the association between incomplete visits and various factors (age, gender, presenting complaints, physician level of training [resident or staff] and patient-physician relational continuity), we performed a multivariable logistic regression on data from August 2020, February 2021, and May 2021. An incomplete visit is one that requires a follow-up in-person visit with a physician within 3 days. RESULTS: Of the 2,138 telemedicine patient visits we investigated, 9.6% were incomplete. Patients presenting with lumps and bumps (OR: 3.84, 95% CI: 1.44, 10.5), as well as those seen by resident physicians (OR: 1.77, 95% CI: 1.22, 2.56) had increased odds of incomplete visits. Telemedicine visits at the family medicine clinic (Site A) with registered patients had lower odds of incomplete visits (OR: 0.24, 95% CI: 0.15, 0.39) than those at the community clinic (Site B), which provides urgent/episodic care with no associated relational continuity between patients and physicians. CONCLUSION: In our urban clinical setting, only a small minority of telemedicine visits required an in-person follow-up visit. This information may be useful in guiding approaches to triaging patients to telemedicine or standard in-person care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".