Telemedicine for Mental Disorders: A Review of Treatment Outcomes, Patient Satisfaction, and Reliability Comparisons with In-Person Care
Bibliographic record
Abstract
Introduction: Many individuals suffering from mental illnesses remain undiagnosed due to accessibility barriers. Emerging trends in telemedicine offer innovative solutions to these challenges: remote healthcare delivery such as videoconferencing eliminates the effort and cost of commuting, allowing patients access to mental health care from the comfort of their homes. This literature review examined patients meeting diagnosis criteria for a mental disorder and receiving treatment either in-person or online, with the goal of comparing treatment outcomes, satisfaction, and reliability. Methods: We conducted a comprehensive literature search directly related to telemedicine as a treatment to mental disorders using PubMed databases, Embase, MEDLINE, and Web of Science databases between database inception to February 2023. All peer-reviewed manuscripts on outcome, reliability, and patient satisfaction on the topic were included. Secondary research, cross-benefit analyses, and summaries of trends were excluded. The results of each study, intervention methods, demographic, and attrition were summarized on Excel. Results: Out of 2034 articles found in the literature search conducted on PubMed, Embase, MEDLINE, and Web of Science databases between inception and February 2023, 25 studies that directly relate to telemedicine as a treatment for mental disorders were included. Most of them found no significant differences in outcome and satisfaction between both delivery modalities. Two studies examined the inter-rater reliability of diagnoses between delivery methods, but one reported no significant differences while the other found a significantly higher correlation between the scores of two raters for telemedicine patients. Discussion: The current literature suggest that telemedicine is at least comparable to in-person healthcare in terms of outcome, as most of the reviewed studies found insignificant differences between the two delivery modalities. However, inter-rater reliability of psychiatric interviews using telemedicine and in-person modalities remain uncertain due to the limited number of studies on the topic and the contradicting results of the two papers addressing this issue. Conclusion: Telemedicine may serve as a cost-effective and time-saving method for interventions that do not require the patient to be on-site. Further research comparing clinical interviews and diagnoses between raters from both modalities should be conducted to establish a larger body of evidence on reliability.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".