Evaluating System-level Implementation of Telepsychiatry in Ontario from 2008-2016: Implications for the Sustainability and Growth of Telepsychiatry
Bibliographic record
Abstract
Objectives The objective of this thesis was to understand the implementation of clinical telepsychiatry in Ontario. To do this, paper 1 described the characteristics of psychiatrists who delivered and patients who received telepsychiatry, calculated how many in-need patients received telepsychiatry, and identified trends in the distribution of telepsychiatry (where care was delivered to and from). Paper 2 described characteristics and predictors of family physicians who referred patients to telepsychiatry and paper 3 compared the costs of telepsychiatry with other traditional models of psychiatric outreach. Study Designs This thesis employed several study designs, including a serial panel study and retrospective cross-sectional studies using linked data from ICES, as well as a cost-minimization analysis. Results Paper 1 found that, in fiscal year (FY) 2012, a total of 3,801 people had 5,635 telepsychiatry visits, and 7% of Ontario psychiatrists provided these visits. Of 48,381 people discharged from a psychiatric hospitalization, 60% saw a local psychiatrist, 39% saw no psychiatrist, and less than 1% used telepsychiatry within 1 year of discharge. Paper 2 showed that the number of patients using telepsychiatry, and the number of family physicians referring to telepsychiatry increased fifteen-fold and nine-fold, respectively, from FY 2008 to FY 2016. 32% of Ontario family physicians referred to telepsychiatry in FY 2016, however, less than 1% of their rostered patients used telepsychiatry (n =12,449/3,513,638). Family physicians that referred to telepsychiatry were more likely to be from a rural residence, to have more nurse practitioners in their practice, and to be part of a Family Health Team, while their patients were more likely to live in rural areas, have increased complexity, and higher rates of mental health service utilization. Paper 3 found that costs per visit were lowest in telepsychiatry ($360), followed by travelling physicians ($558) and patient reimbursement for travel ($620). Conclusions This dissertation has demonstrated that while telepsychiatry has been increasingly adopted by providers, adoption by patients remains fairly low. Telepsychiatry is less costly than other in-person outreach models and shows signs of sustainability, such as the persistent increase in adoption and penetration throughout Ontario over the study period.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".