Some Multidimensional Unintended Consequences of Telehealth Utilization: A Multi-Project Evaluation Synthesis
Bibliographic record
Abstract
BACKGROUND: Telehealth initiatives have bloomed around the globe, but their integration and diffusion remain challenging because of the complex issues they raise. Available evidence around telehealth usually deals with its expected effects and benefits, but its unintended consequences (UCs) and influencing factors are little documented. This study aims to explore, describe and analyze multidimensional UCs that have been associated with the use of telehealth. METHODS: We performed a secondary analysis of the evaluations of 10 telehealth projects conducted over a 22-year period in the province of Quebec (Canada). All material was subjected to a qualitative thematic-pragmatic content analysis with triangulation of methodologies and data sources. We used the conceptual model of the UCs of health information technologies proposed by Bloomrosen et al to structure our analysis. RESULTS: Four major findings emerged from our analysis. First, telehealth utilization requires many adjustments, changes and negotiations often underestimated in the planning and initial phases of the projects. Second, telehealth may result in the emergence of new services corridors that disturb existing ones and involve several adjustments for organizations, such as additional investments and resources, but also the risk of fragmentation of services and the need to balance between standardization of practices and local innovation. Third, telehealth may accentuate power relations between stakeholders. Fourth, it may lead to significant changes in the responsibilities of each actor in the supply chain of services. Finally, current legislative and regulatory frameworks appear ill-adapted to many of the new realities brought by telehealth. CONCLUSION: This study provides a first attempt for an overview of the UCs associated with the use of telehealth. Future research-evaluation studies should be more sensitive to the multidimensional and interdependent factors that influence telehealth implementation and utilization as well as its impacts, intended or unintended, at all levels. Thus, a consideration of potential UCs should inform telehealth projects, from their planning until their scaling-up.
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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 | 0.256 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".