Telehealth Outcome Categories for Patient Safety: A Validation of the Outcome Categorization based on a Literature Review (Preprint)
Bibliographic record
Abstract
Background: Integrating telehealth into established care processes can be challenging. With the integration of telehealth into routine health care practices, there is a growing need to evaluate telehealth outcomes to understand its impact on health care delivery. However, existing literature on telehealth outcomes to support evaluation remains limited. Objective: This study aimed to analyze recent research from the past decade to develop a categorization of telehealth outcomes. This study seeks to validate a defined set of telehealth outcome categories and examine the broader impact of digital transformation on health care delivery. Methods: We built the telehealth outcome categories according to the existing literature. During the category-building process, we identified 2 main components: the patient safety outcomes of telehealth and the other care-related outcomes. To validate these categories, we conducted a literature search. The initial search yielded 65 unique articles. Following the screening process, we selected 15 articles for the review. In the review analysis, comprehensive data extraction points established a robust framework for evaluating the scope and impact of telehealth research across multiple dimensions. Results: On the basis of the analysis, 6 patient safety outcome categories were identified: mortality, adverse effects and harm, complications, hospitalization and readmission, diagnostic and treatment errors, and medication safety. The 9 other care-related outcome categories include cost-effectiveness, access to care, and patient satisfaction. Despite a limited sample, the results on the patient safety outcomes of telehealth indicate a generally positive impact. Several studies have reported that telehealth services are associated with reduced adverse events, complications, and readmissions and enhanced monitoring of patient conditions. The reviewed articles did not include use cases covering all identified preliminary outcome categories, such as medication safety and privacy. However, the review supported patient safety categories being well suited to classifying telehealth outcomes. The other care-related outcomes were not so clearly defined and would require more case examples to support category building. Conclusions: Further refinement of the main categories identified in this article is necessary to allow for the identification of specific areas and themes that warrant further research initiatives. Future research is essential to evaluate the true benefits and outcomes of telemedicine, offering deeper insights into its real-world impact.
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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 | 0.080 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.056 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".