Policy Innovation in Healthcare: Exploring the Adoption and Implementation of Telemedicine
Bibliographic record
Abstract
Background: Telemedicine has emerged as a transformative solution in healthcare, offering improved accessibility and efficiency. However, its widespread adoption remains influenced by policy frameworks, digital infrastructure, and financial sustainability. This study examines the role of policy innovation in telemedicine adoption and implementation, assessing regulatory impact, technological readiness, and reimbursement structures. Methods: A cross-sectional survey design with a mixed-methods approach was employed, integrating quantitative surveys and qualitative interviews. Data were collected from healthcare policymakers, administrators, physicians, and technology developers across hospitals, clinics, and telemedicine service providers. Logistic regression and chi-square tests were conducted to analyze key predictors of telemedicine adoption, including regulatory support, digital infrastructure, and reimbursement policies. A total of 400 participants were surveyed, and 25 stakeholders were interviewed to analyze key predictors of telemedicine adoption. Results: The findings indicate that institutions with clear licensing regulations and policy support exhibited significantly higher telemedicine adoption rates (OR = 2.15, p = 0.004). Standardized reimbursement policies positively influenced adoption rates (χ² = 14.91, p = 0.008). Digital infrastructure readiness, including broadband connectivity and EHR interoperability, was strongly associated with increased telemedicine utilization (OR = 2.31, p = 0.005). Major barriers included regulatory fragmentation, financial constraints, and technological literacy gaps. Conclusion: Policy innovation, digital infrastructure investments, and structured reimbursement models are critical for telemedicine expansion. Addressing regulatory inconsistencies and financial limitations will enhance adoption. Future research should explore long-term policy impacts and AI integration in telemedicine.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".