Driving Business Value Through Telemonitoring: Integrating ESG and Digital Health Equity in Healthcare Organizations
Bibliographic record
Abstract
In the context of accelerating digital transformation in global healthcare systems, telemedicine and, in particular, telemonitoring are emerging as pivotal tools for promoting equitable access to care. This study presents a comprehensive literature review that explores how telemonitoring services can be leveraged to advance Digital Health Equity (DHE), particularly for underserved and marginalized populations. The review synthesizes evidence from peer-reviewed literature in the domains of implementation science, health informatics and organizational management, identifying key factors that influence successful adoption of telemonitoring. These factors include digital literacy, infrastructure readiness, ethical governance and socio-technical alignment. The findings emphasize that while telemedicine offers considerable potential in enhancing healthcare delivery, its impact on equity is contingent upon addressing disparities in digital access, trust in technology, and health system responsiveness. The review also highlights the role of Environmental, Social, and Governance (ESG) frameworks in embedding sustainability and accountability into telehealth strategies. The study concludes by proposing a conceptual foundation for healthcare organizations to co-design inclusive and resilient telemonitoring models that align with long-term public health and digital equity goals.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".