Lessons Learned From Over 20 Years of Telemedicine Services in India: Scoping Review of Telemedicine Services Initiated From 2000 to 2023
Bibliographic record
Abstract
BACKGROUND: India is home to some of the world's earliest and largest telemedicine services. Since the first telemedicine services emerged in the 1990s, the growing digitization of health care services has highlighted the potential for telemedicine services to increase access to timely and appropriate care seeking, corresponding to improved health outcomes and cost savings to the individual and health system. Despite this potential, little is known about the varied typologies of telemedicine services providing in India, their design and model characteristics, scale of implementation, and the available evidence on their impact. OBJECTIVE: This scoping review aims to identify the characteristics of telemedicine services in India, including the type of telemedicine model, details on the timing of delivery, health services provided, and service delivery channel. Additional details are extracted on the scale of implementation, software used, and evidence gathered, including impact on care seeking, health outcomes, and cost. METHODS: Telemedicine services in India were identified through searches of Google, the Google Play Store, 3 major scientific databases (Embase, PubMed, and Scopus), and a reference review of identified peer-reviewed articles. Included services were restricted to those implemented in India between January 1, 2010, and July 4, 2023, which included humans, and were published in the English language. Once identified, articles were imported to Covidence, and the process of abstract screening was initiated using 2 independent reviewers and a third person to resolve conflicts. Full-text articles were screened, and data were extracted into Microsoft Excel. RESULTS: A total of 2368 articles were identified, 151 of which were included for the full-text review and data extraction. From the 151 studies, a total of 115 unique services were identified and further classified based on a scale-moderate to large (n=89) and small (n=26). Among moderate- to large-scale services (n=89), 75 used specialized software and 14 used nonspecialized software, such as WhatsApp. On average, 3 new telemedicine services were initiated annually from 2000 to 2019, and the growth of new services occurred predominantly in the private sector. Evidence was available for 43% (32/75) of the telemedicine services. While 21 services reported on some facet of the quality of care, no studies systematically assessed quality of care. Where structured surveys were reported, questions were often leading, used longer Likert scale response options, and asked respondents about broad constructs subject to varied interpretations (eg, quality of care or satisfaction). Additional details on model characteristics, reach, and impact are presented. CONCLUSIONS: The widespread proliferation of telemedicine services in India has much potential to improve access to and continuity of timely and appropriate care seeking for health. However, improved evidence demonstrating the impact of telemedicine services on care seeking, quality of care, cost, and health outcomes is needed.
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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 | 0.035 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".