Health technology assessment for digital health Technologies in India: a framework for action
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to understand the path for establishing digital health technologies-health technology assessment (DHT-HTA) in India. METHODS: A rapid review of HTA and DHT frameworks on PubMed (MEDLINE) and Google Scholar was conducted to identify DHT-HTA guidelines, and HTA processes in India. MS-Excel template was created with key domains for assessing DHT in resource-constrained settings based on studies and reports identified. Responses received from seventeen experts with varying expertise in DHT, HTA, clinical, and research were contacted using an online form. Following the principles of qualitative research rooted on grounded theory approach, themes and domains were derived for a framework which was again circulated through participants. Weightage for each theme was assigned based on the frequency of responses and qualifiers were used to interpret results. Inductively derived themes from these responses were clubbed together to identify macro-level systems requirements, and finally pre-requisites for setting up DHT-HTA framework was synthesized. RESULTS: HT are commonly perceived by experts (64.7 percent participants) as a technology strictly connected to health information. Real-world data (i.e., electronic health data) are recognized as a relevant tool in support of decision-making for clinical and managerial levels. Experts identified some pre-requisites for the establishment of DHT-HTA in the country in terms of infrastructure, contextual factors, training, finance, data security, and scale-up. CONCLUSION: Our research not only identified the pre-requisites for the adoption of a DHT-HTA framework for India, but confirmed the need to address DHT-HTA's acceptability among. Hospitals and health insurance providers.
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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 | 0.099 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".