Barriers to Point-of-Care Testing in India: Results from Qualitative Research across Different Settings, Users and Major Diseases
Bibliographic record
Abstract
BACKGROUND: Successful point-of-care testing, namely ensuring the completion of the test and treat cycle in the same encounter, has immense potential to reduce diagnostic and treatment delays, and impact patient outcomes. However, having rapid tests is not enough, as many barriers may prevent their successful implementation in point-of-care testing programs. Qualitative research on diagnostic practices may help identify such barriers across different points of care in health systems. METHODS: In this exploratory qualitative study, we conducted 78 semi-structured interviews and 13 focus group discussions in an urban and rural area of Karnataka, India, with healthcare providers (doctors, nurses, specialists, traditional healers, and informal providers), patients, community health workers, test manufacturers, laboratory technicians, program managers and policy-makers. Participants were purposively sampled to represent settings of hospitals, peripheral labs, clinics, communities and homes, in both the public and private sectors. RESULTS: In the Indian context, the onus is on the patient to ensure successful point-of-care testing across homes, clinics, labs and hospitals, amidst uncoordinated providers with divergent and often competing practices, in settings lacking material, money and human resources. We identified three overarching themes affecting point-of-care testing: the main theme is 'relationships' among providers and between providers and patients, influenced by the cross-cutting theme of 'infrastructure'. Challenges with both result in 'modified practices' often favouring empirical (symptomatic) treatment over treatment guided by testing. CONCLUSIONS: Even if tests can be conducted on the spot and infrastructure challenges have been resolved, relationships among providers and between patients and providers are crucial for successful point-of-care testing. Furthermore, these barriers do not act in isolation, but are interlinked and need to be examined as such. Also, a test alone has only limited power to overcome those difficulties. Test developers, policy-makers, healthcare providers and funders need to use these insights in overcoming barriers to point-of-care testing programs.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| 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".