Point-of-care testing in India: missed opportunities to realize the true potential of point-of-care testing programs
Bibliographic record
Abstract
BACKGROUND: The core objective of any point-of-care (POC) testing program is to ensure that testing will result in an actionable management decision (e.g. referral, confirmatory test, treatment), within the same clinical encounter (e.g. POC continuum). This can but does not have to involve rapid tests. Most studies on POC testing focus on one specific test and disease in a particular healthcare setting. This paper describes the actors, technologies and practices involved in diagnosing major diseases in five Indian settings - the home, community, clinics, peripheral laboratories and hospitals. The aim was to understand how tests are used and fit into the health system and with what implications for the POC continuum. METHODS: The paper reports on a qualitative study including 78 semi-structured interviews and 13 focus group discussions with doctors, nurses, patients, lab technicians, program officers and informal providers, conducted between January and June 2013 in rural and urban Karnataka, South India. Actors, diseases, tests and diagnostic processes were mapped for each of the five settings and analyzed with regard to whether and how POC continuums are being ensured. RESULTS: Successful POC testing hardly occurs in any of the five settings. In hospitals and public clinics, most of the rapid tests are used in laboratories where either the single patient encounter advantage is not realized or the rapidity is compromised. Lab-based testing in a context of manpower and equipment shortages leads to delays. In smaller peripheral laboratories and private clinics with shorter turn-around-times, rapid tests are unavailable or too costly. Here providers find alternative measures to ensure the POC continuum. In the home setting, patients who can afford a test are not/do not feel empowered to use those devices. CONCLUSION: These results show that there is much diagnostic delay that deters the POC continuum. Existing rapid tests are currently not translated into treatment decisions rapidly or are not available where they could ensure shorter turn-around times, thus undermining their full potential. To ensure the success of POC testing programs, test developers, decision-makers and funders need to account for such ground realities and overcome barriers to POC 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".