Compounding diagnostic delays: a qualitative study of point‐of‐care testing in <scp>S</scp>outh <scp>A</scp>frica
Bibliographic record
Abstract
OBJECTIVES: Successful point-of-care (POC) testing (completion of test-and-treat cycle in one patient encounter) has immense potential to reduce diagnostic and treatment delays, and improve patient and public health outcomes. We explored what tests are done and how in public/private, rural/urban hospitals and clinics in South Africa and whether they can ensure successful POC testing. METHODS: This qualitative research study examined POC testing across major diseases in Cape Town, Durban and Eastern Cape. We conducted 101 semi-structured interviews and seven focus group discussions with doctors, nurses, community health workers, patients, laboratory technicians, policymakers, hospital managers and diagnostic manufacturers. RESULTS: In South Africa, diagnostics are characterised by a centralised system. Most tests conducted on the spot can be made to work successfully as POC tests. The majority of public/private clinics and smaller hospitals send samples via couriers to centralised laboratories and retrieve results the same way, via internet, fax or phone. The main challenge to POC testing lies in transporting samples and results, while delays risk patient loss from diagnostic/treatment pathways. Strategies to deal with associated delays create new problems, such as artificially prolonged turnaround times, strains on human resources and quality of testing, compounding additional diagnostic and treatment delays. CONCLUSIONS: For POC testing to succeed, particular characteristics of diagnostic ecosystems and adaptations of professional practices to overcome associated challenges must be taken into account.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".