How patients navigate the diagnostic ecosystem in a fragmented health system: a qualitative study from India
Bibliographic record
Abstract
BACKGROUND: Depending on a country's diagnostic infrastructure, patients and providers play different roles in ensuring that correct and timely diagnosis is made. However, little is known about the work done by patients in accessing diagnostic services and completing the 'test and treat' loop. OBJECTIVE: To address this knowledge gap, we traced the diagnostic journeys of patients with tuberculosis, diabetes, hypertension and typhoid, and examined the work they had to do to arrive at a diagnosis. METHODS: This paper draws on a qualitative study, which included 78 semi-structured interviews and 13 focus group discussions with patients, public and private healthcare providers, community health workers, test manufacturers, laboratory technicians, program managers and policymakers. Data were collected between January and June 2013 in rural and urban Karnataka, South India, as part of a larger project on barriers to point-of-care testing. We reconstructed patient diagnostic processes retrospectively and analyzed emerging themes and patterns. RESULTS: The journey to access diagnostic services requires a high level of involvement and immense work from patients and/or their caretakers. This process entails overcoming cost and distance, negotiating social relations, continuously making sense of their illness and diagnosis, producing and transporting samples, dealing with the social consequences of diagnosis, and returning results to the treating provider. The quality and content of interactions with providers were crucial for completion of test and treat loops. If the tasks became overwhelming, patients opted out, delayed being tested, switched providers and/or reverted to self-testing or self-treatment practices. CONCLUSION: Our study demonstrated how difficult it can be for patients to complete diagnostic journeys and how the health system works as far as diagnostics are concerned. If new point-of-care tests are to be implemented successfully, policymakers, program officers and test developers need to find ways to ease patient navigation through diagnostic services.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".