Information asymmetry in the Kenyan medical laboratory sector
Bibliographic record
Abstract
BACKGROUND: Important information about medical laboratory providers is not readily available to all patients, clinicians nor regulators in Kenya. This study was conducted as part of a wider project aiming to improve access to high quality diagnostics by addressing information asymmetries in the Kenyan market for laboratory services. OBJECTIVES: The purpose of this study was to: 1) Gather pricing information for 49 common laboratory tests from medical laboratories in Nairobi, Kenya, noting where these prices were publicly available or withheld. 2) Assess patients' knowledge of testing information including: turnaround time, price, and test availability. METHOD: This was a cross-sectional study where a mystery caller approach was used to survey 49 tests for turnaround time, price, and availability across 13 laboratories selected purposively. The mystery shopper survey was complemented by 251 patient exit interviews at two Kenyan hospitals to understand whether patients seeking laboratory tests in Nairobi had access to such information. All 251 patients were selected by convenience sampling. RESULTS: We noted that 85% of the private laboratories did not disclose test prices and turnaround times to their patients. There was a wide range of prices on several key tests, with private in-facility laboratories charging an average test price of 468% of the average test price in public laboratories across all the 49 tests. We also found that many patients lacked key information regarding the tests they needed: 65% did not know the purpose of the test while 41% did not know the test price at all. CONCLUSION: Under the current system, patients have limited access to information regarding the key criteria required to make a rational decision. This has a significant impact on the quality, price, and turnaround time (TAT) offered by the medical laboratories that operate in this dysfunctional market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".