Quality Management Systems implementation in public medical laboratories; A sustainable approach to health system strengthening in Lagos State, Nigeria
Bibliographic record
Abstract
BACKGROUND: The effectiveness of Quality Management Systems (QMS) in public medical laboratories is crucial to ensure quality and reliable testing outcomes for quality healthcare. This research aims to achieve a minimum of 2 Stars WHO-AFRO rating at the external audit of ten public medical Laboratories within twelve months of intervention using improved documentation and institutionalization of robust QMS. METHOD: A quasi-experimental design was used to assess QMS interventions in ten of 28 public secondary medical laboratories in Lagos State. These facilities were randomly selected using non-probability measures over 12 months from November 2022 to October 2023. The study measured resource allocation, conducted staff training for capacity building, and provided mentoring support. External audits were performed using the WHO-AFRO SLIPTA 2015 checklist, with a grading system from 0 to 5 stars. The data collected included baseline and post-intervention scores, analyzed using descriptive statistics and baseline compared with follow-up audit performance. RESULT: The 12-month implementation of laboratory QMS in ten Lagos State's public secondary health facilities revealed substantial progress. Nine Medical laboratories in the study had a baseline WHO-AFRO rating of 0 Star, while General Hospital Ikorodu had a baseline rating of 1 Star. Sixty percent of the medical laboratories demonstrated commendable QMS improvement and achieved 3 Stars WHO-AFRO rating each, while twenty percent of the medical laboratories attained 2-Stars each. However, the remaining twenty percent of the health facilities achieved minimal improvements, securing 1 Star WHO-AFRO rating each. CONCLUSION: Overall, Eighty percent of the medical laboratories showed progress in QMS implementation in Lagos State. The study reveals that a Government-led QMS implementation drives a more sustainable culture of quality in medical laboratories and the twelve-month measure indicates the possibility to extend the QMS interventions to the remaining eighteen public medical laboratories in Lagos State.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".