Lessons learned from redesigning public health medicines supply chain model in Uttar Pradesh, India
Bibliographic record
Abstract
Uttar Pradesh (UP), the most populous state of India with 238 million people, has over 30,000 public health facilities. Ensuring the continuous availability of essential medicines across these facilities is a significant challenge. An audit conducted in 2017 indicated large gaps in the availability of essential medicines in public health facilities. This study describes the lessons from Tamil Nadu's Medical Supplies Corporation (TNMSC) that were adapted to inform the redesign of the medicines supply chain model and processes of Uttar Pradesh's Medical Supplies Corporation (UPMSC). We identified seven essential pillars for a successful public health supply chain system through a desk review and learnings from TNMSC. These included a stable list of essential medicines, warehouses, centralized procurement, a passbook system, quality control, centralized payment, and digital e-tracking to enable real-time inventory and procurement decisions. The system design established a clear responsibility matrix: UPMSC is responsible for ensuring the availability of all essential medicines in the district warehouses at all times. The facility in-charge is responsible for ensuring the availability of the required drugs at the facility. The facilities are notionally allotted a budget and have complete freedom to pickup medicines from the warehouse, as long as they remain within the budget available. Under these seven essential pillars, several key processes were undertaken to improve vendor participation, reduce vendor dependency, synchronize tenders for all essential drugs, and establish rosters for facilities to pick up drugs from the warehouse. These efforts led to an improvement in the availability of essential medicines from ~40% to ~100%, with an average of 275 medicines out of 287 medicines available per warehouse. Supply orders increased from $58 million to $112 million, and facilities consumption value increased from $38 million to $90 million by April 2024. However, challenges such as last-mile delivery and prompt payment to vendors remain. This paper underscores the importance of system design in the public health supply chain and may be useful for other Indian states and low- and lower-middle-income countries (LMICs) with a similar context.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".