Computational surveillance of Colombian public pharmaceutical procurement using public administrative data: A reproducible analysis of a closed 2020–2025 cohort

Andrés Soto

PLOS ONE, 2026, vol. 21, issue 9, 1-16

Abstract: Introduction: BigLoI monitors Colombian public pharmaceutical procurement from 2015 onward. For this manuscript, the source cohort comprised 162,271 candidate pharmaceutical contracts from 2020 to 2025. After contract-level review excluded 441 records explicitly concerning veterinary, animal-health, or agricultural use, the corrected closed analytical cohort comprised 161,830 contracts. Objective: To describe the design, implementation, and findings of a reproducible computational infrastructure for surveillance of Colombian public pharmaceutical procurement. Methods: Public APIs from SECOP-II, INVIMA, and SISMED were integrated into a reproducible architecture combining PostgreSQL, statistical analysis, and a public-facing observatory. Candidate records whose supplier names indicated possible veterinary or agricultural activity underwent contract-object review; 441 records with explicit animal, veterinary, or agricultural scope were excluded, while human-health and ambiguous records were retained conservatively. A Z-score engine was implemented to flag contracts with atypical total values within therapeutic categories. As a clearly secondary technical-feasibility module, smart-contract automation of a payment workflow was tested on the Sepolia testnet only to verify predefined digital state transitions under simulated conditions. The corrected closed analytical cohort included 161,830 pharmaceutical contracts from 2020 to 2025, while post-2025 records remained available only for live platform monitoring. Monetary results were reported primarily in COP; secondary USD equivalents were included only as approximate interpretive references, with detailed conversions relegated to S1 Table. Results: Among 146,594 contracts analyzed in categories with at least 10 observations, 664 contracts (0.45%) triggered a statistical alert with absolute Z-score greater than or equal to 1.5 sigma. The alert rate among Z-score-eligible contracts increased from 0.30% in 2021 to 1.31% in 2025. Antibiotics showed a category-level maximum Z-score of 8.89. The top 3% of suppliers concentrated 85.8% of total contracted value. As a clearly secondary module, the Sepolia smart-contract prototype confirmed only that predefined digital state transitions could be executed under simulated testnet conditions; it provides no evidence of real-world payment-cycle reduction, realized savings, or institutional deployability. Conclusions: A reproducible national-scale computational infrastructure identified atypical procurement patterns and documented marked market concentration in Colombian public pharmaceutical procurement. These descriptive findings may inform auditing, public health policy discussions, and health data governance, but they do not by themselves establish wrongdoing or prescribe specific reforms. Statistical alerts remain exploratory prioritization signals rather than evidence of corruption or fraud. The blockchain module should be interpreted strictly as a complementary technical proof of concept tested under simulated conditions and not as operational evidence on real-world payment performance, savings, or implementation readiness.

Date: 2026
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DOI: 10.1371/journal.pone.0350967

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