Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers

A. Adebakin; A.O. Mohammed; A. Owoyemi; Axum AI: J. Owoyemi; F.C. Madubuko; K. Akyea; S. Abubakar; S. Oyatoye; T.O. Togunwa; Z. Oyetolu

REVIEW 4 major objections 5 minor 14 references

Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read An open-source RAG chatbot built on the Nigerian EMDEX formulary returns medication answers that match pharmacist answers at 84% average sentence similarity, supporting faster drug lookups for African healthcare workers.

desk verdict A useful open-source RAG pilot for Nigerian formularies, but the evaluation metric under-sells the hallucination claim. read the letter →

arxiv 2502.15722 v1 pith:54MDUSPX submitted 2025-01-28 cs.IR cs.HC

Axum AI: J. Owoyemi , S. Abubakar , A. Owoyemi , T.O. Togunwa , F.C. Madubuko , S. Oyatoye , Z. Oyetolu , K. Akyea

show 2 more authors

A.O. Mohammed A. Adebakin

This is my paper · ORCID

classification cs.IRcs.HC

keywords RetrievalAugmentedGenerationdrugformularyEMDEXNigerianhealthcarepromptengineeringS-BERTevaluationhallucinationreductionopen-sourcehealthAI

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Drug Insights is an open-source retrieval-augmented generation (RAG) chatbot that answers medication questions from Nigerian formulary documents. The paper's central claim is that this pipeline—built on the EMDEX essential-medicines formulary, a vector database, and a GPT-based generator—can deliver accurate, context-specific drug information with minimal made-up content, replacing slow manual lookups in printed handbooks. Against 50 doctor- and pharmacist-reviewed queries, the best of nine prompt designs (guardrails, no sentence limit) achieved an average S-BERT similarity score of 84.09% compared with a pharmacist's answer restricted to the same corpus, and the weakest prompt scored 65.91%. Pharmacist ratings of output accuracy, relevance, usefulness, and source relevance averaged about 3.8 out of 5. The paper frames the work as a feasibility demonstration for a prompt-tunable, open-source drug-information tool, with retrieval precision, chat-session behavior, and corpus coverage listed as future work.

What carries the argument

The load-bearing mechanism is the retrieval-augmented generation loop together with the S-BERT evaluation metric. Sentence-BERT (S-BERT) is a sentence-embedding model that produces a cosine similarity score between two texts, and the paper uses that score to compare Drug Insights' answers with a pharmacist's answers to the same queries. The pipeline itself works through layout-aware text extraction from EMDEX PDFs, schema-based normalization of drug entries by a language model, 1536-dimensional embedding and vector-database storage with top-3 retrieval at a 0.9 cosine threshold, and final answer generation by a GPT-based model from the retrieved passages. Prompt engineering supplies the controlled variation: nine zero-shot prompts differing in output-sentence limits and in whether they use comparison or guardrail instructions, with the guardrail-plus-no-limit combination winning.

What would settle it

Have an independent clinical panel score each chatbot answer against the full EMDEX formulary and standard references, flagging factual errors, omissions, and unsupported statements. If answers that scored high on semantic similarity to a pharmacist's short answer still contain material factual errors—especially on questions the formulary cannot answer—then the reported 84% similarity score would not establish accurate, low-hallucination drug responses.

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Extended reading notes

Core claim

The central claim is that a corpus-bound RAG system can serve as a reliable drug-information lookup for African healthcare workers: given a question, Drug Insights retrieves relevant passages from the EMDEX formulary and generates an answer that, on average, is semantically close to what a pharmacist would write using only that same corpus. The quantitative anchor is the 84.09% average S-BERT cosine similarity achieved by prompt_0a, which pairs guardrail instructions with no sentence limit; the lowest-scoring prompt, prompt_1b, reached 65.91%. Human evaluators from a healthcare-technology partner rated the outputs between 3.76 and 3.88 on a five-point scale for accuracy, relevance, usefulness, and source relevance. The authors interpret these results as evidence that RAG with prompt engineering can reduce hallucination and provide context-limited, trustworthy medication answers.

Load-bearing premise

The whole accuracy claim rests on treating the similarity score between the chatbot's answer and a pharmacist's answer—where the pharmacist could only use the same drug handbook—as proof that the answer is clinically correct and free of made-up information; that equivalence is assumed, not checked against an independent source of truth.

Editorial extensions

If this is right

If the 84.09% similarity result holds, the best prompt can produce formulary answers semantically close to a pharmacist's short answer, making the tool usable as a first-pass drug-information resource where pharmacist time is scarce.
Because the framework is open source, other organizations can substitute their own formulary PDFs and reuse the extraction-to-evaluation pipeline without rebuilding it.
The spread from 65.91% to 84.09% across prompt variants implies that prompt design is a primary determinant of RAG answer quality in this setting.
The pharmacist agreement scores around 3.8/5 support using the tool as a first-pass reference, while the paper's stated future work shows it is not yet positioned as a substitute for professional clinical review.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The similarity score measures agreement with a pharmacist who was restricted to the same corpus, so a high score chiefly shows the chatbot can retrieve and restate what a careful reader would find in EMDEX; independent clinical validation would be needed to show the facts themselves are correct.
The paper includes a few questions whose answers are not in the corpus but does not report their scores, so the most direct untested question is whether the system declines to answer or silently fabricates when coverage fails.
The fixed 0.9 cosine threshold and top-3 retrieval are fixed design choices; varying them would separate retrieval quality from generation style as drivers of the 84.09% score.
The evaluation is limited to Nigerian formulary content, so extending the same pipeline to another country's formulary is a natural test of whether the framework generalizes across African healthcare settings.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my honest take on arXiv:2502.15722. It's a straightforward RAG application: extract EMDEX formulary PDFs, chunk them, embed with Azure OpenAI, store in Pinecone, query with GPT-4o, and compare nine zero-shot prompts against pharmacist-written answers using S-BERT similarity. The best prompt scores 84.09%. The paper's real value is in the package: an open-source, working drug-information chatbot for Nigerian healthcare workers, with a corpus that matters locally and a candid discussion of limitations.

What's new is not the technique—RAG is standard, and the prompt scan is a parameter sweep. What's useful is that they built it, open-sourced it (github.com/OpenSourceCollective/drug_insights), and tested it in a realistic setting with pharmacist feedback. They also explicitly list in Section V what they did not evaluate: retrieval precision/recall, multi-turn performance, and other similarity metrics. That honesty earns goodwill.

The soft spot is the evaluation, and it is load-bearing. The abstract claims the system 'delivers accurate, context-specific responses with minimal hallucination.' The only quantitative support is the S-BERT score, which compares the system's answer to a pharmacist's answer, where the pharmacist was restricted to the same EMDEX corpus. That is a consistency check, not a validity check. A semantically similar answer can still contain a wrong dosage or a fabricated contraindication; a valid answer can be phrased very differently. The score says nothing about hallucination. On top of that, prompt_0a was chosen because it performed best on the same 50 queries used to report the 84.09%, so that number is a test-set maximum, not a held-out estimate. There is no error bar, no baseline system, and the human evaluators are affiliated with the funder. These are real issues.

One small internal contradiction: Section III.B describes prompt_0a as having 'no comparison of multiple generations,' but Section III.A and Table 1 say prompt_0* compares four generated results. Easy to fix, but it suggests the prompt descriptions need a careful pass.

Does the central argument hold up? As a feasibility study, yes—the system works and the authors were careful to scope their claims in the body. As a demonstration of accuracy and minimal hallucination, no. The evidence does not support that claim. But the authors themselves acknowledge the missing recall/precision work, so they are not hiding it.

Who is this for? Researchers and implementers working on clinical RAG in low-resource settings. It is a decent starting point, not a definitive result.

If I were editor, I would send it to peer review. The problem is meaningful, the work is reproducible enough (open source), and the evaluation can be strengthened. I would require a major revision: either a proper gold standard (e.g., clinician-verified answers, or at least a statement-level factuality check) or a softened claim that matches the consistency check actually performed.

Referee Report

4 major / 5 minor

Summary. The paper presents 'Drug Insights,' an open-source retrieval-augmented generation (RAG) chatbot that answers medication queries from a corpus of Nigerian pharmaceutical data, specifically the EMDEX formulary. The system uses Azure OpenAI embeddings, a Pinecone vector database, and GPT-4o for answer generation, with nine prompt variants compared via S-BERT similarity against pharmacist-written answers to 50 queries. The authors report that the best prompt (prompt_0a) achieves an average S-BERT similarity of 84.09%, and they supplement this with pharmacist Likert-scale feedback (averages 3.76–3.88 out of 5). The conclusion claims the system 'delivers accurate, context-specific responses with minimal hallucination.' The paper also describes future plans for retrieval precision/recall evaluation, chat-session testing, and additional similarity metrics.

Significance. The problem addressed is real and consequential: African healthcare workers need rapid, reliable access to medication information, and an open-source, locally relevant RAG tool could help. The authors should be credited for building and releasing the system, grounding it in a real formularies corpus, involving pharmacists in question design and evaluation, and systematically comparing multiple prompt variants. If the accuracy and hallucination claims were rigorously validated, this would be a useful contribution to applied medical NLP and information retrieval. However, the current evaluation does not substantiate those claims: the primary metric (S-BERT similarity to pharmacist answers derived from the same corpus) is not a measure of clinical correctness, the prompt selected as best was chosen on the same test set used to report its score, and the paper itself acknowledges that retrieval recall/precision was not evaluated. The significance is therefore contingent on substantial additional validation.

major comments (4)

[Section III.B] The central evaluation compares Drug Insights responses with pharmacist answers produced using the same EMDEX corpus and reports S-BERT cosine similarity as the evidence for 'accurate, context-specific responses with minimal hallucination.' This is not a measure of clinical accuracy or hallucination: a semantically similar answer can contain a wrong dosage or contraindication, and a semantically dissimilar answer can be clinically correct. No independent gold standard, sentence-level fact checking, or inter-rater reliability of the pharmacist reference answers is provided. The paper's own Section V lists 'Explicit systematic evaluation of the recall and precision of database retrievals' as future work, which further weakens the current claim.
[Section III.B and Fig. 2] The best prompt, prompt_0a, was selected as the one with the highest average S-BERT similarity on the same 50-query set used to report the 84.09% score. This makes the reported number a test-set maximum rather than a held-out estimate, inflating apparent quality. The authors should either split the data into prompt-selection and evaluation sets, use cross-validation, or at minimum report confidence intervals and the selection procedure. Without this, the comparison across prompts in Fig. 2 cannot support any claim about which prompt is best.
[Section II.C] The retrieval configuration (cosine similarity threshold of 0.9, similarity count of 3) is presented as a fixed design choice without any sensitivity analysis. These are free parameters that can substantially affect retrieval quality, and therefore the final answer. The paper should include a brief sensitivity study or at least a rationale grounded in experiments, especially because Section V acknowledges that retrieval recall/precision has not been evaluated.
[Section III.B] The query set of 50 questions is small, and the paper states that 'a few questions whose answers were not present in the corpus' were included as a hallucination check, but the results for those questions are not reported separately. This is a missed opportunity: reporting how the system handled out-of-corpus queries would speak directly to the hallucination claim. At minimum, the authors should quantify how many such questions were included and show their individual scores or a qualitative assessment.

minor comments (5)

[Section IV heading] The heading 'IV. PREPARE HOW IT WORKS, INCLUDING ARCHITECTURE AND PERFORMANCE METRICS' appears garbled; this likely should be 'SYSTEM OVERVIEW' or 'HOW IT WORKS.'
[Section II.A] The sentence 'Maintaining the Integrity of the Specifications' appears at the end of Section II.A and is unrelated to the surrounding text; it appears to be leftover boilerplate and should be removed.
[Figures] The paper refers to 'Fig 2' when describing the average S-BERT similarity scores, but the figure caption in the text says 'Fig 2' and the later figure is numbered 'Fig 3' while the text refers to 'Fig 3' for the sample query image. The figure numbering and in-text references should be re-checked; also, the sample query image in Fig. 3 is not discussed in the body.
[Table 2] The pharmacist feedback table reports averages but not the number of pharmacists who rated the responses, the number of queries each rated, or the standard deviation. Adding these details would make the feedback more interpretable.
[General] The abstract claims the system 'delivers accurate, context-specific responses with minimal hallucination,' but the body results are characterized as 'Preliminary tests' (Abstract) and the conclusion uses 'validated by S-BERT metrics and pharmacist feedback.' The claims should be moderated to match the evidence, e.g., 'shows promise' rather than 'delivers.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the S-BERT comparison is a descriptive evaluation and the prompt selection is not presented as a held-out prediction.

full rationale

This is an engineering report rather than a derivation, so most circularity patterns do not apply. The central quantitative result, the 84.09% S-BERT similarity score for prompt_0a, is presented as the outcome of comparing nine prompts on a 50-query test set, not as a prediction of future performance. Selecting the best prompt on the same set is a model-selection procedure, and the paper does not call the chosen score a held-out estimate. The pharmacist reference answers were produced using the same corpus available to Drug Insights, which makes the S-BERT metric a consistency check rather than an independent clinical-accuracy measurement; this is a validity limitation of the evaluation, not a circular derivation, because the score is not defined in terms of the system's own outputs or fitted to guarantee the claimed accuracy. The paper also explicitly acknowledges that retrieval precision and recall were not systematically evaluated, further confirming that no fitted retrieval parameter is being renamed as a prediction. References to Advantage Health Africa and EMDEX are contextual or evaluative, not load-bearing self-citations that force the paper's conclusions. Overall, the accuracy and minimal-hallucination claims are under-supported by the evidence, but no step in the paper reduces by construction to its own inputs.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The system has no new theoretical entities. The central claim rests on the choice of the EMDEX corpus as authoritative, on S-BERT similarity as a quality measure, and on hand-set retrieval hyperparameters (threshold 0.9, top-k 3). The best prompt is effectively a free parameter selected on the test set.

free parameters (3)

cosine similarity threshold = 0.9
Hand-chosen for retrieval in Section II.C; no justification or sensitivity analysis is given.
similarity count (top-k) = 3
Hand-chosen for retrieval in Section II.C; no justification or sensitivity analysis is given.
prompt_0a selection = best prompt
Selected based on highest S-BERT score on the same 50 test queries that produced the reported 84.09% figure; this is a hyperparameter fitted to the evaluation data.

assumptions (3)

domain assumption EMDEX formulary is an accurate and comprehensive reference for Nigerian medications
The corpus is treated as authoritative without independent validation; Section II.A.
domain assumption Pharmacist responses using the same corpus are a valid gold standard for correct answers
The evaluation compares against pharmacist answers that are constrained to the same corpus, so the gold standard is not independent; Section III.B.
domain assumption S-BERT cosine similarity correlates with clinical answer quality
The paper uses S-BERT similarity as the primary quality metric without evidence that semantic similarity to a pharmacist answer implies clinical accuracy; Section III.B.

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Cite this review

Pith. "Pith review of Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers." pith.science (2026). https://pith.science/paper/54MDUSPX

@misc{pith2026250215722,
  author       = {Pith},
  title        = {Pith review of: Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/54MDUSPX}},
  note         = {Machine review of arXiv:2502.15722}
}

read the original abstract

Accessing accurate medication insights is vital for enhancing patient safety, minimizing errors, and supporting clinical decision-making. However, healthcare professionals in Africa often rely on manual and time-consuming processes to retrieve drug information, exacerbated by limited access to pharmacists due to brain drain and healthcare disparities. This paper presents "Drug Insights," an open-source Retrieval-Augmented Generation (RAG) chatbot designed to streamline medication lookup for healthcare workers in Africa. By leveraging a corpus of Nigerian pharmaceutical data and advanced AI technologies, including Pinecone databases and GPT models, the system delivers accurate, context-specific responses with minimal hallucination. The chatbot integrates prompt engineering and S-BERT evaluation to optimize retrieval and response generation. Preliminary tests, including pharmacist feedback, affirm the tool's potential to improve drug information access while highlighting areas for enhancement, such as UI/UX refinement and extended corpus integration.

Figures

Figures reproduced from arXiv: 2502.15722 by the authors.

**Figure 1.** Architecture of Drug Insights 2 III. PROMPT ENGINEERING AND OUTPUT EVALUATION A. Prompt Engineering Prompt Artisan [11], a widely used custom GPT for cocreation of prompts using best practices, was used in the creation of the default QA system and context prompts suitable for a drug interaction assistant. These prompts are zero-shot style prompts. Using reflections on contemporary prompt engineering techniques such… view at source ↗

**Figure 2.** Average S-BERT similarity score (in %) between Drug insights responses with different prompts and human pharmacists. From [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Utilization of drug information resources among community pharmacists in Jordan: A cross-sectional study

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