Evidence map›Paper›PMID 42763555›Full record

ArticleCureus2026

Beyond Traditional Inventory Matrices: A Multidimensional Inventory and Safety Model for an Outpatient Pharmacy.

Nishant Sharma, Kshitija Singh, Nirupam Madaan, Kanika Jain

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nishant SharmaHospital Administration, All India Institute of Medical Sciences, New Delhi, IND.
Kshitija SinghHospital Administration, All India Institute of Medical Sciences, New Delhi, IND.
Nirupam MadaanHospital Administration, All India Institute of Medical Sciences, New Delhi, IND.
Kanika JainHospital Administration, All India Institute of Medical Sciences, New Delhi, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEffective outpatient pharmacy inventory management in tertiary care hospitals requires balancing rigorous cost containment with absolute clinical safety over multi-year operational cycles. Traditional ABC (Always, Better, Control) and VED (Vital, Essential, Desirable) matrices focus heavily on financial and basic clinical parameters but often overlook direct patient safety indicators and longitudinal consumption trends.

objectiveThis study aimed to evaluate outpatient pharmacy inventory patterns across three consecutive fiscal years (FY 2023-24 to FY 2025-26) using ABC and VED analyses, assess longitudinal changes in expenditure and clinical criticality, and integrate High-Alert Medication and Look-Alike, Sound-Alike assessments to identify medication-safety risks and opportunities for inventory optimization. METHODOLOGY: A retrospective longitudinal inventory analysis spanning three consecutive fiscal years (FY 2023-24, FY 2024-25, and FY 2025-26) was conducted using annual consumption and expenditure data from the outpatient pharmacy at a premier tertiary care teaching hospital in New Delhi. Total unique medication lines analyzed ranged from 6,207 to 6,468 items per year, representing a cumulative multi-billion-rupee expenditure. Each annual dataset was subjected to ABC Pareto analysis and VED classification mapped against the National List of Essential Medicines (NLEM 2022). Algorithmic string matching and established safety guidelines were utilized to isolate high-alert medication (HAM) and look-alike/sound-alike (LASA) vulnerabilities longitudinally.

resultsTotal annual pharmacy expenditure grew from ₹3.80 billion in FY 2023-24 to ₹4.57 billion in FY 2024-25, and ₹5.54 billion in FY 2025-26. Across all three years, Class A items demonstrated a stable Pareto expenditure distribution while shrinking in item volume from 276 (4.45%) down to 179 (2.77%). VED analysis revealed an escalating financial concentration in non-essential ambulatory care, with

conclusionsLongitudinal analysis demonstrates that relying solely on single-year ABC-VED matrices is insufficient for modern hospital administration. Integrating HAM and LASA metrics across multi-year cycles enables proactive cost-containment in expanding non-essential outpatient categories while upholding rigorous national accreditation standards for medication management and safety.

Indexed as

abc-ved analysishospital accreditation standardsmedication management and safetymulti-dimensional inventory modeloutpatient pharmacy inventory management

Identifiers

PMID42763555
PMCPMC13589173

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.